Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
C378etukansi.kesken.fm Page 1 Tuesday, December 21, 2010 3:43 PM C 378 OULU 2011 U N I V E R S I T Y O F O U L U P. O. B . 7 5 0 0 F I - 9 0 0 1 4 U N I V E R S I T Y O F O U L U F I N L A N D U N I V E R S I TAT I S S E R I E S SCIENTIAE RERUM NATURALIUM Professor Mikko Siponen HUMANIORA University Lecturer Elise Kärkkäinen TECHNICA Professor Hannu Heusala ACTA UN NIIVVEERRSSIITTAT ATIISS O OU ULLU UEEN NSSIISS U Dayou Yang E D I T O R S Dayou Yang A B C D E F G O U L U E N S I S ACTA A C TA C 378 OPTIMISATION OF PRODUCT CHANGE PROCESS AND DEMAND-SUPPLY CHAIN IN HIGH TECH ENVIRONMENT MEDICA Professor Olli Vuolteenaho SCIENTIAE RERUM SOCIALIUM Senior Researcher Eila Estola SCRIPTA ACADEMICA Information officer Tiina Pistokoski OECONOMICA University Lecturer Seppo Eriksson EDITOR IN CHIEF Professor Olli Vuolteenaho PUBLICATIONS EDITOR Publications Editor Kirsti Nurkkala ISBN 978-951-42-9354-2 (Paperback) ISBN 978-951-42-9355-9 (PDF) ISSN 0355-3213 (Print) ISSN 1796-2226 (Online) UNIVERSITY OF OULU, DEPARTMENT OF MECHANICAL ENGINEERING; DEPARTMENT OF INDUSTRIAL ENGINEERING AND MANAGEMENT C TECHNICA TECHNICA ACTA UNIVERSITATIS OULUENSIS C Te c h n i c a 3 7 8 DAYOU YANG OPTIMISATION OF PRODUCT CHANGE PROCESS AND DEMANDSUPPLY CHAIN IN HIGH TECH ENVIRONMENT Academic dissertation to be presented, with the assent of the Faculty of Technology of the University of Oulu, for public defence in Auditorium IT115, Linnanmaa, on 28 January 2011, at 12 noon U N I VE R S I T Y O F O U L U , O U L U 2 0 1 1 Copyright © 2011 Acta Univ. Oul. C 378, 2011 Supervised by Professor Kauko Lappalainen Professor Harri Haapasalo Reviewed by Professor Petri Helo Doctor Lasse Pesonen ISBN 978-951-42-9354-2 (Paperback) ISBN 978-951-42-9355-9 (PDF) http://herkules.oulu.fi/isbn9789514293559/ ISSN 0355-3213 (Printed) ISSN 1796-2226 (Online) http://herkules.oulu.fi/issn03553213/ Cover Design Raimo Ahonen JUVENES PRINT TAMPERE 2011 Yang, Dayou, Optimisation of product change process and demand-supply chain in high tech environment University of Oulu, Faculty of Technology, Department of Mechanical Engineering, P.O.Box 4200, FI-90014 University of Oulu, Finland; University of Oulu, Faculty of Technology, Department of Industrial Engineering and Management, P.O.Box 4610, FI-90014 University of Oulu, Finland Acta Univ. Oul. C 378, 2011 Oulu, Finland Abstract Information and communications technology (ICT) companies face challenges in an unpredictable business environment, where demand-supply forecasting is not accurate enough. How to optimally manage product change process and demand-supply chain in this type of environment? Companies face pressures to simultaneously be efficient, responsive and innovative, i.e. to minimise costs, and shorten order delivery and product change periods. This thesis included three action research cycles within a real demand-supply chain of a significant international actor. Each action research cycle sought answers by going into one extreme of minimising costs, diminishing order delivery period, or shortening product change periods. In practice, these research cycles included the case company changing their business accordingly for each of these cases. Conducting required changes in the case company were economically significant trials. The results of this doctoral dissertation provide tips for global high tech companies. Large international companies typically have manufacturing sites in different parts of the world. According to the results, mental shift from local optimisation to a global one is required for efficient manufacturing operations. Companies have traditionally considered their strategy as a choice between minimising costs, quick delivery, and rapid product change. Also, companies have believed that one single strategy is adequate and applicable to all of their products. According to this thesis, different products may have a different strategy. This would allow companies to flexibly react to the needs of different customer groups, business environments, and different competitors. In addition, strategy can be changed relatively often, monthly, weekly, or even daily. Based on the results of this doctoral thesis, companies must harmonise their product portfolio globally, including all their sites. Once the same product version is at all sites, they can help each other from components supply viewpoint. Consequently, product changes can be taken through quicker. Keywords: action research, agile, demand supply, innovativeness, lean, optimisation, synchronization Yang, Dayou, Tuotemuutosprosessin optimointi ja kysyntä-tarjontaketju korkean teknologian yrityksissä Oulun yliopisto, Teknillinen tiedekunta, Konetekniikan osasto, PL 4200, 90014 Oulun yliopisto; Oulun yliopisto, Teknillinen tiedekunta, Tuotantotalouden osasto, PL 4610, 90014 Oulun yliopisto Acta Univ. Oul. C 378, 2011 Oulu Tiivistelmä Informaatio- ja kommunikaatioalan yritykset kohtaavat haasteita toimiessaan vaikeasti ennustettavassa liiketoimintaympäristössä, jossa tilaus-toimitusennusteet ovat epätarkkoja. Miten tällaisessa ympäristössä hallitaan optimaalisesti tuotemuutosprosessi ja tilaus-toimitusketju? Yrityksillä on paineita olla samanaikaisesti tehokkaita ja innovatiivisia: miten minimoida sekä kustannuksia että lyhentää toimitus- ja tuotemuutosaikoja. Tämä väitöskirja tehtiin toimintatutkimuksena merkittävän kansainvälisen yrityksen todellisessa tilaus-toimitusketjussa. Toimintatutkimus eteni vaiheittain kokeilemalla kolmea eri ääripäätä minimoimalla 1) kustannuksia, 2) toimitusaikoja ja 3) tuotemuutosaikoja. Käytännössä nämä ääripäät sisälsivät case-yrityksen liiketoiminnan muuttamista vastaavasti sisältäen taloudellisesti merkittäviä kokeiluja. Tämän väitöskirjan tulokset tarjoavat käytännön esimerkkejä globaaleille korkeanteknologian yrityksille. Suurilla kansainvälisillä yrityksillä on tyypillisesti valmistusyksiköitä eripuolilla maailmaa. Tämän tutkimuksen tulosten mukaan yritykset tarvitsevat asennemuutoksen paikallisesta optimoinnista globaaliin, jotta tuotanto toimisi tehokkaasti. Perinteisesti yritykset ovat ymmärtäneet strategian tarkoittavan valinnan tekemistä kustannusten minimoinnin, nopeiden toimitusaikojen tai nopeiden tuotemuutosten välillä. Yritykset ovat myös uskoneet, että yksi yrityskohtainen strategia kattaa kaikki yrityksen tuotteet. Tämän väitöskirjan tulosten mukaan yrityksen eri tuotteilla voi olla erilainen strategia. Tällainen ratkaisu mahdollistaa nopean reagoinnin muutoksiin asiakasryhmien tarpeissa, liiketoimintaympäristössä ja kilpailutilanteissa. Strategiaa voidaan myös muuttaa usein, kuukausittain, viikoittain tai jopa päivittäin. Tämän väitöskirjatutkimuksen tulosten mukaan, yritysten tulisi harmonisoida tuoteportfolionsa globaalisti kattaen kaikki tuotantolaitokset. Silloin kun yrityksen kaikissa valmistusyksiköissä valmistetaan samaa tuoteversiota, yksiköt voivat auttaa toisiaan komponenttien hankinnassa. Tuotemuutokset voidaan tällöin toteuttaa nopeammin. Asiasanat: innovatiivisuus, ketteryys, kysyntä, optimointi, synkronointi, tarjonta, toimintatutkimus Acknowledgements This dissertation is about a research initiated in a tough situation of high-tech manufacturing back in 2002. It was economic hard time with lean strategy as a must in the company where the researcher was employed. However, the company had to struggle with inaccurate forecasts in their daily work making product change management more challenging. Earlier planning, or even comprehensive knowledge over JIT (Just-In-Time), was not enough due to big lead-time gaps in demand-supply. Thus this learning journey was initiated to develop new solutions. The research was conducted cycle-by-cycle, and the outcomes were gradually implemented to IT over the years. During this process, many people provided their valuable assistance. I am very grateful to my supervising professors - Harri Haapasalo and Kauko Lappalainen for their professional guidance through the whole research process. Their strong commitment always inspired me to overcome any difficulties. Constructive advice from Dr. Janne Härkönen, Dr. Pekka Belt and Dr. Matti Möttönen of the University of Oulu were especially helpful. They helped to broaden my way of thinking about my research and the dissertation and helped me to see things from multiple viewpoints. Also I wish to thank Professor JuhaMatti Lehtonen being so supportive and patient when I was struggling while aiming to a breakthrough. Deep in my heart, great thanks belong to Mr. Ari Kurikka who has remarkably coached me from the very beginning until all the action research cycles were finished. The insight of focusing on the whole demand-supply network kept the research aiming for a win-win solution to all network parties. Special acknowledgement goes to Mr. Arto Tolonen for many of his valuable advices. Especially with his “Design for Excellence” contribution implemented in the company, it made it easier for this research to operate with less product variants. I want to present my sincere thanks to Mr. Jukka Kukkonen, Mr. Ville Jokelainen, Mr. Kaj Sundberg and Mr. Jussi Parviainen for supporting me when conducting this research besides my daily work. I very much appreciate the help and interest of my other colleagues for their insightful inputs. My warm thanks belong to AAC Global Oyj and other native English-speaking friends for their language assistance. I also need to acknowledge the financial aid from Finnish Foundation for Economic Education. In addition, I would like to thank the pre-examiners of this study - Professor Petri Helo and Dr. Lasse Pesonen for their valuable comments and recommendations. 7 Finally, my deepest gratitude belongs to my wife Weilin and my children Yuchen & Tina. I value their support and care to tolerate my mental absence due to this work. Their patience makes my learning journey possible and rewardable. Oulu, December 2010 8 Dayou Yang Abbreviations and key terminology 3C 3D CE ABM AR ATO BAM BOM BPR BTO CAD CIB CIM CLM CLCP CM CMMI CPM CRB CRM DA DNA ECN ECR EMS ESP ERP EVDB FAT FMS GIT i2 ICH IQ IT JIT Capacity, Commonality, Consumption (management system) Three Dimensional Concurrent Engineering Agent-Based Manufacturing Action Research Assemble-To-Order Business Activity Monitoring Bill Of Material Business Process Re-engineering Build-To-Order Computer Aided Design Change Implementation Board Computer Integrated Manufacturing Council of Logistics Management Closed Loop Change Process Configuration Management Capability Maturity Model Integration Corporate Performance Management Change Review Board Customer Relationship Management Delivery Accuracy Deoxyribonucleic Acid Enterprise Change Notice Enterprise Change Request Electronics Manufacturing Services Equalised and Synchronised Production Enterprise Resource Planning Events and Venues Database Focus, Architecture, and Technology Flexible Manufacturing System Goods In Transit A management application supplier Inventory Collaboration Hub Intelligence Quotient Information Technology Just-In-Time 9 MAS MICE MRP MTO MTS NMS NPI OEM OPP OPT OSS PASSI PC PMBOK PS PTK PTO PWB R&D RIA RDBMS ROI SAP SCM SCOR SOA STO TOC TPI TQM TTC TTM UML VMI VOP WIP 10 Multi-Agent System Multimedia, information, communications, and electronics Material Requirements Planning Make-To-Order Make-To-Stock Network Managed Supply New Product Introduction Original Equipment Manufacturer Order Penetration Point Optimised Production Technology Operation and Support Subsystem Process for Agent Societies Specification and Implementation Personal Computer Project Management Body of Knowledge Physical Stock PASSI Tool Kit Pack-To-Order Printed Wiring Board Research and Development Rich Internet Application Relational Database Management System Return on Investment A management application supplier Supply Chain Management Supply-Chain Operations Reference Service Oriented Architecture Ship-To-Order Theory of Constraints Trading Partner Integration Total Quality Management Time to Customer Time to Market Unified Modelling Language Vender Managed Inventory Value Offering Point Work In Process Please note that following list describes the terminology for the purpose of this dissertation rather than giving official definitions. Minimise costs (Lean) = Creating value with as little work and waste as possible. Quick delivery (Agility) = Responsiveness in demand fulfilment Fast product change (Innovativeness) = Making product changes as quick as possible Zero-series = series after proto type in product development, before actual volume production Component equalisation = In a large organisation there are different persons responsible for buying different components, causing differences in the levels of different components as buyers buy in different pace and their activities are not adequately coordinated. In a situation with too many components, the component you have least determines the equalised level. If you have any components more than the equalised level, those can be considered as waste. The difference between the equalised level and the original forecasted level can be considered as tolerance margin increasing agility. However, if the company prefers lean over agility, this type of tolerance should be avoided. Time based optimisation (Synchronisation) = In modern business, when new product versions are introduced, there are a large number of tasks that must be conducted. As time has become increasingly important aspect for business success, time-based coordination of activities is important for total optimisation. In this dissertation this coordination is also called synchronisation. Also, the handling of component supply change, including component equalisation on time basis, must be included in this synchronisation. Liability = Company has contractual obligations for a certain period of a forecast before they can stop buying certain components from a supplier. From a supplier’s viewpoint, this gives a level of security for a certain period of time, such as two months, allowing it to cut costs and adjust to changes. This liability only applies to buyer company specific components. 11 Dynamic cut-off window = Buyer company has a natural goal of minimising the liability of the amount of components it is obliged to buy. In order to optimise the operations of buyer-seller cooperation, the information on critical issues must be transferred as early as possible, for instance updating forecasts on a weekly basis. This way of dynamically informing a supplier allows it to have time to react accordingly. This in turn makes it possible to reduce the liability of the buyer. Fixed cut-off window = Before starting a zero-series, product new version changeover date is selected and fixed. This type of fixed cut-off window enables suppliers to deliver the existing order plus liability. No further orders are placed for the old material. 12 Contents Abstract Tiivistelmä Acknowledgements 7 Abbreviations and key terminology 9 Contents 13 Introduction 15 1.1 Research background & motivation ........................................................ 15 1.2 Objectives and scope ............................................................................... 18 1.3 Research process ..................................................................................... 19 1.3.1 Action research ............................................................................. 19 1.3.2 Research context........................................................................... 20 1.3.3 Practical realisation ...................................................................... 22 1.4 Structure of the thesis .............................................................................. 23 2 Literature review 25 2.1 Manufacturing philosophies .................................................................... 25 2.1.1 Lean manufacturing and JIT philosophy ...................................... 25 2.1.2 ESP concept beyond JIT philosophy ............................................ 26 2.1.3 Agile manufacturing and leagility concepts ................................. 27 2.1.4 Manufacturing strategies and product life cycle ........................... 28 2.1.5 The innovator’s strategy ............................................................... 29 2.1.6 Summary of manufacturing philosophies ..................................... 30 2.2 Developing demand-supply network ...................................................... 32 2.2.1 Value oriented development for demand-supply network ............ 32 2.2.2 Manufacturing strategies affect demand-supply network ............. 36 2.2.3 The role of collaboration in demand-supply ................................. 40 2.2.4 Measuring demand-supply performance ...................................... 44 2.2.5 Purchasing automation challenge in product life cycle ................ 46 2.2.6 Optimisation of demand-supply with thinking of BI automation .................................................................................... 48 2.3 Product change management................................................................... 52 2.4 Special characteristics of high-tech industries ........................................ 54 2.4.1 Challenges in forecasting ............................................................. 54 2.4.2 Telecom supply chain of case company ....................................... 55 2.4.3 Case Ericsson (analysed in 2002–2003) ....................................... 56 13 2.4.4 Case Dell Corporation/Lucent Technologies (analysed in 2002–2003) ................................................................................... 58 2.4.5 Case Huawei Technologies (the new competition reality)............ 60 2.4.6 Other studies oriented by value differentiation or unique advantage ...................................................................................... 61 2.5 Theory synthesis...................................................................................... 69 3 Results of the three action research cycles 73 3.1 Research Cycle 1 – minimising costs ...................................................... 75 3.1.1 Pre-Step ........................................................................................ 76 3.1.2 Diagnosis ...................................................................................... 77 3.1.3 Planning ........................................................................................ 77 3.1.4 Taking action ................................................................................ 80 3.1.5 Evaluation ..................................................................................... 81 3.2 Research Cycle 2 - shortening order delivery time ................................. 84 3.2.1 Pre-Step ........................................................................................ 84 3.2.2 Diagnosis ...................................................................................... 85 3.2.3 Planning ........................................................................................ 86 3.2.4 Taking action ................................................................................ 88 3.2.5 Evaluation ..................................................................................... 89 3.3 Research Cycle 3 - shortening product change time ............................... 91 3.3.1 Pre-Step ........................................................................................ 93 3.3.2 Diagnosis ...................................................................................... 94 3.3.3 Planning ........................................................................................ 94 3.3.4 Taking action ................................................................................ 95 3.3.5 Evaluation ..................................................................................... 96 4 Discussion 99 4.1 Answering research questions ................................................................. 99 4.1.1 Research question 1 ...................................................................... 99 4.1.2 Research question 2 .................................................................... 100 4.1.3 Research question 3 .................................................................... 102 4.2 Managerial implications ........................................................................ 103 4.3 Scientific implications ........................................................................... 105 4.4 Reliability and validity .......................................................................... 107 4.5 Research contribution & discussion ...................................................... 110 4.6 Future research ...................................................................................... 112 5 Summary 115 References 117 14 Introduction 1.1 Research background & motivation Industrial globalisation has greatly changed high-tech companies while they have created significant operations in multiple countries. Because poor visibility and massive uncertainty are part of the operational nature, new challenges arise continuously for companies who want to internationalise their demand-supply network. The struggle to survive has become an integral part of each giant company’s way of life (Hill, 2000). As the operations become more dynamic (Wazed et al. 2009), the problems of the famous JIT (Just-In-Time) concept (Voss, 1987) are increasingly reported with the facts, even in Japan: zero-inventory management is just a fiction (Hann et al., 1999), and JIT is not necessarily useful for part suppliers (Naruse, 2003). Even Toyota Motor Corporation as a model of operational efficiency within the auto industry, it also got its first annual operating loss in 2009 after 70 years of enjoying healthy profits. Not as a symbol of operational excellence, Toyota recall crisis of 2010 has prompted much criticism in media circles, national business forums and automotive trade publications (Piotrowski and Guyette 2010). Consequently, it is now time for new thinking. For example, it needs to go against the mainstream and take current strategy to a more extreme version of itself, before scaling back just a little bit (Schmitt 2007). The research was initiated in 2002 during last economic downtime by solution-finding for product change management in a famous international company, the case company of this research, who operates as one the world’s largest telecommunications infrastructure suppliers, and which continuously suffers from inaccurate forecasting and dynamic demand in its innovative manufacturing. As the nature of mobile infrastructure industry (Collin et al., 2005; Heikkilä 2002), the system vendors have to be able to quickly respond to shortterm changes in demand. On the one hand, they are forced to have an in-built ability to constantly adapt their supply chains to rapid and unexpected changes in the markets or technologies (Raisinghani et al. 2002; Webster 2002). On the other hand, the vendors are also expected to be fast and flexible while delivering customised products and services with a high standard of delivery accuracy (Alfnes and Strandhagen 2000; Småros et al. 2003; Knowles et al. 2005). In the case company, the old way of doing things was to make a perfect production plan based on a perfect forecast, at some point this did not work 15 anymore. In reality there was always some components missing, and production stopped. As a consequence, scrapping costs became very high. There were different product versions in different sites, with up to one year’s difference resulting in sites being unable to help each other. In addition, more R&D people were required to support the supply-chain and product changes became very slow, almost out of control. Figure 1 presents an example of the problem situation, relating to demand fulfilment, during a one-year period in the case company. It shows how the forecasts of one or two months were so different from the true demand fulfilled. The example records a hopeless situation, in which such uncertainties make product innovation through engineering changes as well as normal delivery of customer order fulfilment extremely problematic. In other words, and to state the problem for academic purpose, the intangible information flow in demand-supply network cannot ensure physical product flow just-in-time at each step of the manufacturing operation. Due to the bullwhip effect (Lee et al., 1997; Lee, 2002) in material forecast and product delivery, it is even more frustrating when utilising traditional purchase orders or long distance transportation. The tough choice of a trade-off (such as inventory increase, change slow-down, delivery delay, lost sales) has to be made due to such lead-time gaps in global operation (Shahbazpour and Seidel 2006; Bozarth et al. 2009). It can be even worse when product changes are included as extra uncertainties in this unsynchronised status (Salmi and Holmström 2004). Fig. 1. Challenge with monthly forecast and true demand. 16 In innovative businesses, the changes occur for most of a product’s life with great impact to whole demand-supply network (Aitken et al. 2003; Dreyer et al. 2007). It is unique to utilise the details about cases of product change management constantly in the research of manufacturing operation, which was not seen in previous attempts by others. It can include more factors than those studies only dealing with product development (Knight, 2003; Guess, 2002) and demandsupply operation (Bengtsson, 2002; Christopher and Peck 2004) alone, or mainly at a conceptual and simulation-oriented level (Subramoniam et al. 2008; Falasca and Zobel 2008; Koh and Gunasekaran 2006; Zhou 2006; Kemppainen and Vepsäläinen 2004; Saab and Correa 2004). Under a complex business environment as in Figure 2, the research was based on a simple clue from product change implementation. It is then expected to equalise the amount of all material in the whole supply operation at anytime and anywhere. Fig. 2. Business operational environment of the research. 17 In the case company, there were simultaneous pressures to minimise costs, shorten the product change period and quicken order delivery processes. In addition, the case company had an aim to minimise scrapping costs in all situations. 1.2 Objectives and scope The research problem arises from the case company’s challenges in an unpredictable business environment, where demand-supply forecasting is not accurate enough. How to optimally manage product change process and demandsupply chain in this type of environment? Companies phase pressures to simultaneously be efficient, responsive and innovative, i.e. to minimise costs, and shorten order delivery and product change periods. The research problem of this dissertation is formulated: How should companies optimise the product change process strategy in a situation where there are simultaneous and variable pressures to be lean, agile and innovative. This research problem is addressed by focusing on product change process and demand-supply chain optimisation of large global ICT companies operating in business-to-business environment. First, literature was reviewed to gain understanding on lean philosophy, agility, and innovativeness and consequently to find potential solutions for the research problem. In order to obtain information for deeper analyses and conclusions, the following research question were formulated. RQ1 What are the effects for the product change process when costs are minimised (Cycle 1)? RQ2 What are the effects for the product change process when order delivery period is minimised (Cycle 2)? RQ3 What are the effects for the product change process when product change time is minimised (Cycle 3)? Action research method was utilised in the case company to find answers to these above mentioned research questions. Each action research cycle, representing a separate trial, seeks answers for one research question by going into one extreme 18 of minimising costs, diminishing order delivery time, or shortening product change periods. 1.3 Research process The aim of this study was to conduct practical analyses on the effects of changes in essential parameters, namely inventory level, order delivery period, and product change time. The effects were studied for a real demand-supply chain of a significant international actor. Secondly, based on these analyses, this study attempted to find new means of dealing with complex issues in the described environment. 1.3.1 Action research According to O’Brien (1998) action research can be used in practical situations where the primary focus is on solving real problems. In addition, the researcher was employed by a company to whom the studied aspects were of great importance. Action research was chosen as a research method as it enables combining research and ordinary business work within the studied organisation. Action research is concerned with the resolution of organisational issues, such as the implications of change together with those who experience the issues directly. In action research the practitioners are involved in the research, and there is a collaborative partnership between practitioners and researchers. In simple terms, the researcher is a part of the research subject. Often action research is an iterative process, often depicted as a spiral, of diagnosing, planning, taking actions and evaluating. (Saunders et al. 2007). Action Research is the process of systematically collecting research data about an ongoing system relative to some objective, goal, or need of that system; feeding these data back into the system; taking actions by altering selected variables within the system based on the data and on the hypothesis; and evaluating the results of actions by collecting more data (French et al., 1973). Action research enables simultaneous utilisation of different research methods and techniques (O’Brien 1998). According to Coughlan (2002) action research requires that the researcher enters the culture, understands the common values, and uses its language. This research method was chosen, even though action research does not meet the verification criteria of positivitism, meaning objective study as in natural sciences (Susman and Evered, 1978; Saunders 2007). 19 1.3.2 Research context Selected case company is a significant global actor in the ICT system business. The researcher was employed by the company, thus having a good access and the research was related to his everyday work. The global demand-supply chain of the case company is studied in this thesis from the perspective of product change process. The research can be described to simultaneously include aspects of worldwide business impact, rapid innovative pace, and high volume in operation. There are many engineering changes during a product’s lifetime without a period when new and old versions overlap as execution principle. Component changes in products often happen at any time adding extra complexity for manufacturing besides original demand uncertainty. Product versions were different more than one year at some manufacturing sites before the research was launched. The component logistics, as in the electronics industry in general, is extremely complex due to a vast number of required components with long production or delivery lead-times. For example, the lead-times may differ by days, weeks (such as PWB and own specific integrated circuits), or even months due to sea transportation (such as the cabinet). This causes bottlenecks or big inventories in the supply network due to those time variances and real demand often not matching with earlier forecasts. The case company had to combine push-based supply chain and pull-based demand chain together as a mix to synchronise production and delivery of all product parts with big lead-time gaps. Pull principle was applied at internal steps of the production, as well as the delivery end. Push principle had to apply for the supply end and keep the inventories to absorb the impact of inaccurate forecast. Demand-supply network had to thus have enough tolerance to avoid undesirable conditions, such as production stop due to lack of key components. Below list describes the challenges faced by the case company: 1. 20 Both strategies of lean or agile thinking were not good enough as there were some obvious drawbacks. For example, production lead time was at a level of counting hours or days, which was not a critical step if comparing to months or weeks for material supply. The wish of zero inventory or fast response is hard to achieve constantly in dynamic demand situation. With whole demandsupply network in consideration, not just the case company itself, lead time gaps could not be solved by lean or agile principles alone. It was the 2. 3. playground reality when product changes were to be also added into the complexity. Production can be described as a multiproduct / multistage stochastic pull system (Askin and Krishnan 2009). Pull principle was applied from product delivery till production start, in order to balance the pace and the flow of manufacturing operations. When the gap of material supply occurred, such a balance would be destroyed in a fire-fighting manner to take time for its recovery. As an example, principles of popular theories were all checked but with the product flow in FIFO (First-In-First-Out) mode at each step of manufacturing, meant that not a same product was initiated, moved and delivered in the operation to fulfil the demand at customer end. Observing in various ways, the effects of different theories could be seen “virtually”, e.g. MTO (Make-To-Order), ATO (Assemble-To-Order), DTO (Deliver-To-Order), and even MTS (Make-To-Stock). The main difficulty related to material supply and its liability for key components due to long lead times. It could not be avoided and was a reality for the case company if lead times were not possible to be shortened. For example, new and old material in product change should be controlled well in such a synchronisation. Especially, old components with lead time as weeks could cause the liability as the amount for months to consume. Otherwise, it could result in enormous scraping costs. It was the limitation to product change and normal operations lean effect in mind. The liability was invisible in MRP systems because of inaccurate forecast in the past, which was seldom to be studied to reduce its effect. The bottom-line was to deliver products to customers’ requirements (especially having the changes of delivery amount or product configuration) at a high speed, without means to develop efficient forecasting processes to manage demand uncertainty. Whenever the volume of pull at delivery side was larger than the amount of push at supply side, production had to be stopped due to missing components. The case company had to find an alternative way to survive better in the competition as everyone in the industry suffered by those same challenges. In addition, multiple tiers of many companies were involved in the demandsupply chain with international manufacturing operation. Faster transfer of demand information or a more reactive planning was not enough to save manufacturing companies as a physical process is inflexible in responding to frequent plan changes in normal operation. When product changes added on this, 21 demand-supply planning practices became even more fragmented and frustrated. There were no existing solutions available, academic or industrial, at the time. 1.3.3 Practical realisation The research was mainly realised during the period of 2003–2006. The research included three action research cycles. Each action research cycle sought answers by going into one extreme of minimising costs, diminishing order delivery period, or shortening product change periods. In practice, these research cycles included the case company changing their business accordingly for each of these cases. Conducting required changes in the case company were economically significant trials. Figure 3 describes the research process. Fig. 3. The research process. Research Cycle 1 included the case company aiming all of its actions to minimising costs. The case company executed a strategy of cost effectiveness. Minimising inventory and scrapping costs required swift component control in the whole demand-supply chain. In research Cycle 2 the case company aimed at diminishing order delivery period. In this trial, the case company aimed at strong concurrency in engineering to get order delivery period as short as possible. Research Cycle 3 concentrated on shortening product change period. The case company executed a strategy of innovativeness making product changes as fast as possible. The trial clarified whether a ready-product inventory could be used to speed up product change. During research cycles, every change case was recorded using change notes (CN). Change notes compare the old and the new product versions, indicating all changes in used components. CN also indicated the expectation when the changes 22 will be conducted. CN was common for all sites enabling to tell which site is influenced. Site specific implementation reports were utilised to record changes, the implementation time and scrapping costs. Implementation report described all the results from different sites. Both, implementation reports and change notes were stored into a database. There were over one hundred product change cases available within the company at the time of research. The researcher selected three cases out of all product changes, one for each cycle. The cases were important for business and there was a significant change in the product. Process improvements were made based on the three selected product change cases individually. After the process improvements, it was checked whether the targets set for that particular cycle was reached or not. The researcher worked as the project manager for all the studied product change cases. He was responsible for product change implementations, including planning & informing all the sites, and cooperation between these sites, collecting results, analysing and making conclusions. 1.4 Structure of the thesis Chapter 1 describes the background information of this research straightforwardly by using a true problem from industrial practices. The goal is to survive better than others in the industry under inaccurate forecast. Because modern manufacturing in global scale is more sophisticated than ever, it is essential to define the scope and the limitation of this research precisely. It is aiming to be beyond lean or agile manufacturing, as well as any improved versions currently in use. The research approach is selected briefly from reviewing different methodologies in order to obtain the advantages of the action research method. This method enables developing modular solutions piece by piece in an innovative way. In Chapter 2, the literature review is conducted to collect applicable elements from existing management science for further development. They are mainly from the fields of manufacturing philosophies, operational performance of demand-supply, product change management, and industrial case study. The empirical research is stated in Chapter 3, and the results accomplished in 3 cycles of action research are presented. The key thoughts of each research cycle are verified in order to ensure the research questions studied by sufficient details. 23 In Chapter 4, research questions are answered to summarise the thoughts on flexible optimisation rather than choosing only one option and being stuck in the middle. The key is applying multi-strategies in business environment as a multidimensional playground. The validation and reliability of the research are checked. The implications of research with its constructive contributions are discussed for practical and academic evaluation. After summarising new contributions of the research, the recommendations for future development are also presented in order to continue the learning journey further for great success. 24 2 Literature review 2.1 Manufacturing philosophies Different manufacturing philosophies include, lean thinking, JIT (Just-In-Time), agile manufacturing, and their derivates. 2.1.1 Lean manufacturing and JIT philosophy Lean manufacturing, as practiced in the Toyota production system, was a revolutionary change of just-in-time (JIT) philosophy to mass production practices in the automotive industry (Haan et al., 1999). The conceptual model can be like a continuously moving conveyor belt from the beginning of production to the delivery of finished products. It aimed to provide cost-effective production as its delivery of only the necessary quantity of parts at the right quality, at the right time and place, while using a minimum amount of facilities, equipment, materials and human resources. A time line from 1930 to 2006 about its development within Toyota to form an overview of JIT can be found in Holweg (2006). However, the problems have been widely reported more and more as the disadvantages of JIT in the dynamic business of global manufacturing nowadays: – – – Limited to repetitive manufacturing Requires stable production level Does not allow much flexibility in the products produced Seeking for the improvements, one example is the most efficient type of JIT operation – Synchronous Manufacturing (Umble et al., 1996; Srikanth et al., 1997; Doran, 2002), which can be a direction towards new JIT to solve the above drawbacks. Synchronous manufacturing embodies many concepts related to focusing and synchronising production control around bottleneck resources (Frazier et al., 2000). Other common names for these concepts are the theory of constraints (or simply TOC) and Drum-Buffer-Rope, which was introduced in 1984 by Eliyahu Goldratt in The Goal (Walker, 2002). The Theory of Constraints (TOC) is an overall management philosophy that aims to continually achieve more of the goal of a system. The key is to improve schedule attainment performance and reduce inventories, as well as lead times 25 (Frazier et al., 2000). Drum-Buffer-Rope is a manufacturing execution methodology, named for its three components. – – – The drum is the physical constraint of the plant: the work centre or machine or operation that limits the ability of the entire system to produce more. The buffer protects the drum, so that it always has work flowing to it. Buffers in DBR have time as their unit of measure, rather than quantity of material. The rope is the work release mechanism for the plant. Pulling work into the system earlier than a buffer time guarantees high work-in-process and slows down the entire system. It was also reported Drum-Buffer-Rope as the synchronisation for agility purpose (Walker, 2002). This can support optimisation, possibly for both lean and agile manufacturing as two different balancing points for the synchronisation. However, few companies can keep the focus on bottlenecks (as they are hard to identify or too often keep changing) to plan and control production. It cannot become a popular way due to such a limitation from the Theory of Constraints (TOC) as the base of synchronous manufacturing. In fact, the synchronisation should not be related only to the constraints – it is more reasonable to act above the business bottom-line if the tolerance is needed as a must from the view of synchronisation. 2.1.2 ESP concept beyond JIT philosophy In high-mix manufacturing, a new concept of Equalised and Synchronised Production (ESP) has been researched by Toshiki Naruse for a revolution beyond the Japanese Just-In-Time (JIT) system (Naruse, 2003). According to Naruse (2003), the new system of ESP has the following features in the development: – ESP original concept one: Production guard strictly to customer needs is inefficient. – – 26 Hint: Need product inventory to separate production schedule from direct link to the buyer’s orders. ESP original concept two: To fulfil the production division’s mission, daily production output and production sequences must be stabilised, with production output equalised among the various item numbers. – The production Division’s mission: – – To maximise production efficiency by making and maintaining improvements toward that end. To minimise inventory by working toward the goal of zero inventory. For the JIT concept, the supplier or its warehouse must physically locate its plants either within the manufacturer’s site or nearby. If located far away, it is hard for them to make synchronisation well enough to meet the requirements of demandsupply (specific volumes and delivery deadlines for specific product items). However, Naruse (2003) claimed the ESP approach is the best way for suppliers in various industries. As a feature or a limitation from view of Naruse (2003), the system of ESP is more for a parts supplier to deliver products made on its production lines to multiple buyers / locations. JIT is more for a company to purchase material from a parts supplier and assemble them to finished products, or a parts supplier to built dedicated production lines synchronised with the production of corresponding buyers. The ESP production system basically uses the periodic reordering of variable amounts method. Both production and purchasing can use the multiples of these equalised units. It also needs to ensure the supplier implements synchronisation with the buyer’s delivery deadline. Shortening lead time, using smaller lots and raising in-house production efficiency are all key activities under ESP. Comparing with JIT of 100 percent response to orders from customers, ESP emphasises maximising in-house production efficiency and minimising inventory as its focus. 2.1.3 Agile manufacturing and leagility concepts Because of the complexity of today’s supply chains, another direction of operational improvements leading to agile manufacturing has been discussed widely (more radical than the above lean-alternatives of synchronous manufacturing or ESP). Other names include responsive manufacturing and supply chain flexibility. The 1990s is associated with two important considerations of agility and supply chain in a history review by Sharifi et al. (2006). A summary of the literature on supply chain flexibility can be found from Stevenson et al. (2007). There is also a list of the contributors relating to flexibility / responsiveness / agility in Reichart et al. (2007). 27 Agile manufacturing is a vision of manufacturing that is a natural development from the original concept of lean manufacturing (Gunasekaran, 1999). Yusuf et al. (1999) indicates the main driving force behind agility is change. It is recognised as a necessary condition for competitiveness. The comparison of lean supply with agile supply can be seen in the following Table 1 (Mason-Jones et al., 2000): Table 1. The comparison of lean supply with agile supply. Distinguishing attributes Lean supply Agile supply Typical products Commodities Fashion goods Marketplace demand Predictable Volatile Product variety Low High Product life cycle Long Short Customer drivers Cost Availability Profit margin Low High Dominant costs Physical costs Marketability costs Stockout penalties Long-term contractual Immediate and volatile Purchasing policy Buy goods Assign capacity Information enrichment Highly desirable Obligatory Forecasting mechanism Algorithmic Consultative However, it is very rare to see benchmark cases from famous companies for agile supply operation as well as IT applications (Helo et al., 2006). More and more, researchers are adjusting the concept backwards and forwards, using with a new word, “leagility” – better to keep efficiency and flexibility always together. It is a more balanced thinking to compare or combine both factors properly in business. According to Mason-Jones et al. (2000) leagility is the combination of the lean and agile paradigm within a total supply chain strategy by positioning the decoupling point so as to best suit the need to respond to volatile demand. 2.1.4 Manufacturing strategies and product life cycle Scholarly research in the manufacturing strategy field has moved its focus more and more to the total impact on product life cycle, as well as to the trend to whole supply chain in a global scale (Aitken et al., 2003). Aitken (2003) identified the operational differences of demand-supply network needed in each phase of product life cycle (PLC) as an interesting example of those multiple choices at 28 strategy level. The strategic effect from a higher level can provide a larger tolerance to supply operation. Holmström et al. (2006) reported external collaboration initiatives such as Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR) not being sufficient on their own to produce improved efficiency and responsiveness. Firms need to actively co-ordinate internal collaborative practices between functions to benefit from their development projects with customers and suppliers. As the view of Hilletofth et al. (2010), it has been always a big challenge how to bring new product to the market faster as a competitive advantage, which remains to be an essential need in high-tech industries discussed. In markets where short product life cycles are the norm, delays in bringing products to the market can have detrimental consequences to sales and profit. To remain competitive in these environments, companies need to produce innovative, high quality, highly value-added products and services and bring them quickly and effectively to the market. Hilletofth et al. (2010) emphasise two major issues need to be addressed: – – The need to develop innovative, value-adding products The necessity of bringing them quickly to the market. 2.1.5 The innovator’s strategy With the additional interest of radical innovation in industries, a further review was conducted of the innovator’s strategy (Christensen, 2003) about an extraordinary way of competing by disruption in business, as well as its great impact especially on the manufacturing operation. There are two kinds of industrial innovation: Sustaining or disruptive innovation. A sustaining innovation targets satisfying highly demanding customers by incremental improvements in products with better performance, rather than what was previously available. A disruptive innovation model shapes the strategies for those new growth builders to win the fights. To create a new value network on the third axis is called new-market disruptions. According to Christensen (2003), it brings an opportunity for the company to satisfy the customer well enough by squeezing the bubble out of disruptive innovation. The innovation is thus leveraged by the value as business driver focused clearly on the customers. 29 With its big impact, disruptive innovation can act as a force also in manufacturing, for example, going to market as soon as possible to take more risks than in normal time. This is the reason to use the innovation in manufacturing strategies along with product life cycle changes as a new thinking, which actually also happened in one of the cases in the action research. 2.1.6 Summary of manufacturing philosophies This thesis utilises the following concepts from earlier research as theoretical foundation: 1. New JIT to adopt postponement for a leaner efficiency as synchronous manufacturing Originally JIT was oriented for a repetitive manufacturing environment. Synchronous manufacturing was developed for low-volume/high-mix production. Concepts related to JIT operational strategy include lean and postponement principles together with flexibility in the manufacturing process. (Cusumano, 1992; Gunasekaran, 1999; Haan et al., 1999; Frazier et al., 2000; Vokurka et al., 2000; Amasaka, 2002; Coronado M. et al., 2002; Doran, 2002; Papadopoulou et al., 2005; Bhasin et al., 2006; Graman et al., 2006; Holweg, 2006; Ruffa, 2008). 2. Agile manufacturing to achieve flexible and responsive operation The concepts related to agile manufacturing are claimed to be the next steps after the lean philosophy in production management evolution. Their focus is to respond to customer needs and market changes faster while still controlling costs and quality. These agile concepts are suitable for product-based industries with unstable markets and volatile demand, as well as products with short life cycles. (Brennan et al., 1999; Gunasekaran, 1999; Yusuf et al., 1999; Rigby et al., 2000; Hoek et al., 2001; Little et al., 2001; Prater et al., 2001; Welker et al., 2005; Sharifi et al., 2006; Swafford et al., 2006; Reichhart et al., 2007; Stevenson et al., 2007). 3. The leagility to combine lean and agile characteristics The definition of leagility, i.e. combining leanness and agility, was originally developed to describe manufacturing supply chains. The basic idea behind leagility is the existence of a decoupling point, which separates the lean 30 processes from the agile processes in the supply chain. Lean processes are seen to be on the upstream side of the decoupling point, and agile processes on downstream. A similar concept is applicable also within a company. Lean and agile concepts can be applied at different stages of the same manufacturing process, for different machines and parts, etc. In this case, a level of buffer stock is maintained between lean and agile manufacturing strategies. (Bonney et al., 1999; Naylor et al., 1999; Robertson et al., 1999; Bolander et al., 2000; Hoek, 2000; Mason-Jones et al., 2000; Pagell et al., 2000; Sahin, 2000; Takahashi et al., 2000; McCullen et al., 2001; Prince et al., 2003; Christopher et al., 2002; Stratton et al., 2003; Corti et al., 2006; Hoque et al., 2006; Stratton et al., 2006; Krishnamurthy et al., 2007; Mohebbi et al., 2007). 4. Manufacturing strategy management focused for superior demand-supply performance Demand-supply performance is further studied for optimising, not only a company, but also its ecosystem. Competitive advantages of global manufacturing can be achieved if the supply chain has less organisational boundaries. The key is to simultaneously aim for operational efficiency and market responsiveness, including all parties. (Lummus et al., 1998; Banerjee, 2000; Golder, 2000; Sahin, 2000; Brassler et al., 2001; Olhager et al., 2001; Christopher et al., 2002; Hinterhuber et al., 2002; Loch et al., 2002; Brown et al., 2003; Stratton et al., 2003; Hui, 2004; Hallgren et al., 2006; Brown et al., 2007). 5. Others: product innovation, agent-based modelling, IT implementation proposal, research methodology This group of concepts ensures the research supporting a wider knowledge base. For example, the innovation through product changes is in the focus of this research. The development of IT tools for optimising manufacturing execution can be also important, as well as right methodology. (Papandreou et al., 1998; Bajgoric, 2000; Davidrajuh et al., 2000; Thomke et al., 2000; Corbett et al., 2001; Coronado M. et al. 2002; Coughlan et al., 2002; Forza, 2002; Mandal et al., 2002; Walker, 2002; Dooley et al., 2003; Jalote et al., 2004; Ottosson, 2004; Ashayeri et al., 2005; Buxey, 2006; Helo et al., 2006; Nilsson et al., 2006). 31 In order to ensure the literature review focusing on manufacturing optimisation, the discussion includes synchronous manufacturing, Equalised and Synchronised Production (ESP), the Leagility, Manufacturing Strategies in Product Life Cycle, and the Innovator’s Strategy. 2.2 Developing demand-supply network It has been many years as a popular thought that DCM (Demand Chain Management) and SCM (Supply Chain Management) are not separate but inextricably intertwined (Min and Mentzer 2000) The demand-supply network management concept of Holmström et al. (1999) proved to be a useful tool in analysing the demand and supply balancing mechanisms (Auramo and Ala-Risku 2005). Combining push-based supply chain and pull-based demand chain together, the study is better focused directly on demand-supply network theory more applicable to case company in the research. The reason is no major difference between the demand and supply chain with respect to the network of organizations involved, which are all to create, produce, and deliver customer value. (Hilletofth 2010). 2.2.1 Value oriented development for demand-supply network The target of developing demand-supply network is to maximise the overall value generated. Value as a key of winning in competition According to the analysis by Chopra & Meindl (2001), the value is the difference between what the final product is worth to the customer and effort the supply chain expends in filling the customer’s request. The success key is the appropriate management of all flows of information, and product, generating costs within the supply chain. Monczka and Morgan (2000) identified those “critical six” as follows to be the trend of developing demand-supply network: – – – – 32 Increasing efficiency requirements Making use of information technology Integration and consolidation Insourcing and outsourcing – – Strategic cost management “Network” management. For example, PC (Personal Computer) industry has many ways to organize the value chain in a network manner. Curry and Kenney (1999) illustrated that the traditional production-distribution channel (such as IBM and Compaq) co-existed with new emerging structures represented by “local assemblers” and “direct marketers” such as Dell. Such a complexity as global operation scale has been also seen nowadays widely in other high-tech industries. Ketchen et al. (2008) presented a tool as the best value supply chains designed to deliver superior total value to the customer in terms of speed, cost, quality, and flexibility. It is not just simply to create low costs, but also to maximise the total value added to the customer. Relative to traditional supply chains, best value supply chains also take much different approaches to key functions such as strategic sourcing, logistics, information systems, and relationship management. Thinking as a networked way Wu and Zhang (2009) introduced the value network perspective into the field of business model study and discussed basic issues about business model such as definition, elements and classification through the lens of value network. From the perspective of value network, the definition of its business module is the system connecting internal and external actors by value flows to create, deliver and capture value: – – – Value actors as the network nodes Value flows as the network relation Part of or the whole value network as the network structure. In comparison with real business cases, Wu and Zhang (2009) summarised business model innovations of value network as follows: – – – – – Business model innovation based on actor change Business model innovation based on relation change Business model innovation based on network subdivision Business model innovation based on network extension Business model innovation based on network integration. 33 Gadde and Håkansson (2001) studied activity co-operation of JIT (Just-In-Time) deliveries with numerous activities conducted by a large number of actors as a network view. The complexity of strategising in networks is related to their multidimensionality. Any change has some direct effects but also a number of indirect effects, on other firms, impact on the actor’s performance. The focus is emphasised on the interdependence among the activities conducted by customer and supplier and call for more co-ordination than is needed when inventories serve as buffers. The main issue in all network thinking is that “others” need to be included. The second key aspect is related to time. The importance of others and the crucial time dimension indicate that boundaries are key issues in all network thinking. Focus on demand or supply? Esper et al. (2010) emphasised two primary sets of processes through which the firm creates value for its customers by moving goods and information through marketing channels: demand-focused and supply-focused processes. Historically, firms have invested resources to develop a core differential advantage in one or other of these areas—but rarely in both—often resulting in mismatches between demand (what customers want) and supply (what is available in the marketplace). Yusuf et al. (2004) also found supply chains (or demand-supply network) were understood mainly in terms of long-term upstream collaboration with suppliers. However, an equal amount of emphasis is then paid to downstream collaboration with customers and even collaboration with competitors as a means of integrating the total value creation process. Hilletofth and Hilmola (2010) indicated management of the demand side (DCM – Demand Chain Management) being revenue driven and focused on effectiveness whilst the management of the supply side (SCM – Supply Chain Management) having a tendency to be cost oriented and focus on efficiency. Together these management directions determine a company’s profitability and thus need to be coordinated, requiring a demand supply oriented management approach. As the finding of Hilletofth (2010), it is important to promote the coordination of DCM and SCM, which can occur within a particular company and across the demand supply chain at different planning levels (strategic, tactical, and operational). From a survey result by Boonyathan and Power (2007), following outcomes were found: 34 – – – Supply uncertainty is a more significant determinant of performance than demand uncertainty. Closer relationships with trading partners are associated with higher levels of performance. Uncertainty can be reduced by being more closely aligned with both suppliers and customers. Mason-Jones et al. (2000) emphasised that the success and failure of supply chains are ultimately determined in the marketplace by the end consumer. Getting the right product, at the right price, at the right time to the consumer is not only the lynchpin to competitive success but also the key to survival. According to the report from Ervolina et al. (2006), availability management process called Available-to-Sell (ATS) is an example that incorporates demand shaping and profitable demand response to drive better operational efficiency through improved synchronisation of supply and demand. IBM has implemented an ATS process in its complex-configured server supply chain in 2002. The realized savings include $100M of inventory reduction in the first year of implementation and over $20M reduction annually in the subsequent years. New trend of operations management As a strong trend, demand management should be more integrated in supply operation to increase customer satisfaction and life cycle profit (Reiner et al. 2009). As the view of Frohlich and Westbrook (2002), the DCM strategy appeared to be the best overall approach for manufacturers to follow and the relatively few manufacturers that are already following this approach. As Ettl et al. (2006) described, a demand-driven supply network (DDSN) is a system of technologies and business processes that senses and responds to real time demand across a network of customers, suppliers, and employees. DDSN principles require that companies shift from a traditional push-based supply chain to a pullbased, customer-centric approach. Waters and Rainbird (2008) even claimed the demand chain and response management is new direction for operations management. Supply chain management would appear to be at the end of its lifecycle. Customers of all types are expressing preferences based upon some degree of product-service differentiation and not simply on cost. They suggested the supply chain is obsolescent and should be replaced by a more proactive response system. 35 2.2.2 Manufacturing strategies affect demand-supply network Mason-Jones et al. (2000) presented that classifying supply chain design and operations according to the Lean, Agile and Leagile paradigms enables the companies to match the demand-supply type according to marketplace need. For example, they could be mechanical precision products (lean); carpet manufacture (agile); and electronics products (leagile). Multiple strategy choices Christopher and Towill (2000) summarised the differences on how to apply lean or agile thinking for demand-supply network affected by manufacturing strategies. The lean paradigm requires that ``fat'' is eliminated. However, the agile paradigm must be ``nimble'' since sales lost are missed forever. An important difference is that lean supply is associated with level scheduling, whereas agile supply means reserving capacity to cope with volatile demand. Lack of agile benchmark cases brings the difficulty to understand such a concept clearly. As the view of Yusuf et al. (2004), the agility of a supply chain is a measure of how well the relationships involved in the processes of design, manufacturing and delivery of products and services. Monroe and Martin (2009) described that agility in the supply chain is described as being able to “respond to sudden and unexpected changes in markets. Agility is critical, because in most industries, both demand and supply fluctuate more rapidly and widely than they used to. According to the explanation of Mason-Jones et al. (2000), leagile supply chains already exist in the real world. Just as case company due to big differences of material supply lead-time, there is decoupling point in demand fulfilment process where order-driven way changed to forecast-driven way. Design of demand-supply network to support strategies Vonderembse et al. (2006) defined the characteristics for standard, innovative, and hybrid products, and provided a framework for understanding lean and agile supply chains. Lean supply chains (LSCs) employ continuous improvement efforts and focus on the elimination of nonvalue added steps across the supply chain. Agile supply chains (ASCs) respond to rapidly changing, continually fragmenting global markets by being dynamic, context-specific, growth-oriented, 36 and customer focused. Hybrid supply chains (HSCs) combine the capabilities of lean and agile supply chains to create a supply network that meets the needs of complex products. As the view of Vonderembse et al. (2006), early in their product life cycle, innovative products, which may employ new and complex technology, require ASC. As the product enters the maturity and decline phases of the product life cycle, a LSC could be more appropriate. Hybrid products, which are complex, have many components and participating companies in the supply chain. Some components may be commodities while others may be new and innovative. Hilletofth (2009) suggested that companies need to use several SC (Supply Chain) solutions concurrently (i.e. develop a differentiated SC strategy) to stay competitive in today’s fragmented and complex markets. The arguments in favour is that there are no SC strategies that are applicable to all types of products and markets and since companies usually offer a wide range of products and services in various types of non-coherent business environments. In particular, Hilletofth and Hilmola (2010) also emphasised a need for real life based industrial case studies addressing how the various demand and supply processes influence each other and how they can be coordinated across intra- and inter-organizational boundaries. Thus, benefits to all parties should be aimed for developing win-win solution in demand-supply network co-operation. The differences in supplier selection were further studied by Chopra and Sodhi (2004) how to plan the manufacturing in demand-supply network smarter: When planning capacity, companies should select an efficient, low-cost supplier for fast-moving (low-risk) items. In contrast a more responsive supplier better suits slow-moving (high-risk and high-value) items. For example, Cisco tailors its response by manufacturing fast-moving products in specialised, inexpensive but not-so-responsive Chinese plants. High-value, slow-moving items are assembled in responsive, flexible (and more expensive) U.S. plants. Santoso et al. (2005) reported a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Santoso et al. (2005) proposed a methodology to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. 37 Lead time reduction as strategic effect Amoako-Gyampah (2003) indicated that manufacturing strategy represents the way a company plans to deploy its manufacturing resources and to use its manufacturing capability to achieve its goals. Lead time has been recognised as a very important issue in almost all strategy theories. It is one of the root-causes to determine the choice of manufacturing strategies in many cases. From the view of Sapkauskiene and Leitoniene (2010), speed as a competitive factor is gaining more and more importance for companies involved in global market competition. The company tends to compete for rapid response to consumer demand and new products and technologies introduced to the market. This type of competition in terms of reaction time is described as time based competition (TBC). Comparing to lead time reduction in production, such an effort in demandsupply network is often limited so as to bring big operational uncertainty and the bullwhip effect significantly. The time gains so greater importance, as speed, which is required by business and consumer expectations, continues to increase even more (Sapkauskiene and Leitoniene 2010). Lyu and Su (2009) described the challenges in demand-supply including uncertainty of customers’ demands, high inventory levels and cost, inaccurate due date estimation, and slow response to customer inquires. Lead time reduction is a critical issue which enables manufactures to solve problems. They proposed extended master production scheduling (MPS) system, developed using Internet technology, can be deployed in a supply chain environment. As similar philosophy focused for reducing lead time, Quick Response Manufacturing (QRM) developed by Rajan Suri is a strategy that enables companies to significantly improve their productivity and their competitive edge. Suri (1998) presented the way how QRM has refined time based competition by: – – – – – – 38 Focusing only on manufacturing. Taking advantage of basic principles of system dynamics to provide insight into how to best reorganise an enterprise to achieve quick response. Clarifying the misunderstandings and misconceptions managers have about how to apply time-based strategies. Providing specific QRM principles on how to rethink manufacturing process and equipment decisions. Developing a whole new material planning and control approach. Developing a novel performance measure. – Understanding what it takes to implement QRM to ensure lasting success. Suri (2002) claimed that JIT (Just-In-Time) was perfected by Toyota over 30 years ago. For certain markets, lean manufacturing has several drawbacks. Quick Response Manufacturing (QRM) can be a more effective competitive strategy for companies targeting such markets. Specifically, QRM is more effective for companies making a large variety of products with variable demand, as well as for companies making highly engineered products. Suri (2003) explained why QRM has greater competitive potential and described POLCA (Paired-cell Overlapping Loops of Cards with Authorization), a material control system to be used as part of QRM. The combination of QRM and POLCA will provide companies with significant competitive advantage through their ability to deliver customised products with short lead times. Suri and Krishnamurthy (2003) explained that POLCA is a hybrid push-pull system that combines the best features of push/MRP systems and Kanban/pull control, while at the same time avoiding their disadvantages. The flow of orders through the different production cells is controlled through a combination of release authorizations (High Level Materials Requirements Planning system or HL/MRP) and production control cards known as POLCA cards (not part-specific like a Kanban card). The release authorization times only authorize the beginning of the work, but the cell cannot start unless the corresponding POLCA card is also available. A POLCA card is a capacity signal, while a pull/Kanban signal is an inventory signal. If there is no authorized job, then no job is started, even though a POLCA card is available. It should be designed available capacities are not significantly below the required levels. From the description by Suri and Krishnamurthy (2003), there are Safety Cards, which are only used to release POLCA cards that get stuck in the loop due to occasional component part shortages. After a period of time, statistics from these incidents will provide concrete insight into root causes of the shortages. As their suggestions, the key metrics are measured as follows: – – – – – The lead times for the products The throughputs of the cells The reliability of delivery between cells WIP inventories at various points in the system The on-time delivery performance of upstream and downstream cells in the POLCA loops. 39 Vandaele et al. (2005) also reported the implementation of an E-POLCA system in a paperless – cardless – environment. It is a load based version for a multiproduct, multi-machine queuing network to determine release authorisations and allowed workloads. 2.2.3 The role of collaboration in demand-supply According to the explanation of Kaipia and Hartiala H (2006), manufacturing companies need the collaboration with customers and suppliers to improve the performance of demand-supply network. Better information-sharing can reduce both the bullwhip effect and the operational risk (such as the level of safety stocks). Networked collaboration for better performance Holweg et al. (2005) discussed that collaboration in the demand-supply network comes in a wide range of forms, but in general have a common goal: to create a transparent, visible demand pattern that paces the entire supply chain. Such collaboration is for jointly creating the common pace of information sharing, replenishment, and supply synchronisation in the system to reduce both excess inventory and the costly bullwhip effect. For example, Ryu et al. (2009) can identify types of demand information according to their timestamp. There are three types of demand information classified according to where they are located along the time-axis. These are realised demand information, planned demand information, and forecasted demand information. Two different information-sharing methods are defined according to types of shared information and sharing procedures. One is the ‘planned demand transferring method (PDTM)’ and the other is the ‘forecasted demand distributing method (FDDM)’. Udin et al. (2006) proposed a collaborative supply chain management framework. Normally, supply chain management (SCM) is a system that contains multiple entities, processes and activities from suppliers to customers. – 40 The basic concept behind SCM is how the raw materials and information flow from the supplier to the manufacturer, before final distributions to customers as finished products or services. – – In addition, functional areas within the organisation also need information that flows through the SCM in order for them to make a decision to produce products. The capability of sharing and exchanging information is essential to improve the effectiveness of the SCM. Udin et al. (2006) provided a collaborative framework how to analyse the gap between the current and the desirable position (benchmark) for its effective implementation in organisation. Heikkilä (2002) described about the collaboration oriented more by changing from SCM (Supply China Management) to DCM (Demand Supply Management) with following propositions: 1. 2. 3. 4. 5. Good relationship characteristics contribute to reliable information flows. Reliable information flows contribute to high efficiency. Understanding the customer situation and need and good relationship characteristics contribute to co-operation between the customer and supplier. Good co-operation in implementing demand chain improvement contributes to high efficiency and high customer satisfaction. High customer satisfaction contributes to good relationship characteristics. Collaboration to reduce bullwhip effect As explained by Ismail (2009), bullwhip effect is a major problem in supply chains. It means the amplification of orders as you go up along the supply chain. The bullwhip effect is a phenomenon that was discovered by Forrester (1958) who realized that variations of demand increase up the supply chain from customer to supplier, what was called the Bullwhip Effect or known as the Forrester Effect. Holweg et al. (2005) also described that unpredictable or nontransparent demand patterns have been found to cause artificial demand amplification in a range of settings, which is also referred to as the ‘bullwhip’ effect’ (Lee et al., 1997; Lee, 2002). This leads to poor service levels, high inventories and frequent stock-outs. After studying three proposed scenarios, Bolarin et al. (2008) concluded that collaborative structures improve the Bullwhip effect and reduce the total costs of the supply chain in which these structures applied. Those are 3 scenarios in the simulation: Traditional Supply Chain, VMI (Vendor Management Inventory) (based on collaborative structures among the members that make up the Supply 41 Chain), and EPOS (Electronic Point of Sales). In the collaborative EPOS scenario, the end consumer sales are sent to all members of the supply chain. Specifically, in this strategy the end consumer sales may be used by each echelon for their own planning purposes, but each echelon still has to deliver (if possible) what was ordered by their customer (Disney et al 2004). The EPOS chain has proved to be more efficient than the VMI and the traditional ones in reducing the Bullwhip effect and in holding costs. Susarla et al. (2004) argued that advances in information technology (IT) that improve coordinated information exchange between firms result in a significant impact on measures of operational efficiency such as time to market, inventory turnover, and order delivery cycle time. To reduce bullwhip effect, IT can also make it possible by exchanging information on a variety of parameters such as demand and inventory related information, process quality information, feedback from customers etc. Collaborative risk management Christopher and Lee (2004) noticed that many companies have experienced a change in their supply chain risk profile as a result of changes in their business models, for example the adoption of ‘lean’ practices, the move to outsourcing and a general tendency to reduce the size of the supplier base. As their view, the improvements in confidence can have a significant effect on mitigating supply chain risk. Snyder et al. (2006) researched about supply chain disruptions. It needs to consider the risk of disruptions when designing supply chain networks. Supply chain disruptions have a number of causes and may take a number of forms. They presented a broad range of models for designing supply chains resilient to disruptions. For example, these models can be categorised by the status of the existing network: A network may be designed from scratch, or an existing network may be modified to prevent disruptions at some facilities. Snyder et al. (2006) emphasised that the companies may face costs associated with destroyed inventory, reconstruction of disrupted facilities, and customer attrition (if the disruption does not affect the firm’s competitors). In addition, the competitive environment in which a firm operates may significantly affect the decisions for risk mitigation. The key objective may be to ensure that their post-disruption situation is no worse than that of their competitors. 42 Goh et al. (2007) presented a stochastic model of the multi-stage global supply chain network problem, incorporating a set of related risks: supply, demand, exchange, and disruption. With the increasing emphasis on supply chain vulnerabilities, effective mathematical tools for analysing and understanding appropriate supply chain risk management are now attracting much attention. They provided an optimal solution with profit maximisation and risk minimisation objectives. Thomas and Tyworth (2006) discussed about pooling lead-time risk by order splitting. The policy of pooling lead-time risk by simultaneously splitting replenishment orders among several suppliers continues to attract the attention of researchers even after more than 20 years of extensive study. The research has following major tracks: – – Modelling effective lead-time demand under a variety of stochastic assumptions and enabling an assessment of the impact of pooling on reorder points, stockout risk, safety stock, and shortages. Modelling cost tradeoffs on a comparison of the long run average total costs for single-source versus dual- or multiple-source models under identical conditions. Thomas and Tyworth (2006) revealed two important and persistent limitations: – – The models do not give appropriate attention to transportation economies of scale. Specifically, there are important gaps with respect to the true magnitude of transportation cost, as well as the impact of order quantity (weight), supply lines (distance), and mode (especially air versus ocean shipments in a global setting) on transportation and incremental ordering costs. The current theory that a reduction in average cycle stock is the key benefit of splitting orders simultaneously considers only the buyer’s on-hand inventory in the supply chain. The absence of in-transit inventory is an important limitation, because simultaneously splitting an order among suppliers does not reduce the combined amount of in-transit stock and cycle stock in the system. Consequently, the only meaningful benefit of pooling lead times is to safety stock from a total system-cost perspective. Thomas and Tyworth (2006) also introduced other options such as a single supplier to receive an order and then split it into smaller shipments released 43 sequentially. The long-term transportation commitments can also absorb some of the demand variability at the consumer-facing point in the supply chain. 2.2.4 Measuring demand-supply performance As the view of Jammernegg and Reiner (2007), supply chain performance improvement should be measured by reduced total costs (transport, inventory carrying and resources), as well as improved customer service (delivery performance). For MTO (Make-To-Order) and ATO (Assemble-To-Order) production, delivery performance (percentage of orders fulfilled within the promised delivery time (or due date)) is used as measure of delivery reliability. However, the trade-off between inventory cost and capacity cost has to be considered. Reiner (2005) also discussed how performance measures derived from total quality management (TQM) models could help to overcome the limitations of financial measures. In such a context, process management and customer orientation occupy a central position. The performance of demand-supply network should be measured so as to ensure its improvement accountable or at least visible. As one of other more comparable options, it is also better to use existing key performance indicators for a SCOR (Supply Chain Operations Reference) model, which can compare other cases in this field. Here is an overview of SCOR model (Supply Chain Council, 2005): SCOR-model key performance indicators 1. Customer focus – – – – – – 2. Internal cost focus – – – 44 Delivery performance Fill rate Order fulfilment lead time Perfect order fulfilment Supply chain response time Production flexibility Total supply chain management cost Cost of goods sold Value-added productivity – – – – – Warranty cost or returns Processing cost Cash-to-cash cycle time Inventory days of supply Asset turns. Ho et al. (2005) emphasised the SCOR model is to help companies in managing their supply chain. Process reference models integrate the mechanisms of business process reengineering, benchmarking, and process measurement in a cross-functional framework to helping companies to capture the “as-is” state of a process and derive the desired “to-be” future status. However, Ho et al. (2005) also indicated that SCOR does not provide a mechanism for measuring uncertainty to enable a company to understand clearly the problems related to uncertainty before the setting strategy. Besides, Drzymalski and Odrey (2006) summarise a list of performance metrics options from literature review, as well as ISO9001 and FEA (Federal Enterprise Architecture) Consolidated Reference Model Document v2.0. Chan (2003) presents following performance measurements as the suggestion. Apart from the common criteria such as cost and quality, five other performance measurements can be defined: resource utilisation; flexibility; visibility; trust; and innovativeness. Kaipia et al. (2007) introduced another option as the time benefit method, which compares two potential collaboration modes as the following steps: 1. 2. 3. 4. 5. Describe the existing mode of replenishment process – the base case – and one alternative mode. Collect demand data for both alternatives to be examined. Calculate the following for each item in the product range, and for both the base case and the alternative solution. Calculate for each item in the product range. Graph for each product item in the product range the time benefit and reordering amplification of demand. For applying the thought from Kaipia et al. (2007) to product change implementation, the most of key components (such as material supply normally) belong to the base case and others belong to attentive case (such as VMI). Furthermore, the trend of leading companies in high-tech industry has been changed to using IT (Information Technology) solutions as a must in demand45 supply performance (Kauremaa et al. 2004). Auramo et al. (2005) found the IT solutions to be divided into three categories, 1) transaction processing, 2) supply chain planning and collaboration, and 3) order tracking and delivery coordination. The role of information technology is shifting from a passive management enabler through databases to a highly advanced process controller that can monitor each activity (Gunasekaran et al. 2001). New idea or theory how to measure the performance should be embedded in information technology tools as IT-enabled research and development (Dong 2010). It could improve real business in global scale and also bring reliable academic value, which is a trend focused on how to leverage knowledge faster and better than competitors (Thite 2003). In order to discuss such a trend, Auramo et al. (2005) presented an explorative study about the benefits and their observations of IT involvement in performance measurement. To gain strategic benefits, the use of IT has to be also coupled with business process re-design. It is a new normal of playground for business and a new interesting field for academic research, which is so called IT enabled innovation (Watad 2009). 2.2.5 Purchasing automation challenge in product life cycle Purchasing is a key activity in demand-supply operation especially hard in dynamic product changes. Hilmola et al. (2008) suggested why a portfolio approach of using different purchasing policies may be central to new intelligent purchasing systems. A portfolio approach means lot for lot policy (L4L - The order or run size is set equal to the demand for that period) may be useful in an early phase of the product life-cycle, and later it may be an advantage to change over to economic order quantity (EOQ) based ordering. Jammernegg and Reinera (2007) described about the trade-off of inventory level in purchasing operation. On the one hand, different types of inventory are necessary to buffer against market and operational uncertainties but, on the other hand, inventory is sometimes the result of inefficient management of the supply chain processes. Therefore, inventory management has been a focal point of managing supply chain processes. As emphasised by Hilmola et al. (2008), accuracy of demand forecasting is vital to switching point estimation. One potential method for tracking these signals of that switching point was mentioned as the development of the GARCH technique (proven useful in financial risk management and awarded the 2003 Nobel Prize in Economics). GARCH stands for Generalized Auto Regressive 46 Conditional Heteroscedasticity, which is an econometric model used for modelling and forecasting time-dependent variance. Lin (2010) proposed a GARCH based collaborative planning, forecasting, and replenishment (CPFR) model. Through numerical analysis, a GARCH based collaborative forecasting model is much suitable than the other time series forecasting model. From the view of Lin (2010), ability to evaluate and qualify risk associated with volatility by GARCH will enable businesses to collaboratively manage inventory risks better and benefit both parties. Meanwhile, through setting an optimal safety multiplier in exception policy, an exception demand also can be efficiently and effectively controlled to maximise the net present value. According to the view from Rantala and Hilmola (2005), business conditions of electronics manufacturers are demanding due to ever shortening product life cycles, higher variety and increased outsourcing activity. Even though companies could manage the increasing amount of purchased items with modularity, software-based customization and well designed product platforms; the case is often so that item count in purchasing is increasing with high rates. Rantala and Hilmola (2005) proposed about purchasing automation to solve it as a combination of ERP system integration as well as supply chain solutions, which was measured by inventory turns. Based on the case study of a middle-sized telecom electronics manufacturer, Rantala and Hilmola (2010) further reported that an entirely automated order enables the full use of ‘economic order quantities’ and its derivatives with following factors in the conditions: – – – – Lead time for components is set to be five working days MOQ (Minimum Order Quantities) is the manufacturer package size and its coefficients Safety stock for parts is 20 days demand, estimated based on six months’ historical demand Period of Supply (POS) for needs is 15 working days. As the research finding of Rantala and Hilmola (2010), the inventory turns tend to move towards a common inventory turn level that is around ten times a year and component level variance declined a great deal by purchasing automation. However, it was worried MRP nervousness and component buffering services represent caveats for future APO implementations and use. 47 Dreyer et al. 2007 discussed the concept of Global Control Centre (GCC) for manufacturing activity. The main elements of the GCC is found to be the global control model, performance measurement system, ICT solutions and the organization and the physical environment. The GCC should decrease the level of complexity and improve control of operating environment for those main benefits: – – – – The access to true-time monitoring facilities at a high level A true SC (Supply Chain) perspective (different from a single actor perspective) Speeding up recognition and decision making Integrated decision making (for instance purchasing and production control). 2.2.6 Optimisation of demand-supply with thinking of BI automation Similar as purchasing automation, demand-supply network is mostly supported by Business Intelligence (BI) solutions with information technology to ensure its performance management (Blankenship 2004). BI is a field of the investigation of the application of human cognitive faculties and artificial intelligence technologies to the management and decision support in different business problems (Ranjan 2009). It also needs the thinking of automation to enlarge business value and create higher differentiation effect (Kaipia and Laiho 2009) for the companies to win in global competition. According to the view of Ranjan (2009, companies have understood the importance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts. However, it is a challenge in leading companies how to utilise huge amount of operational data for monitoring and reporting to achieve business excellence (Zicojinovic and Stanimirovic 2009). As the finding of Popovic et al. (2010), measuring the business value of business intelligence in practice is often not or hard carried out due to the lack of measurement methods and resources. Organisational or enterprise boundaries (Nightingale 2009) often cause the development of such competitive advantage extremely hard, which can be seen as lower priority if the company is always stuck in business fire-fighting issues. With own end-to-end insight, business intelligence automation, is thought as a journey of innovation how to visualise, connect, simplify and optimise the intelligences. The available knowledge can be found mostly about visualisation and optimisation of demand-supply network: 48 Visibility of Demand-supply Network Demand-supply network needs good enough visibility as a condition for optimising business operations. It has been one of top priorities in the most of companies for high-tech industries. Otherwise, it is very hard in daily work to match supply and demand with least inventory (Joshi 2000; Kaipia and Hartiala 2006). As observed by Falck et al. (2003), the challenge in developing an information management approach is to find solutions that enable information management across many different organizations. The issue of how to integrate external collaboration with internal processes is identified as a gap by Holmstrőm et al. (2003). As the view of Holweg et al. (2005), collaboration in the supply chain comes in a wide range of forms, but in general has a common goal: to create a transparent, visible demand pattern that paces the entire supply chain. Reducing uncertainty via transparency of information flow is a major objective in external supply chain collaboration. Kaipia and Hartiala (2006) have reported that there are several sources of information along the supply chain, differing in data quality, information delays, and usability. There is a challenge in choosing the most beneficial data sources and in making the best use of the data. Information-sharing can take place across various numbers of levels in the supply chain, the most typical being sharing information between two levels. The information needs also varies according to the role of each supply chain player and the location they have in the chain. Also, according to the finding from Lehtonen et al. (2005), replenishment collaboration, such as VMI, between manufacturers and distributors may be of little value in speeding up demand synchronisation in product introductions. As the view from Kaipia (2009), specific supply chain characteristics need to be balanced by selecting a coordination mechanism that uses information optimally to support the material flow. Flexible material flow needs frequent updates of the plan based on accurate information: – – If frequent information sharing and planning practices are used to support inflexible material flow, the result may be volatility in plans, and planning resources are wasted. If a flexible material flow is supported by inadequate information, waste may be produced in the material flow, in the form of excess inventories or capacity. 49 Obviously, the influence of demand-supply network integration on product innovation is greater than other variables (Baharanchi 2009). It is important that rapidly responding demand-supply requires more integrated planning and frequent information sharing (Kaipia 2009). As studied by Christopher and Towill (2000), lean or agile strategy needs also to emphasise information visibility in demand-supply network. Whereas information transparency is desirable in a lean regime, it is obligatory for agility. Lean forecasting is algorithmic, but agile forecasting requires shared information on current demand captured as close to the marketplace as possible. As the observation from Auramo (2006), visibility can be thus approached from both a tactical and strategic perspective. The tactical perspective focuses on transactions as it offers visibility to the flow of materials, available capacity and resources. From a strategic perspective, visibility enables evaluation and reshaping of such operational network more in line with changing business environments. Optimisation of Demand-Supply Network Thinking of business intelligence automation is not just for traditional automation of tasks that were previously performed by humans (Stohr and Zhao 1997). With visibility development focusing heavily on individual results, there are many opportunities to connect and simplify them further for new offers in business intelligence field. They are the steps leading to optimisation of demand-supply network, which can form a journey of business intelligence automation to develop great value. But, capturing the business value of business intelligence (BI) is a strategic challenge (Williams et al. 2003). It has bee hard to find those practical cases of optimisation reported in industrial or academic world. Especially, available information of the research is found more often as the simulation or mathematical models (Reiner et al. 2009, Sepehri et al. 2010). A focused review of literature is mostly to study the outcome (such as simulation result or conclusion) if applicable for its trial and implementation later in real business environment. The thoughts can be useful to support action research at least before own thinking of business intelligence automation will be continued. Reiner (2005) described how process improvements can be dynamically evaluated under consideration of customer orientation and supported by an integrated usage of discrete-event simulations models and system dynamics models. It was the use of selected performance measures as well as indicators by 50 a specific process improvement (postponement), which was conducted by an electronic manufacturer in the telecom industry. Using process simulation by Jammernegg and Reiner (2007), they demonstrated how the coordinated application of methods from inventory management and capacity management result in improved performance measures of both intra-organizational (costs) and inter-organizational (service level) objectives. It was conducted to a quantitative model-oriented research, based on empirical data. The results had shown that a change from MTS (Make-To-Stock) to ATO (Assemble-To-Order) production leads to reduction of total costs (shipping and inventory carrying) of 11% on average. Govindu and Chinnam (2007) described a generic process-centred methodological framework for analysis and design of multi-agent supply chain systems with following contributions: – – – Development and validation of generic methodological support for analysis and design of multi-agent supply chain systems. Creative adoption of SCOR (Supply Chain Operations Reference) to generic MAS (Multi-Agent Systems) development methodology. Introduction of the notion “process-centred organisation metaphor” for multiagent systems. Amer et al. (2008) provided a method optimising order fulfilment by six sigma and fuzzy logic, which is as an effective methodology for monitoring and controlling supply chain variables, optimizing supply chain processes and meeting customer’s requirements. Unlike product design where the final deliverable is a tangible product, the supply chain can be presented as an intangible component of service design (i.e. covering a work plan to meet supply targets, management of information flow, decision making, etc) with the tangible component being the practical implementation of the service design (actual hardware like logistics, transportation, information infrastructure, etc). Raj and Lakshminarayanan (2008) proposed that entropy based complexity minimization method is able to improve the performance of the distribution system significantly compared to the initial performance of the supply chain. This complexity management strategy can be extended to the overall network and for systems with more states of interest. The work aims to improve supply chain performance by quantifying and minimizing the complexity associated with the distribution system through entropy calculations in accordance with the business goal and demand pattern faced by the network. 51 Reiner and Fichtinger (2009) developed a dynamic model that can be used to evaluate supply chain process improvements, e.g. different forecast methods. In particular they used for evaluation a bullwhip effect measure, the service level (fill-rate) and the average on hold inventory. It was found that the bullwhip effect is an important but not the only performance measure that should be used to evaluate process improvements Rabta et al. (2009) discussed about queuing networks modelling software for manufacturing. In order to improve performance of a complex manufacturing system, the dynamic dependencies need to be understood well (e.g., utilization, variability, lead time, throughput, WIP, operating expenses, quality, etc). In this manner rapid modelling techniques like queuing theory, can be applied to improve such an understanding. Queuing networks are useful to model and measure the performance of manufacturing systems and also complex service processes. Radhakrishnan et al. (2009) studied inventory optimisation in supply chain management using genetic algorithm. It is a innovative and efficient methodology that works with the aid of Genetic Algorithms in order to facilitate the precise determination of the most probable excess stock level and shortage level required for inventory optimization in the supply chain so that minimal total supply chain cost is ensured. Sepehri et al. (2010) suggested a Corporate Supply Optimizer (CSO), as a central entity taking advantage of the notion of flow networks, gathers necessary operational information from members of the corporate supply chain. The CSO then guides supply chain members on ordering decisions for a minimum overall cost for the entire supply chain. The CSO seeks a solution with minimum total costs, unlike non-cooperative supply chains where individual members compete to optimize their local costs. 2.3 Product change management As Christopher (1998) explained, time has become a critical issue in management as the most visible feature in industries. Product life cycles are shorter than ever, industrial customers and distributors require just-in-time deliveries, and end users are ever more willing to accept a substitute product if their first choice is not instantly available. Product change management has to be applied as a key role in enterprise operation, which has been able to establish a differential advantage in high-tech industry. It can bring an end-to-end impact through the supply network 52 to other partners. According to Knight (2003), Product change management had been standardised to a popular process - enterprise change request and notice in its version control. In detail, it was mainly based on CM II principles of Closed Loop Change Process (CLCP), which was developed and marketed by the Institute of Configuration Management in co-operation with Arizona State University and the University of Tennessee. All results of product version changeover were recorded as quantified data no matter if the case was belonging to 75–85% of the fast track or 15–25% of large changes. For high-quality of product change management, a balance is needed between implementation speed at each manufacturing site and scraping cost in the whole supply network. Scraping cost was normally caused by those nonstandardised components in material supply not usable anymore after product changes. The targets of faster implementation and lower scraping cost should be controlled carefully in all changes. Particularly, every ECN was generated with change description detail, as well as Bill of Material (BOM) for product current and new versions. It included all affected sites and their implementation results. The analysis to indentify change drivers (key components) was essential for updating demand-supply status at weekly level and selecting changeover date with scraping cost known in advance as a quantitative manner. The amount of new, existing and closed ECN was followed monthly with implementation time as main focus. The scraping cost trend was also studied regularly according to product or manufacturing site. Quantified data in such a change management practice was traceable along with whole product life. As a research, the selection of those cases was oriented by different manufacturing strategies in order to present the results from ECN database in a comparable way but also with meaningful diversities. Christopher (1998) suggested that successful companies should have a productivity advantage (lower cost profile) or a “value” advantage (offering a differential “plus” – such as quick delivery), or a combination of the two. The research was aimed to develop a unique breakthrough that goes beyond either traditional lean or agile benchmarks (Krishnamurthy et al., 2007 or Mohebbi et al., 2007). As the research shown by Reiner et al., (2009), technology advances and competitive pressure have shortened the life cycles for many products and drastically increased the penalty of holding inventories. A major problem is that forecasting the volume of products with short life cycles is difficult. Therefore, 53 many supply chains rely on large inventory holding to reduce the risk of product unavailability, which is a costly way very slow for implementing product changes. The research of Reiner et al. (2009) has been targeted to mobile phone industry with simultaneous inventory and pricing decision in the consideration, which utilise the software tool Ithink to generate and analyse the mathematical formulation. However, it is a different challenge to study mobile infrastructure companies due to no product version overlapping there. For example, it is hardly to see any research about reducing material supply liability and obsolete cost in such product changes. With product innovation as a creative force proposed by Utterback (1996), new search can be thus as the first study in this field, including adequate details, covering new thinking to utilise product change cases to understand the nature of global manufacturing. 2.4 Special characteristics of high-tech industries 2.4.1 Challenges in forecasting Similar analyses of typical disturbances in industrial environments can be easily found from others researching the uncertainties of supply networks. According to Mascada (1998), they can be grouped to two main types: internal and external disturbances. The internal disturbances can include equipment failures, quality miss, lack of co-ordination, and workforce unavailability. The rest of the others are external disturbances caused by customers or suppliers. All of those factors affect the forecast, which is thus difficult to make it accurately. Another sample is from the research on different planning deviations and disruptions in the risk management of supply chains (Roshan et al., 2004) shown as Table 2: Table 2. Types of deviations Planning Level Type of Event Example Strategic Deviation Logistics/Manufacturing Capacity Reduction Disruption Supplier bankruptcy Tactical Deviation Order forecast Disruption Port strike Operational Deviation Lead-time variation Disruption Machine/Truck breakdown 54 The events at each level of corporate planning can bring challenges with different scales. As Roshan et al. (2004) states, both factors of regular deviations and major disruptions should be considered. Especially, the impact from the higher level can create a disaster with a system-wide effect. From all of the above analysis, it is reasonable to consider that the business forecast is unlikely to be right most of time in light of these uncertainties. People have to live with and survive business uncertainty by seeking other ways if not just to improve forecasting accuracy alone. As global markets are becoming more turbulent and volatile, it reveals such a common challenge in the industry affecting truly to everyone. Thus, it can be also as a great opportunity for research purpose. 2.4.2 Telecom supply chain of case company Manufacturing operation in telecommunications infrastructure industry is closely related to product life cycles. Such a feature of innovative business model has been studied by Bengtsson and Berggren (2002), which can be briefly as a basic introduction of the industry. As the operational type of case company, both product development and manufacturing functions are well combined in a project business way to fulfil customer demand (Collin 2003). For example, it includes prototype fabrication and pilot capability (zero-series production), departments for product industrialisation and high volume production (Bengtsson and Berggren 2002). Volume production can be also called flow production, repetitive flow production, or other names. Indicated by Terwiesch et al. (1999) for achieving a fast pay-back of investments in new product designs and production facilities, companies in high-tech industry must reduce their development time (time-to-market) as well as the time it takes them to achieve acceptable manufacturing volume, cost, and quality (time-to-volume). The reason for keeping volume production in-house, apart from cost and revenue considerations, is the importance of maintaining a high skill level in manufacturing from the view of Bengtsson and Berggren (2002). As Flynn (1994) emphasised, fast product innovation can be considered to be an element of world class manufacturing. Such a way can thus provide a rapid feedback from mass production to product design and engineering directly. As “product focused”, the manufacturing and sourcing operation is a “component oriented” manner, which means the company maintained strategic components and processes in-house, together with the majority of final assembly 55 and testing of ready modular products (Bengtsson and Berggren 2002). Of course, non-strategic components are sourced from selected suppliers or sub-contracted manufacturers. However, a reliable forecasting for telecom industry is difficult in such an increasingly deregulated and competitive market place (Fildes and Kumar 2002). It can be very hard to analyse future trends and adapt the capacity levels accordingly for all parties in demand-supply network. Heikkilä (2002) discussed about three demand chain processes as variations of generic demand chain architecture with following features: 1. 2. 3. 4. Supporting the customer’s network building process by sufficiently fast deliveries. Building a product structure to enable decisions on the order-penetration point for a base station according to the customer need. Flexibility in the assembly capacity to meet the market uncertainty. Inventory optimisation within the constraints resulting from the above criteria. Heikkilä (2002) proposed that supply chain improvement should start from the customer end, and the concept of SCM should be changed into demand chain management. Demand chain management understands the need for good customer–supplier relationships and reliable information flows as contributors to high efficiency. Berggren and Bengtsson (2004) have described this horizontal model as superior option, which includes the advantages of speed to market, and revenue. The used horizontal model can facilitate intensive interaction and reduces interorganisational interfaces. It is seen as more responsive and conducive to rapid industrialisation of new products than a vertically sliced model, where volume production is externalised. 2.4.3 Case Ericsson (analysed in 2002–2003) From the study to one of leading players in this industry, Gustafsson and Norrman (2001) reported a detail description about TTC (Time to Customer) flow and TTM (Time to Market) flow in Ericsson’s demand-supply network. An obvious feature of the demand-supply network is to use a common forecast to all parties and call-off as the feedback to form a close loop. Due to its manufacturing mainly outsourced, the information flow interacting with the customers and suppliers is very essential to Ericsson. With such a set-up, it could help the speed of introducing new products and also normal time of its global operation. 56 Ericsson Radio System’s demand-supply chain was proud of its following features (Gustafsson et al., 2001): – – – – Able to track and manage customer orders from receipt to fourth-tier supplier authorization. A response to a customer inquiry about a delivery promise can be determined within 10 seconds based on a current view of value-chain capabilities. The order information is then sent throughout the enterprise, which includes the currently connected 25 first-tier suppliers, 10 second-tier suppliers, one third-tier supplier, and one fourth-tier supplier. The resulting improvements include order lead-time reductions from 15 to 1– 2 days, inventory-turn increases from five to 80, and on-time delivery improvement from 20% to 99.9%. But, Ericsson’s bad situation (at the end of 2003) came back later again. It was mainly because the difficulty was not just to measure one company itself but to synchronize all parties in demand-supply network. Here was the information from internet (Contact no.20) found at that time: – – – – – – – – – – Purchasing amount is near 2/3 of Ericsson’s total costs. Market is unpredictable in challenging to require better forecasts. Product volumes are smaller but the level of complexity is greater. Fire-fighting to get components. Delivery problems in Ericsson and its suppliers. Existing lead-time too long and uncertain forecasts are causing production plan out of synch with actual demand for last-minute changes. Sales organization will add a safety margin and order more than needs resulting greater variations in volumes with long or increasing lead times. Material shortage causes the plant and subcontractors with more stress, money, quality inspection … All putting Ericsson back where it started – long lead times. Customer satisfaction is only about 70 percent. “Santa Claus always delivers on time, but only once a year.” 57 2.4.4 Case Dell Corporation/Lucent Technologies (analysed in 2002– 2003) As one of the most commonly cited success stories of business operational excellence, Dell Corporation represents the out-box-thinking model in computer industry with remarkable achievements (Bilbrey 2000). Karemer et al. (2000) described the exceptional performance was achieved by innovative response to a fundamental competitive factor in the personal computer industry—the value of time. It included Dell’s strategies of direct sales and build-to-order production have proven successful in minimizing inventory and bringing new products to market quickly, enabling it to increase market share and achieve high returns on investment. The detailed features of Dell’s model are stated as follows (Dell Corporation 2003): – – – – – – – – – 58 Dell computers are made with the latest available technology. Materials costs account for about 74% of the revenues. The suppliers are actually located all over the world (such as its Ireland plant with the suppliers 65% in Asia, 25% in Europe and 10 % in USA). Many of the suppliers have plants within 20 minutes of Dell's manufacturing plants. Dell replenishes inventory levels as often as hourly with some vendors; it turns over 52 inventory cycles each year, or once a week. Share information by real time communication with suppliers for rapid order fulfilment (such as 10,000-plus customers every day in USA to change demands unpredictably). Five day average Dell’s inventory in 2001 with target of 2.5 days (the competitors carry 30, 45, or even 90 days' worth) & the third-party logistics providers storing supplier-owned products with ten extra days or one week in HUB near Dell’s factory. Dell Company gets billed after the components leaving supplier’s HUB. The inventory-carrying costs are transferred to its suppliers to decreases the level of inventory on Dell’s balance sheet. Demand-pull rather than supply-push. It never builds a computer without a customer order. Most Dell systems are built in five hours or less. Excess and obsolete inventory (about $21 million / year) between 0.05% and 0.1% of total material costs (the competitors probably 2% to 3% worth of excess and obsolete inventory). 84% of orders are built, customized, and shipped within 8 hours. – – Dell sells 90% product directly to the customer. Market share +170% in 5 years (1997–2002) with profitable growth even in a global economic hard time. As indicated by Karemer et al. (2000), the key to Dell’s success has been its direct sales and build-to-order business model. This model is simple in concept but highly complex in its execution, especially under conditions of rapid growth and change. Dell has continually renewed and extended its business model while striking a balance between control and flexibility. However, the customer feedbacks show the results of its delivery still with big challenges (from web of HardwareCentral accessed in 2003): – – – Good feedback: “The delivery was 12 business days after ordering”, “PC delivery was within 6 business days”. Bad feedback: “Computer was not received more than 3 weeks after ordering”, “Delaying the delivery by 3 weeks”, “Delayed shipment up to 30 days” Customer satisfaction indicator = 2.9/5 (58% - quite low). The challenge aiming for delivery properly on time seems hard in real life to Dell Corporation due to its demand-supply chain sometime not matching with the ideal requirement of responsiveness if bottlenecks does exist in suppliers! As explained by Hoover et al. (2001), a new approach was developed in Lucent Technologies as 3C (capacity, commonality, consumption) materials management system with the following principles: – – – Plan the business (sales) based on capacity. Leverage commonality to reduce inventory. Produce according to consumption (actual demand). Its success key factor is because the 3C approach links sales planning seamlessly to component suppliers using a collaboration process based on ranking maximum usage rates of individual components (Holmström et al. 2002). Hoover et al. (2001) stated the details about the 3C approach: The first step is to define a maximum sales rate of each end product that the factory will support. Second, the factory capacity to produce the end product (units of output per day) is determined. And finally, the component level maximum daily usage rate is defined. 59 Kumar and Meade (2002) described the system allows a manufacturer to be prepared to produce anything they manufacture up to the maximum production capacity for that item at any time. Instead of being driven by a finished goods forecast that is turned into a daily or weekly production schedule, the 3C system is driven by component-level maximum daily usage rates, which are set quarterly or annually. According to further explanation by Hoover et al. (2001), the only thing that is needed daily is to check the on-hand inventory, what is on the way from suppliers, and make sure that the sum is better than the maximum usage rage for the number of days it takes the supplier to replenish. The supplier replenishes to consumption. As a result at the Lucent Technologies Tres Cantos, Spain, plant, the application of 3C led to an increase in fill rate from 75% to 95% (Kopczak et al., 1998), nearly double the industry average. 2.4.5 Case Huawei Technologies (the new competition reality) As indicated by Pisano and Shih (2009), outsourcing manufacturing has left U.S. industry without the means to invent the next generation of high-tech products. Nearly every U.S. brand of laptop and cell phone is not only manufactured but designed in Asia. A new original equipment manufacturer (OEM) should be studied from those rapid growth companies or countries, in which Huawei Technologies can be such a leading Chinese player with remarkable impact in international telecommunications markets. Its aggressive strategy has resulted in the acquisition and merger of several international telecommunication device suppliers (Dickson and Fang, 2008). In 1988, Huawei was establishes in Shenzhen China as sales agent for Hong Kong company producing Private Branch Exchange (PBX) switches. It was ranked as No. 3 in terms of worldwide market share in mobile network equipment in 2008. Then, it became No.2 in global market share of radio access equipment in 2009 (Huawei 2010). According to the view of Nishimura (2008), Huawei should be able to attain its full growth potential as one of the strongest multinational companies. With its strong capabilities in development and design, it can combine with the most advanced technology and parts, meanwhile utilising cheap domestic labour and other resources. As reported by Wu and Zhao (2007), Huawei applied different market entry mode in different markets (different geographical markets and different products markets). It had to enter the developing countries’ market first before it enters 60 developed countries’ market. Similar as its business model in domestic market, the method was to set up the R&D department or register subsidiary companies in developed countries to develop an international market share. Zhang (2009) studied also following reasons why Huawei has been recognized by Business Week as the 3rd World’s Most Influential Company (following after Apply and Google): – – – In order to develop management skills and structure, Huawei invested in collaboration with IBM Consultant. Besides catching up in management, Huawei invested heavily in Research and Development. Averagely each year, at least 10 % of annual sales were put into R&D for developing absorptive capacity. For example, Huawei so far has established 14 R&D centres around the world. Its alliance-based network is characterized by multidiscipline, multi-level, and multi-regions, being embedded in the collaboration with suppliers, customers, universities, and leading players. To support motivating Huawei people, it adopted a bonus and stock-option system to reward good technology (Lau et al. 2002). As the observation of Liu (2005), Huawei can thus grow faster based on a market-oriented innovation strategy. In contrast with current No. 1 leader in same industry, the battlefield of leagility in demand-supply operation can be no longer to protect its leading position or even ensure its better survival. The key is because Huawei has more relative advantages as the compensation to win the battle: lower break-even and lower revenue expectation in cost-profit analysis. Same competitive effect could be achieved easily if product value is as good as other competitors. It will become an unstoppable journey for Huawei to re-write the history if other leading companies would ignore the radical innovation as a new must nowadays. Similar in many circumstances, the No. 1 leader should bring its value differentiation to the customers or keep its unique advantage in the industry. 2.4.6 Other studies oriented by value differentiation or unique advantage Kim and Mauborgne (2005) indicated that head-to-head competition results in nothing but a bloody red ocean as rivals fight over shrinking profits. Similar as their proposal of blue ocean strategy focused on creating unknown market space, value differentiation can have a same effect in any period of the lifecycle for 61 industrial innovation by making the competition irrelevant, as well as leading the trend in Information and Communication Technologies (ICTs). Industrial lifecycle analysis as a tool According to the research of Gottfredson et al. (2008), experience curves can be used to show how much industry prices and company costs have fallen each time the industry’s cumulative experience (total units produced or services delivered) has doubled. It is possible to allow the companies to predict how much inflation adjusted prices and costs are likely to decline in the future. Tan and Mathews (2010) made a similar research how to utilise the view of business cycle, industry / technology lifecycle, and industry cycle for the companies to win in the competition. They also indicated that cyclical behaviour in the economic system is one of the great themes in economic forecasting and innovation study. Firms such as Intel have made a major discovery in their ability to profit from industry cyclical downturns. Intel has consistently acted as a ‘counter-cyclical investor’ over the past two industry cycle downturns. These business successes now call for complementary innovations in the fields of business policy and strategy to generalize the findings and account for their success in terms of the field's theoretical frameworks. Tan and Mathews (2010) executed the time series analysis in the time domain and in the frequency domain. It was not only to understand more precisely the cyclical movement of the industry, but also new insights about potential sources of the cyclicality and the implications of industry cycles to innovation strategies and behaviour in the industry. Business growth by blue ocean strategy thinking The Blue Ocean Strategy was introduced by W. C. Kim and R Mauborgne with following six principles (Kim and Mauborgne 2005): 1. 2. 3. 4. 5. 6. 62 Reconstruct Market Boundaries Focus on the Big Picture, not the Numbers Reach beyond existing demand Get the Strategic Sequence Right Overcome Key Organizational Hurdles Build Execution in the Strategy. Kim and Mauborgne (2005) emphasise the strategic move is the right unit of analysis for explaining the root of profitable growth, and not the company or the industry. As explained by Kim and Mauborgne (2005), the strategic move is the set of managerial actions and decisions involved in making a major marketcreating business offering. The definition of the red or blue ocean can be seen as follows: – – In the red oceans, industry boundaries are defined and accepted, and the competitive rules of the game are known. As the market space gets more crowded, prospects for profits and growth are reduced. Products become commodities, and cut-throat competition turns the red ocean bloody. Blue oceans, in contrast, are defined by untapped market space, demand creation, and the opportunity for highly profitable growth. Although some blue oceans are created well beyond existing industry boundaries, most are created from within red oceans by expanding existing industry boundaries. In blue oceans, competition is irrelevant because the rules of the game are waiting to be set. From the view of Kim and Mauborgne (2005), the market universe has never been constant; rather, blue oceans have continuously been created over time. To focus on the red ocean is therefore to accept the key constraining factors of competition— limited market space and the need to beat the enemy in order to succeed. However, companies need to go beyond competing in established industries. To seize new profit and growth opportunities, they also need to create blue oceans. Leading industrial innovation as Apple In order to create value differentiation via platform leadership similar as Intel, Ghazawneh (2010) emphasised the Apple’s iPhone as another one of the projects adopting the open innovation paradigm since it does not only depend on internal but external and distributed sources for the developments of its applications and services. The adoption of this open innovation model is mainly fulfilled by the implementation of a product platform that enables almost anyone to innovate upon its evolving system in an interdependent way. From the view of Braithwaite (2007), the benefit of using the iTunes platform is that Apple can maintain a direct and ongoing relationship with customers not feasible for other handset manufacturers. Apple uses the iTunes ecosystem as the 63 means for upgrading the phone’s capabilities through software upgrades as well as an e-commerce web site for the sale of music and video content. Braithwaite (2007) argued the revolutionary “user interface” and enhanced “user experience” as the function of new technology as well as software designed to simplify the operations of the phone. The iPhone proves to be as revolutionary as widely predicted other cell phone manufacturers would need to respond. As reported by Mohr et al. (2010), Apple does product design of all its products in-house in California with its own designers and engineers. Design is a core, proprietary skill set for Apple which gives it competitive advantage in the marketplace. For example, Rixner (2007) indicated the Apple’s iPod wildly successful relative to MP3 offerings is a business design that provides a complete digital music experience. While its competitors pursued either a device approach focused on MP3 players or a music-store approach focused on downloadable songs, Apple provided an integrated offer of hardware (iPod), software (iTunes music library), and content (iTunes music store). For Apple’s iPod, its manufacturing and even core software are outsourced (Lo 2008). The subcontracting manufacturer likes to work with Apple more than with other firms because “the iPod’s popularity ensures that orders keep coming in” largely due to customers’ loyalty to Apple’s notable R&D capabilities. As mentioned by Spink and Krudewagen (2009), Apple sells a $299 iPod (designed in California, assembled in China), for instance, it makes an $80 profit, while the Chinese assembly plant makes $4. Known from the analysis of Mulrennan (2010), Apple’s share price rise from $9.43 to $203.00 per share in the following eight years after the iPod was launched in 2001. By late 2009, the unique position that the iPod held within the market was validated by the announcement that 225 million units had been sold worldwide. The iPod currently holds a market share of 78% among digital media players. By contrast, Copeland and Shapiro (2010) mentioned that Apple is slower at technological adoption than the other PC (Personal Computer) manufacturers. PC manufacturers are introducing significantly more products with shorter life spans relative to Apple. Apple keeps its computers on the market about twice as long as the other PC manufacturers. Apple's prices fall relatively slowly and less extensively than do the prices of the PC manufacturers. Prater et al. (2001) also mentioned Apple's supply chain was not complex, the uncertainty involved in sea transportation made Apple's supply chain vulnerable. At the same time, Apple's supply chain agility was low because of the low speed and flexibility with which product could be brought to market. 64 Obviously, Apple has learned the tricks from its practices in PC industry and brought open innovation into smart-phone industry better than other competitors (such as Nokia or RIM’s Blackberry acting still so similar as Apple’s vertical platform in PC industry), as well as keeping some advantages on product lifecycle control. Sako (2009) claimed Apple Computer was not successful as an integrated PC firm, but emerged successful as Apple Inc. with its iPod and iPhone, when it bundled entertainment and mobile telephony. Competing by new product or business design (case RIM’s Smartphone)? In comparison with unique advantage of Apple in a same market, it is interesting to check other competitors such as RIM (Research In Motion) with its BlackBerry product as an example. As Hahn and Singer (2009) described, it was Research in Motion (RIM) and not Nokia that developed the smart-phone segment. Although RIM’s BlackBerry was not the first wireless device with reliable e-mail access, it popularized mobile e-mail among business professionals because of its integration with Microsoft Exchange servers and strong encryption. Through the introduction of the iconic BlackBerry, RIM has proven itself to be a leader in the handset industry. Expectations were high in November 2008 when RIM introduced a touch-screen smart-phone, the BlackBerry Storm, to compete with the iPhone. But the Storm has proven to be somewhat of a disappointment. However, innovation is a continuous process. Hahn and Singer (2009) believed that BlackBerry will likely learn from its successes and failures. Given the pace of technology development in the mobile handset market, the iPhone’s position is hardly guaranteed. A new device could render the iPhone obsolete quickly. As indicated by Rixner (2007), the key to each of these successes (such as Intel, Apple, or RIM) goes far beyond the company’s products and lies in the business designs surrounding their technologies. If the Apple’s iPhone can be seen as a strategic move to the blue ocean of Smartphone market, what is the next big thing to beat it or re-create a new successful story by another unique way? “Shanzhai” to be a bad copycat manner or as an open innovation As Lee et al. (2010) explained, “Shanzhai” is a Chinese term referring to companies that operate outside traditional rules and practices. One product that has been particularly impacted by Shanzhai manufacturers is the mobile phone. 65 According to the study of Li (2010), the first Shanzhai mobile phone appeared in 2004. They were fake goods of famous brands such as Nokia or Samsung. With very cheap chips in bad quality, they were not accepted by consumers. Since 2006, the MTK mobile phone chip was developed by MediaTek (headquartered in Taiwan). Due to more integration of multimedia features and lower prices, the MTK chip was utilized by mobile phone companies and mobile phone design companies in a wide range. Lee et al. (2010) reported an impressive growth status of Shanzhai phones. In 2008, more than 750 million cell phones were produced in China. A significant portion (20 percent, or about 150 million units) of these phones were produced by Shanzhai companies. These companies had rapidly taken a significant share (about 10 percent) of the worldwide market. As the comparison from International Data Corporation (IDC) about market share of main business players in the fourth quarter of 2008, Nokia is 39.1%, Samsung is 18.3%, LG is 8.9%, Sony Ericsson is 8.4% and Motorola is 6.6%. Li (2010) stated Shanzhai mobile phones can attract many customers who focused on the cost performance of products. There are many advantages to Shanzhai products: no 17 percent added-value tax, no network license fee, no sales tax, and no 3–4 Euros checking fee to the government. Shanzhai running costs are further minimized by the absence of marketing and after-sales service. However, sales were not only high in the domestic mobile phone market; its export volume was considerable as well, including India, Brazil, Russia, and even the European market. Wu and Zhang (2009) indicated “Shanzhai” is actually not simply to be a copycat, which was thought as a bad manner in the competition with its threat often ignored by mainstream companies. In fact, "Shanzhai mobile phone" can also offer numerous innovative functions such as emergency light, telephoto lens and even counterfeit currency detector. "Shanzhai mobile phone " represents not only product innovations, but also business model innovations. Lee et al. (2010) emphasised this phenomenal growth of “Shanzhai” was primarily due to nonconventional approaches to the global market in market positioning, rapid product development, and tightly coupled, responsive and efficient supply chain management. Known from the explanation of Wu and Zhang (2009), "Shanzhai mobile phone" companies needn’t to invest on R&D because of using chips from Taiwanese company MTK as “turn-key” solution also with SDK (software development toolkit) and application software ready. Besides, there are thousands of design houses in Shenzhen, the capital of "Shanzhai mobile phone" providing total solution of mobile phone design and thousands of dealers providing all kinds 66 of components like supermarkets. Such an open innovation way applied in manufacturing industry is so well combined with the innovator’s strategy when its disruptive effect in market competition is often noticed too late by those leading companies. As the view of Li (2010), in the low-end consumption market in China, the foreign products tend to be over-designed. Thus, a large number of domestic supplier and demands emerge which results in a heated competition on prices. Shanzhai manufacturers start to produce recreation products through imitation, and then undergo the rapid change from imitation to innovation. The Shanzhai industrial development began with imitation, which can be traced back to the examples in Korea (such as Samsung) and Japan (such as Toyota). They are all as leading companies nowadays in the industry - not anymore just based on offering cheaper cost or lower-end product. As Quad-Band-Phones.com (‘QBP’) to be another example (accessed in December 2010), it can offer some really cool non brand mobile phones that are for sale at ridiculously low prices, which is owned by US citizen even the company is located in China. Obviously, “Shanzhai” has been becoming more neutral with many complex effects as a business model for new comers in the industries. Lee et al. (2010) argued it would be unfair and inaccurate to classify all Shanzhai mobile phones as “pirated” or “illegal” products. Whether legal or illegal, whether they imitate or innovate, they have demonstrated amazing flexibility and tenacity. With the determination, a company can be successfully transitioned from a Shanzhai culture to become a major mainstream force in the industry. “Shanzhai” just indicates that it has been gradually organized and enlarged in an unauthorized fashion during company’s earlier life. The innovation can be as one of the driving forces of Shanzhai manufacturer’s competitive strategy. Understanding the product development process and supply chains used by Shanzhai mobile phone makers may stimulate new ideas for design and manufacturing by mainstream companies. The complexity of high-tech innovation studied by case Nokia Naturally, the review of mobile phone industry should be continued with case Nokia as the next step after a wider study was mostly concentrated on those with business model impacts (such as Apple and Shanzhai). Although many of new challenges have turned the competition as a red bloody ocean, how Nokia can remain its No. 1 leading position better than other competitions? As analysed by 67 Chang and Horng (2010), Nokia operates successfully not only on high-end market but also on low-end mobile phones. For example, Nokia ranked number one in China’s branding market in 2008. Its high-quality low-price business strategies include many creative changes actually as a new business model in the past if comparing to other competitors: – – – – Manufacturing strategy on integrating supply chain Technology strategy on establishing R&D centres in China Channelling strategy of consumers in small towns and villages Pricing strategy in response to low-end market. The capability to bring radical changes in the innovation based on people spirit of motivation to win business growth is the key of Nokia success in the past or in the future, no matter facing which traditional competitors as Motorola or Samsung lack of such impacts. However, Nielsen and Hanseth (2010) compared Nokia with the iPhone approach from a free and open innovation perspective. Apple has at the same time shown as a fairly successful model in serving users and innovators. For example, buying an iPhone is also buying into a value network where new services can easily be bought and installed from an application store (App Store). Even if the application store has been criticized for challenging some of the core values of the Internet since all applications have to be signed by Apple, this has really made a difference for the users, and other mobile phone manufacturers are following (like Nokia’s Ovi). As reported by Halonen et al. (2010), Nokia hasn’t been as successful as Apple in building its application store. Nokia launched Ovi Store in May 2009, almost one year behind Apple. During the first three months, Ovi Store had only 10 million downloads; whereas Apple App Store had 100 million downloads during the first two months only. The weakness of open innovation comparing with Apple makes Nokia to introduce radical changes so slowly, which used to be Nokia strength but now as a sign of dangerous losses in high-end market. Similar as Nokia Siemens Networks in mobile infrastructure industry struggling with Ericsson and Huawei, another threat to Nokia can come from lowend competition, in which the advantages of Shanzhai can be mostly utilised by a much more powerful competitor. As an example reported by Foster (2010), Huawei Technologies shot past Alcatel-Lucent and Nokia Siemens in 2009 to become the world's No. 2 telecom-equipment provider, powered by quality and product upgrades on top of its long-standing low prices. For leading companies (as Nokia or Ericsson), the winner at the end of battle in red ocean will be not 68 determined by lean or agile improvement efforts, but the No. 1 position in the industry by the capability to create radical innovation and bring blue ocean opportunities. If the urgency is misplaced somewhere else, same wrong focus can affect Nokia’s success in mobile phone industry again even Apple is just a new comer at high-end market now. From the view of Braithwaite (2007), the iPhone has the capacity to impact all players in the cell phone network: consumers, rival wireless carriers, Apple’s wireless partner, and rival handset manufacturers. Issues of usability and enhanced ‘user’ experience are also likely to influence rivals’ phone operating systems software. From the perspective of the ‘user experience’, the multi-touch screen and enhanced functionality, the iPhone introduces a radically innovative and simplified user interface. Due to the complexity of high-tech business, Nokia should be alerted and focused how to regain the competitive advantage beyond Apple and lead new radical innovation in the industry. Nokia is still as the No. 1 leader for market share even now also in smart-phone field. Great opportunity to win the competition exists if Nokia will not repeat the path of Nokia Siemens Networks and keep top priority to its right battlefield. Outcome of value differentiation studies All in all, the innovation for value differentiation should be emphasised not only as the element in lean or agile improvements, but also more importantly as its own portion in the research. The difficulty of radical innovation must be not underestimated in business with the risk ahead. Besides, same thought can be also applied to optimise company’s manufacturing operation, as well as new product industrialisation in change implementation research. It should not be forgotten about great opportunities in leading companies how to synchronise with industry lifecycles – always aiming for value differentiation or unique advantage by the innovation! 2.5 Theory synthesis There is a growing concern to emphasise global manufacturing in a strategydriven way. The above review of existing theories indicates that there is a gap requiring further research. For example, there have been a number of valuable studies emphasising lean and agile in global manufacturing, separately as well as their combination. However, business reality is much more complex similar as a 69 dynamic world with three-dimensions, which can not be looked sufficiently as a static picture of two-dimensions. Lean and agile strategies have not been studied well in an environment of global manufacturing where the third dimension is innovativeness: interacting dramatically with each other influencing product change. Such an environment can be shown as following Figure 4: Fig. 4. Thinking three dimensions for product change. Theory findings that act as a basis for this research: 1. Strategy is not static but needs adapt swiftly In modern high tech business, the competitive situation is turbulent, resulting in pressures for changing manufacturing strategy even separately for different products or product groups. Previously it was thought the strategy can be generated and maintained for years, even the shortest update period could be as long as a half year. 2. Lean and agile aspects are both needed in manufacturing strategy Traditionally, literature indicates that a company must make a choice between lean or agile. This can be misleading and result in an unbalanced situation in modern high tech business. On the contrary, lean and agile ingredients should be embedded. The literature uses the term leagility to describe the simultaneous combination of these two. Only in few extreme cases, extreme choice along lean-agile axis is sensible. 3. 70 Rapid product change as a driver for manufacturing strategy Rapid product change is not an element of lean or agile, but an element of its own influencing the choice of strategy. It is not enough to work on the two dimensions of lean and agile, but rather to introduce a third dimension of rapid product change. Rapid product change can be seen as a part or an example of the innovation. Same principle can be applied to any of other similar innovative changes (such as new breakthrough technologies, disruptive business model, or even revolutionary “user interface” and enhanced “user experience” as iPhone). 4. Demand-supply chain as a competitive factor Industrial competition in this current period of globalisation is becoming a battle between demand-supply networks, not just single companies. However, it is a great challenge to find ways to tackle operational bottlenecks, and to overcome organisational boundaries, both within the company and between its suppliers. 5. Accept inaccurate forecasts As seen from Ericsson’s model, it is challenging to ensure the reliability of a company’s operational performance. A company has to make a choice in an environment with uncertainties on whether to accept inaccurate forecasts or seek for other ways to overcome this problem. 6. Emphasise radical innovation always in new high-tech business reality With many high-tech companies analysed in the literature review, it shows the innovation should be emphasised as an independent strategy, not just as a component in the legality thinking. For leading companies or new comers aimed for the No. 1 position in industry, radical innovation needs to be as a must to achieve or keep winning in the competition. Although it increases the complexity to describe, business reality should be thought as a 3D world no matter how easier from lean or agile view only. 71 72 3 Results of the three action research cycles Selected case company is a significant global actor in the mobile infrastructure industry. The research environment can be described in line with product change implementation, which is mostly focused how to optimise manufacturing operation and global demand-supply chain. There are many engineering changes during a product’s lifetime without a period when new and old versions overlap as execution principle. Component changes in products often happen at any time adding extra complexity for manufacturing besides original demand uncertainty. Product change management scope includes planning & informing all the sites (own primary and substitute factories, as well as its subcontract manufacturing partners), and cooperation between these sites, collecting results, analysing and making conclusions. The research can be characterised to simultaneously include aspects of worldwide business impact, rapid innovative pace, and high volume in operation. The case company combines push-based supply chain and pull-based demand chain together as a mix to synchronise production and delivery of all product parts with big lead-time gaps (mostly unavoidable from material supply). Pull principle is applied at internal steps of the production, as well as the delivery end. The product flow is in FIFO (First-In-First-Out) mode at each step of manufacturing, meant that not a same product is initiated, moved and delivered in the operation to fulfil the demand at customer end. With it, short lead time can be achieved in production to balance the pace and the flow of manufacturing operations. Push principle has to apply for the supply end and keep the inventories to absorb the impact of inaccurate forecast. Demand-supply network has to thus have enough tolerance to avoid undesirable conditions, such as production stop due to lack of key components. Observing in such various ways, the effects of different theories could be seen “virtually”, e.g. MTO (Make-ToOrder), ATO (Assemble-To-Order), DTO (Deliver-To-Order), and even MTS (Make-To-Stock). Obviously, the challenge is caused from component logistics in the electronics industry, which is extremely complex due to a vast number of required components with long production or delivery lead-times. For example, the leadtimes may differ by days (such as VMI – Vendor Managed Inventory or two-bin system), weeks (such as PWB and own specific integrated circuits), or even months due to sea transportation (such as the cabinet). This creates bottlenecks or big inventories in the supply network due to those time variances and real demand 73 often not matching with earlier forecasts. When the gap of material supply occurred by the changes of product customisation or delivery requirements, demand-supply network balance would be easily destroyed in a fire-fighting manner to take time for its recovery. It affects also the speed of product development in its change implementation phase. For example in case company, the product versions were different more than one year at some manufacturing sites before the research was launched. The case company had to find an alternative way to survive better in the competition as everyone in the industry suffered by those same challenges. The bottom-line was to deliver products to customers’ requirements (especially having the changes of delivery amount or product configuration) at a high speed, without means to develop efficient forecasting processes to manage demand uncertainty. Whenever the volume of pull at delivery side was larger than the amount of push at supply side, production had to be stopped due to missing components. Such a demand-supply network problematic area could not be simply solved by the outsourcing of manufacturing operation or VMI (Vendor Managed Inventory) like supply, which would just mean to move the headaches to other business partners. Faster transfer of demand information or a more reactive planning was also not enough to save manufacturing companies as a physical process is inflexible in responding to frequent plan changes in normal operation. When product changes added on this, demand-supply planning practices became even more fragmented and frustrated. There were no existing solutions available, academic or industrial, at the time. As a competitive advantage of case company, both product development and manufacturing functions are well combined. For example, it includes prototype fabrication and pilot capability (zero-series production), departments for product industrialisation (where the research existed) and high volume production. The pilot of zero-series production uses same BOM (Bill-Of-Material) as the last prototype-run but now with a bigger volume similar as normal production lot size. If the result is failed, product development should be returned to prototype phase to solve the problems found in the pilot and then back to zero-series production in the future. If the result is successful, it is the approval for product development entering new phase of change implementation. There are no other more-series of the pilot (or normal production) needed as the way of one single gate to approve product development before volume production phase. Volume production can be also called flow production as manufacturing the products in a repetitive manner. 74 Research Cycle 1 included the case company aiming all of its actions to minimising costs, which was as a strategy of cost effectiveness. Minimising inventory and scrapping costs required its effect into the whole demand-supply chain. In research Cycle 2 the case company aimed at diminishing order delivery period. Research Cycle 3 concentrated on shortening product change period. The case company executed a strategy of innovativeness making product changes as fast as possible. During research cycles, every change case was recorded using change notes (CN). Change notes compare the old and the new product versions, indicating all changes in used components. CN also indicated the expectation when the changes will be conducted. Site specific implementation reports were utilised to record changes, the implementation time and scrapping costs. Implementation report described all the results from different sites. Both, implementation reports and change notes were stored into a database. There were over one hundred product change cases available within the company at the time of research. The researcher selected three cases out of all product changes, one for each cycle. The cases were important for business and there was a significant change in the product. Process improvements were made based on the three selected product change cases individually. After the process improvements, it was checked whether the targets set for that particular cycle was reached or not. 3.1 Research Cycle 1 – minimising costs The action research was initiated by problem-solving in a tough situation: The forecast was so inaccurate. The lead time of material supply varied from a few hours to several months. When demand is this dynamic, it is not possible to react quickly, often delaying of the implementation of product changes or causing a production stop at some manufacturing sites. The old way of planning based on the forecast cannot work well anymore when faced with such business uncertainty. Although the learning journey actually started in a fire-fighting way, a systematic approach was planned. In the research, the focus was on the challenges of seeking a new solution for surviving product changes. Even with the urgency of problem solving, it was expected to be a part of the long-term development (as the above pre-step) of global manufacturing’s adaptation to a dynamic business environment. With product change implementation and action research combined together, it can 75 ensure the quality in a systematic way with all strategic and operational factors taken into account. 3.1.1 Pre-Step Before the first cycle of action research was started, several cases of large product changes were actually already done. The implementation of product change can be seen as a black-box in context analysis. Its interactions with external factors were very similar in all cases. It was already introduced in an earlier chapter when explaining why product changes can be used for operational improvement – both with the same variants affecting inputs or outputs. There were many ideas from previous lessons for further development. Such a procedure was repeatedly utilised also in later research cycles because it can act in a target-driven manner to make the outcome with a better quality. Here was the review of research conditions in the company as a context analysis: – – – – – – – – – A worldwide economic hardship to most of the global companies at that time naturally with cost-effectiveness as the strategy. Dynamic business environment with forecast accuracy extremely poor. The product or material variation has been controlled and reduced continuously well by company-wide process of “Design for Excellence”. Lead time of own production has been shortened to a good level (not as a main factor). Inventory should be kept as small as possible to be an operational condition. To avoid scraping cost in material supply as a big issue to product change management. Earlier planning to reduce inventory for implementation of product changes not working good enough. The production stop caused by actual demand and supply do not match each other due to inaccurate forecast and material lead time gaps. It had to seek new solutions in the survival for product delivery and engineering changes. As expecting synchronisation for survival, it was aimed to accept inaccurate forecast / dynamic demand, introduce a timely manner in balancing supply operation, and even synchronise invisible liability. From a Change Notice 76 database of product change results, the implementation time and scraping cost were used as two measuring indicators comparable with all research cycles. The thoughts from this round can be brought to the next cycle as its input, i.e., as the pre-step of that cycle. It can thus make the cycle-by-cycle progress a continuous learning journey. 3.1.2 Diagnosis The diagnosis was done in a more practical sense to detail new improvement ideas, which can be used further for action planning in the next step. Such a way of analysis (at pre-step in general and at this step in practice) will be used for every cycle of action research. It can thus ensure the quality of action research, as well as product change implementation itself. The outcome of diagnosing in this Cycle 1 can be shown as follows: – – – Acceptance of inaccurate forecasts Clock-speed to be weekly as the material in-flow for demand-supply network Invisible liability in material supply – controllable or not? All of the above elements acted as the targets or baselines for planning the actions in this cycle and to prepare for the next step. 3.1.3 Planning The breakthrough was intended in action research Cycle 1 to achieve the following two solutions of weekly clock-speed and dynamic cut-off window. Weekly Clock-speed With PWB (Printed Wiring Board) purchasing as an example, a new attempt was made to change the material order for small and frequent deliveries (such as on a weekly basis) as the improvement in demand-supply operation. It will replace the way originally with a big order just according to the message from MRP system by inventory level control. The idea was to consider the time added to the amount as both factors for forming a material supply flow. The actions were planned in a procedure shown as following Figure 5: 77 Fig. 5. A series of actions planned for the trial of weekly purchasing. Even with the difficulties at the beginning, it was gradually becoming more and more understandable by the buyers in practice. It was found that it was better to consider not only the amount but also the time for all components in the supply. It was needed to repeatedly equalise the available amount of each component according to the product’s BOM (Bill Of Material) at every moment in line with the weekly updating of the forecast or demand. Such a dynamic balance with the postponement of manufacturing can be the key of true synchronisation. No matter how the forecast would be right or wrong, it actually just changed the usage time due to dynamic demand. The adaptive focus was clearly moved to the delivery and the usage – even the supply operation was still in a business uncertain situation. Although material sourcing department had difficulties understanding the idea at first, the attempt was finally succeed in moving the focus to synchronisation for demand-supply as a weekly pace! Dynamic Cut-off Window The material liability to the company was caused by its responsible forecast to other parties in the supply chain on a pre-agreed scale (such as weeks or months). It is the duty of the platform company to take the liability amount for its 78 consumption or pay for it as scraping cost. It can thus impact the implementation of product change or the scraping cost dramatically as an unbalanced component amount. Normally, the material liability amount cannot be seen directly in MRP systems of any company when approaching the version changeover date. It was because the forecast can already be changed so greatly month by month. It should be negotiated case by case between the companies at each tier of the demandsupply chain. The design of a creative solution for this challenge can be stated as a fourstep process called dynamic cut-off window in Figure 6: Fig. 6. Action plan for a dynamic cut-off window. It was needed to set a product version change date in that MRP system that was longer than the longest lead-time of any component in the current and new BOM. This date can be changed in a weekly management meeting. The idea was to keep a status just not to stop the ordering of old material but also not to start the purchasing of new material during the trial period of a new product version. The moving of version changeover date was done weekly to ensure such a time-window (one week more than the longest lead-time of key component – the change driver) just working at the “risky-edge” to cut off the forecast of current version material. The attempt was meant to avoid the liability and not harm the current supply operation. If the product trial period might take one month, the forecast of that amount globally could be eliminated without harmful side-effects. 79 It can thus reduce the maximum amount of material liability. Besides, it was always planned to keep a difference of implementation time (such as one month) between the platform site and the lean sites of the company. It can help the situation further to consume the exceeding amount of the material by those lean sites. However, there were the disadvantages of confusing the whole demandsupply network because of the trouble in moving the version change date weekly in MRP. It was a type of manual planning – also a lack of effective communication in advance. (Can it be improved in a process well-defined way also with better IT supports if possible?) The forecast proved its power by penetrating everywhere in the enterprise eco-system in a forced manner. Obviously, as its benefit at the end, a good result was to cut all liabilities by that one-month period before the change approval to a new product version. 3.1.4 Taking action The actions were taken using the process shown in the following Figure 7. Fig. 7. Actions in action research Cycle 1. The figure describes the approval process how to promote product development phase ready for entering volume production, as well as some improvement 80 happened in that research cycle. The BOM (Bill-Of-Material) in the last prototype run had to be tested by a bigger amount of volume manufacturing called “0 series” run. It was mainly to prove the last proto BOM suitable as a product change without a quality problem in volume production. The BOM of this version can be utilised as “Planning BOM” for some earlier activities in product change management, such as comparing with current BOM to identify “Product Change Driver” (the component with the longest lead time or as the most expensive one). If the result of the “0 series” run was OK, it can mean product change approval ready for buying new material and stopping the purchase of old material – a version change to the product. If the result of the “0 series” run is not OK, it could mean another prototype run for big modification change and another 0 series after it (or to run it directly again for small modifications). The implementation time of product change and scraping cost (caused by extra material left in the company not possible for its consumption or transfer to other sites) were the targets to control a change properly implemented. 3.1.5 Evaluation The result of change implementation time with very low scrapping cost can be seen in the following Figure 8 for platform site (Site 1 as primary site of case company) and another main lean site (Site 2 as one of substitutive sites). Fig. 8. Implementation result of action research Cycle 1 from change notice database. 81 The analysis and explanation of the implementation result The implementation result can be analysed also with some of more explanations. A platform site (site 1) and another main lean site (site 2) of global manufacturing are included in the figure of the implementation result. The target of implementation time for site 1 was determined because the lead time for PWB (Printed Wiring Board) was six weeks, so using seven weeks as a reasonable shortest time due to the spare amount of safety inventory at a very limited level still required at any time. It was planned to the change implementation better always with one month difference between both sites to reduce the risk. The target of the second site for implementation of product change can thus be 11 weeks (the shortest possible and allowable time). It is also aiming to the implementation at the first site as soon as possible with reasonable amount of exceeding material transferable to the second site to be consumed there. All sites are independent of the implementation (such as getting cost saving from product change due to cheaper material in new BOM) and scraping cost. Due to material transfer from the first site to the second site, the consumption at the second site can generally take about 1–1.5 months depending on their capacity ratio. In this case, both sites had done the implementation with a “normal” speed but without the liability amount of old material for further consumption. Product 1 & 2 are main products with a larger volume normally also with common components that product 3 & 4. For old material consumption, it can take a longer time if only using product 3 & 4 in the manufacturing. New findings in this action research cycle Most of the new improvements had been achieved as expectation. They can be utilised repeatedly in other cases of product changes or next cycle of action research. 1. 2. 82 Effectiveness strategy as one extreme of the choices – The strategy of costeffectiveness means material supply is synchronised at a balance point of minimum inventory. (This also affects implementation time, producing a shorter one but with less tolerance – production stop occurred a few times). Acceptance of inaccurate forecasts – It can be possible if the equalisation of old material was focused. 3. 4. 5. Clock-speed to be weekly base – It helped the material supply in a dynamic situation with the focus on the factors of amount as well as time. Dynamic cut-off window showing IT importance – It was done via MRP system to the whole supply chain, but manual changes were weekly adjustments. Possible to control invisible liability of old material – The result demonstrated a large improvement with the dynamic cut-off window, but when done manually problems appeared. Practical contributions The contributions can be stated as follows so as to show practical business value: 1. 2. 3. 4. 5. Keeping a big picture view of the strategy in order to ensure that activities at the operational level match the target of cost-effectiveness and that the benefits are spread to all in the supply chain. Moving the focus away from inaccurate forecasts to the equalisation of material supply in a timely way as the core of synchronisation in global manufacturing. Implementing the supply balance on a weekly basis with factors of amount as well as time clearly as a synchronisation trial. Doing dynamic cut-off window as an example of synchronisation – as manual changes in IT systems to achieve operational adjustment interactively with global manufacturing. Showing synchronisation in demand-supply operation is capable of controlling old material liability. Comparing to the targets of product change management, Table 3 summarises the findings of the research Cycle 1: Table 3. Targets & findings of the Cycle 1. Strategic Targets in Product Change Management The Implications of New Findings from the Results Executing corporate strategy: cost effectiveness. Acceptance of inaccurate forecasts Trial for operating from inventory level to clock- Effectiveness strategy as one extreme of the choices speed control Clock-speed to be weekly base Balancing between fast implementation & lower Dynamic cut-off window showing IT importance scraping cost in whole demand-supply chain Possible for controlling invisible liability 83 3.2 Research Cycle 2 - shortening order delivery time The action research continued in another cycle using a new product change case in Figure 9. The economic hard time was almost over and ready for agile thinking as a new improvement focus. It was the period with a different strategy so called “responsiveness” to the company and its demand-supply network. In this cycle, the effort was targeted to a new operational model of faster responsiveness in product delivery. It was a great opportunity to see how this strategy in an opposite way to affect business operation. Besides, the argument of what as good or bad happened in this case provided a valuable lesson that served to enhance the understanding of synchronisation. Fig. 9. The action research for new operational model. With thinking for a new operational model, some small modifications were made also to the action research process, though most of the steps were still similar. It could be a great opportunity during a better economic period to make more radical changes. 3.2.1 Pre-Step Because the research framework was generally explained in earlier parts, the information in this cycle can be stated immediately with the topics. As a targetdriven way, it was determined to verify existing knowledge such as ESP and 84 Three Dimensional Concurrent Engineering (3D CE) – Product, Process, and Supply Chain. The Review of Research Conditions in Context Analysis: – – – – – Worldwide economy was recovering with a different situation to the most of global companies possible for using a new strategy. It was acceptable to deal with inaccurate forecast in business operation as a given condition. Responsiveness as the strategy even to its extreme – keep the material inventory level according to maximum production capacity. Weekly pace to demand-supply operation as a default. Planning BOM with dynamic cut-off window done in a manual way was applied again at the beginning but cancelled later. It was mainly due to its trouble and the confusion it caused others in the co-operation. 3.2.2 Diagnosis It was noticed that the forecast function did not matter so much in the situation if aiming to reserve spare capacity fully. It was a case after diagnosing and planning for a trial of new strategy to its extreme – to keep inventory required by maximum production capacity without the forecast needed in MRP system. The outcome of diagnosing in this Cycle 1 can be shown as follows: Key Points of New Improvement Thoughts – – The extreme responsiveness as the strategy (jut according to maximum production capacity in operation without forecast) Concurrent engineering with R&D by 2 phase approval for product change started earlier in demand-supply operation It was a radical change to the forecast by a new thinking: without it at all in the operation because it was wrong anyway the most of time. Synchronisation between both processes of product creation and demand fulfilment was another key issue in the trial with two-phase approval for product change. If the first approval can pick an earlier status in product change process, it was expected to see the benefit from applying 3D CE (Three Dimensional – Product, Process, and 85 Supply Chain – Concurrent Engineering). Such extra time in the supply chain will make product change implementation faster. 3.2.3 Planning Planning work was done for the following two issues: New Trial for an Extreme of Responsiveness Strategy The responsiveness as the strategy means the balance point of synchronisation for material supply was moved to a reasonably high level in the inventory. This was based on a new operational model consisting of the following principles: – – – – Systematic Concept 1: Real-Demand-Pull for whole demand-supply chain Systematic Concept 2: Immediate delivery without extra cost at each tier Systematic Concept 3: “Financial Zero Inventory” by “Cash-To-Cash Time” Systematic Concept 4: Profitable by the volume and speed from the innovation. During the trial, it should keep checking any of bad influences due to no forecast to both normal manufacturing operation and product change implementation. The procedure how the actions were planned is shown in following Figure 10. Fig. 10. Action plan for the extreme of responsiveness strategy. 86 The four principles of the new operational model were verified if they can work without the forecast. It depended just on demand pull at each tier of the supply chain with the inventory as the buffer to compensate lead-time gaps. In this way, it was targeted for a quick delivery everywhere in the operation of demand-supply. Concurrent Engineering by Two-Phase Approval The principles of two-phase approval in Figure 11 are simple. Aiming for earlier implementation with controllable risks, it needs the planning of new and old material supply to be quantified to a detailed level. If product version change happened before the second approval, it can cause the confusion and disaster in global manufacturing operation. Fig. 11. Action plan for two-phase approval. As a process modification to try 3D CE, the attempt at two-phase approval should be communicated to all relevant personnel in product creation and demand fulfilment. The time comparison and the amount of material supply should be all based on the detail of quantified information. 87 If affecting as an operation to multiple sites, the risk management should also be in place. Of course, its implementation can be adjusted or stopped whenever to find any issue out of the control. After the result is made available by the trial, it should be analysed to see if it can be a solution for long-term usage or not. 3.2.4 Taking action The actions were taken with the process shown in the following Figure 12: Fig. 12. Actions in action research Cycle 2. The process was similar as the research Cycle 1 but with some of new improvement ideas as a trial. The BOM (Bill-of-Material) in the last prototyperun was not used for the dynamic cut-off window in the 0 series period to avoid or reduce old material liability problem as in Cycle 1, because it was cancelled shortly at the beginning stage due to it being so hard to operate manually. Besides, the concurrent engineering principle was used between R&D and product change management with two approvals to product change. The first 88 approval can be just after the testing was done without problem so as to allow buying new material and stopping old material purchasing immediately. The second approval can be after all R&D work was done to approve product change finally. The important thing was to be sure the period between the first and the second approval should be shorter than the implementation time of the first site. 3.2.5 Evaluation The result of change implementation time with very low scraping cost can be seen in the following Figure 13 for the platform site (Site 1) and another main site (Site 2): Fig. 13. Implementation result of action research Cycle 2 from change notice database. The implementation result provided many meaningful implications for further analysis. It was the first time to have the results shorter than the target time at the first site. It was due to two approvals by saving about one month time from the concurrent engineering with R&D. The lesson was also learned from the material liability problem caused by the cancellation of the dynamic cut-off window. It was found a big amount of liability after the implementation was done. It had to request that the second site return to the old version in order to consume the old material so that the implementation time shown in the figure on the right was 34–35 weeks at the second site. 89 Without the information from the forecast, it was a lack of a future estimation. Such a situation made it very hard to plan for the next product change coming after this case. New findings in this action research cycle In action research Cycle 2, the attempt was actually focused on synchronisation to ensure the maximum production capacity as a balance point. Due to the forecast being mostly wrong, an attempt was made to live without it as an extension of Cycle 1. With the opportunity of the responsiveness strategy with a larger tolerance, there were also other lessons learned in the analysis to understand the secrets of global manufacturing. As a summary, here are new findings in action research Cycle 2: 1. 2. 3. 4. Possible to live without forecast – Just keeping inventory according to maximum production capacity if business strategy can be the extreme of responsiveness strategy. Another balance point for synchronisation – However, it can be selected not necessary always at the extreme (OK also with other possibilities). Concurrent engineering as synchronisation extended to R&D – possible again applying to other fields in business operation? Invisible material liability as an X-ray picture of supply operation – It seems no other options better than a dynamic cut-off window. – – 5. The forecast actually as a balance point - With similar principles, the forecast can act as a reference to be a balance point for synchronisation around it dynamically. – – 90 It will be much better if information can be visible as a direction for improvements. Is it possible to use IT solutions to obtain benefits for synchronisation deep into demand-supply chain but without causing too much trouble in the operation? The extreme of a strategy can be one of the statuses among a whole operational range. How to “place” global manufacturing by corporate strategy for other options in the middle? Practical contributions All of those new findings were the elements to enhance synchronisation. The contributions can be stated as follows so as to show practical business value: 1. 2. 3. 4. 5. It was a valuable experience to implement an extreme version of synchronisation (operating even without the forecast). The range of adjusting synchronisation can be thus pushed to the boundary approaching its limits. It was a case of understanding the lean or agile thinking to notice its extremes in synchronisation. With synchronisation extended to R&D, the total operational range was further thought as a better way. Although invisible material liability was a lesson to learn in this case, it can show that synchronisation made huge differences in the results for product change management, and also provided meaningful insights to global manufacturing in general. It was the case of facing uncertain future to understand the importance of the forecast - as a reference point in dynamic status. Comparing to the targets of product change management, Table 4 summarises the findings of the research Cycle 2: Table 4. Targets and new findings in action research Cycle 2. Strategic Targets in Product Change Management The Implications of New Findings from the Results Executing corporate strategy: responsiveness. Possible to live without forecast as an extreme Trial of four systematic concepts but actually to the Another balance point for synchronisation at the extreme of responsiveness opposite extreme Trial of concurrent engineering Concurrent engineering as synchronisation extended to R&D Invisible liability as an X-ray picture of supply operation The forecast actually as a balance reference point in dynamic business 3.3 Research Cycle 3 - shortening product change time In this strategy choice of action research Cycle 3 a technology leader with a new product or a significant change of an existing product quickly goes to market to 91 capitalise on a booming business. It was the focus moved to making product change as fast as possible even to accept a much bigger scraping cost. Obviously, it was not same as the strategy of cost-effectiveness in Cycle 1 or responsiveness in Cycle 2. The Cycle 3 in Figure 14 was a case in product change management including a big innovation. In this strategy, a revolutionary solution was expected. Fig. 14. The action research for faster innovativeness. All three extreme strategy choices were for different business reasons. In order to avoid the confusion, other formats of similar statement about those strategies are listed in following Figure 15. Fig. 15. A new choice of business strategy. 92 Not as easily understandable as other two options, innovativeness can help a technology leader to achieve technical advance, and then to use that advance as a weapon in tough competition. It was not the same as effectiveness or responsiveness, which can be for lean or agile manufacturers without the need for more explanations. It was a strategy used in action research Cycle 3 to do everything (even to accept much larger operational risk and scraping costs) just so that product change was made quickly. For this case only, the target was to make the innovation speed of new product to the market as fast as possible. Choosing the extreme strategies can be a natural way when starting to seek more alternatives. This research cycle became very exciting because it presented the opportunity to try something different from the two previous cycles. 3.3.1 Pre-Step By using innovativeness as the strategy, it should keep old material as little as possible to get rid of the previous version quickly as a lean effect. But, it also requires new material as much as possible to enter market faster with the new version as an agile purpose. The review of research conditions in context analysis: – – – – – – It was essential to achieve a fast product innovation with shorter time to the market. In a certain situation, it was also extremely vital in global competition for the company to do all possible efforts for it. Profitable innovation is an important competence for the company. Innovativeness as a strategy: a bigger scraping cost was acceptable for a faster speed of product changes. The inventory of ready products in the outbound warehouse (HUB) was a new condition to the demand-supply network with the possibility to watch product version change at the HUB (but not version modification for small changes). The forecast was used again in the MRP system, even though it could be still wrong. Planning BOM of the last prototype run was applied again during the 0 series period for material preparation but not as a dynamic cut-off window (actually as a fixed cut-off window with material version changeover date selected earlier but without any modification). 93 – – It was using the safety inventory of ready product in HUB to avoid the risk of the 0 series run failing. The implementation result can be evaluated in a new way to consider the product version change at both production and HUB inventory. It was a valuable opportunity to study a new extreme strategy – the innovativeness as another alternative outside the box of lean or agile manufacturing! 3.3.2 Diagnosis A new process was needed to develop what was achieved in the previous cycles for an even faster implementation of product change. The complexity of the dynamic cut-off window and the two approvals can be improved with more disruptive intentions. A simpler way was tried during the 0 series period just by starting implementation activities directly. With its bigger risk covered by ready product inventory, it can go beyond the limits of normal operational process for radical changes never done in the past. The outcome of diagnosing can be shown as follows: – – “Actual” implementation of product change started before 0 series. Fixed cut-off window better than dynamic cut-off window? It was an opportunity to go beyond normal limitations in manufacturing operation for more creative improvements. The strategy of innovativeness was thus as the third factor to affect the balance of synchronisation efforts. 3.3.3 Planning The safety inventory of products and the bigger scraping cost were accepted. Radical changes can be made to the product change process. Of course, the evaluation after the trial or implementation should also be made carefully. Each step of the action plan was aligned using the following procedure in Figure 16: 94 Fig. 16. Action plan for faster innovativeness. Even if the 0 series fails, product delivery to the customers can still be ensured by a HUB inventory of ready products. The worst situation can be the scraping cost from unusable new material and production stop due to lack of old material. However, it can be recovered with only limited damage in a short period of the trial. 3.3.4 Taking action The actions were taken following the process shown in the Figure 17 below: Fig. 17. Actions in action research Cycle 3. 95 The radical change in business process was planned and verified in the research Cycle 3 with the intention seeking a breakthrough effect in product innovation. The BOM (Bill-of-Material) in the last prototype run was used as a planning BOM to buy new material and stop old material purchasing even before the 0 series. The version change date was input into MRP without moving as a fixed cut-off window. Due to the unknown result of the 0 series (to approve product change or not), ready product inventory at the HUB was used as safety inventory. Product in HUB inventory should be consumed by itself without any chance of transferring it to other sites. If the 0 series run fails, another prototype or/and 0 series run will be needed, which, according to the modification, can be large or small. New material can be scraped if it is not used in the next new BOM. However, product delivery will not be delayed due to safety inventory in HUB, but production can be stopped due to no material of old version. It was thus beyond the limits at the material supply side involving the risks almost at a not acceptable level. 3.3.5 Evaluation The result of implementation time can be seen in the following Figure 18 for platform site (Site 1) and another main lean site (Site 2): Fig. 18. Implementation result of action research Cycle 3 from change notice database. 96 The analysis of implementation result indicated the challenge of product innovation due to its complexity in global manufacturing environment. The result was different from the perspective of production or HUB at the first site. From a production perspective, it was a very quick change done just after the approval – never working at such a fast speed in any previous cases. But from the view of HUB, product inventory was still consumed with a long period and showed almost no difference to past cases. The business benefit of cost saving from this product change can be achieved only after product version change done at HUB, but not just in production. Besides, the version change was selected as a fixed date, so the unbalanced material caused a higher scraping cost in this case (even though it was expected). Actually later in the practice, it was forbidden to use such an extreme way again without waiting for the 0 series result. But, this lesson had provided an unusual experience on how to move beyond the limits of product change management. The opportunity of innovation oriented manufacturing in a revolutionary way was remained for further research. New findings in this action research cycle In this case of innovativeness strategy, the product change was happening quickly in material inventory and production. The balance to equalise material supply for the changeover moment was not achieved. It was an impressive scraping cost to show such an unbalanced status of demand-supply: How big could the amounts of related components be if synchronisation was not done properly. Besides, the situation in product inventory reflected synchronisation equally important to all in demand-supply chain. It can imply the value of synchronisation better if it is done not just for a certain moment or a special part of enterprise ecosystem. As a summary, here are the new findings of action research Cycle 3: 1. 2. 3. The strategy of innovativeness emphasised as an independent option - It is not same as the innovation in lean or agile strategy to be its ingredient. Actual implementation of product change not so faster – As seen from this example, it can be very important how to evaluate the improvements properly to get an accurate picture. An unbalanced status in the supply without synchronisation – But, it can also be a great opportunity if synchronisation can be properly done. 97 4. Demand-supply chain always considered as a whole – As in many other examples, the whole demand-supply chain should be re-engineered not just for a company or a department. Practical contributions All of those new findings were valuable lessons for synchronisation. The contributions can be stated as follows so as to show practical business value: 1. 2. 3. 4. This cycle was a case to introduce the third factor into operational synchronisation for business complexity shown in an innovative way. Although the innovation would increase the uncertainties in strategic planning, this third factor can describe the real challenges in global manufacturing often truly driven by such a strategy. The lesson learned from the evaluation of the final result can indicate that the expectation of improvements should be ensured by a right way to do synchronisation in a big picture of the total range – not just a part of it. This cycle was also as a case to show an unbalanced status in the material supply if synchronisation was not done properly. It was obviously better if synchronisation can be done not just during a changing period but also during a normal time of healthy global manufacturing. It was increasingly noticed that as the range of synchronisation gets bigger and bigger, it approaches a total range, which was one of the reasons that finally led to synchronisation. It was learning by doing to find a new way in line with those action research cycles for a breakthrough. Comparing to the targets of product change management, Table 5 summarises the findings of the research Cycle 3: Table 5. Targets and new findings in action research Cycle 3. Strategic Targets in Product Change Management The Implications of New Findings from the Results Executing corporate strategy: innovativeness The strategy of innovativeness with possibility to be (product change as fast as possible). an extreme Disruptive changes in the process Actual implementation of product change not faster Trial how to use ready-product inventory in product An unbalanced status in the supply if there is no change management synchronisation Demand-supply chain always considered as a whole 98 4 Discussion 4.1 Answering research questions 4.1.1 Research question 1 Research Cycle 1 included the case company aiming all of its actions to minimising costs (Figure 19). The case company executed a strategy of cost effectiveness. Minimising inventory and scrapping costs required swift component control in the whole demand-supply chain. This chapter answers research question one by describing the results obtained through research Cycle 1, including both the positive and negative impacts of this trial. Fig. 19. Focus of research Cycle 1. As a starting point for this cycle, forecasts were not accurate and scrapping costs were a challenge. Lean aims to minimise costs. This research identified that it is beneficial to accept that forecasts are not always accurate and find ways to navigate in this type of reality. Before, in the case company, components had set minimum inventory levels. New order was placed once going below this minimum. Order sizes were set based on pre-calculated batch sizes. The company moved from this type of solution to a weekly assessment. Inventory levels and forecasts were followed on a weekly basis, and were further used for necessary orders. This resulted in 99 smaller batch sizes, more swift reaction on changes in demand and product variations, and less scrapping. In the case company, the old way was that new product version’s introduction to production was considered only after obtaining approval of zero-series. In order to speed up new product versions entering production, and improve transparency towards suppliers, ramp-up is now considered already during the zero-series. Changes in a product result in changes in the BOM. The company first analyses the critical components of the new BOM version. Component is critical if it is expensive, or it has a long lead time from order to delivery. In order to minimise scrapping in product change situations, it is necessary to identify the components with the longest lead times. This decides the earliest possible point when one can move to a new product version. One week’s margin is utilised to decide the product version change moment. The zero-series of the new version is followed, and ramp-up is postponed weekly until zero-series approval. As suppliers are now informed already in the beginning of zero-series, the suppliers will have time to react accordingly. This in turn reduces the liability and possible liability related costs. The case company uses the term of dynamic cut-off window for the new way. Negative aspects: People complained that dynamic cut-off window causes confusion as it changes every week. Manual way of changing target dates for new product versions was seen non-suitable new ways desired and IT tool would be possibly of benefit. 4.1.2 Research question 2 In research Cycle 2 the case company aimed at diminishing order delivery period (Figure 20). In this trial, the case company aimed at strong concurrency in engineering to get order delivery period as short as possible. This chapter answers research question two by describing the results obtained through research Cycle 2, including both the positive and negative impacts of this trial. 100 Fig. 20. Focus of research Cycle 2. Quick delivery (agility) strategy was utilised and the case company considered whether it would be possible in some situation to live without forecasts and accept high inventory. Higher component inventory enabled greater tolerance. The old way of doing things included new product versions going through zero-series, production and testing, and if the results were ok, documentation and approval process followed. Documentation and approval took about 3–4 weeks. Only once R&D approved new production version, the company started buying new material and stopped buying old material. Downside of the old way was that this waiting time of 3–4 weeks can be considered as waste from the perspective of product change management. The new way, tried during this research cycle, aimed to speed up the process by involving R&D to give earlier signal to product changes, so that the supplychain management people could start their work earlier. After Zero-series production testing proved acceptable, buying new material was started and buying old material finished. Documentation and approval process was conducted in parallel by R&D. Negative aspects: Using dynamic cut-off window was abandoned in this case. As a consequence the liability of the case company increased as the company had committed to buy components for the need of a certain period. This experiment caused delays at other sites, even if the situation was ok at the main site. This type of situation might cause other sites having to switch back to producing an old product version. 101 Without forecasts in the system, visibility over coming changes was lost. Now it was understood that a forecast would act as a reference point for equalising supply. This situation would also influence other companies, aside the case company. 4.1.3 Research question 3 Research Cycle 3 concentrated on shortening product change period (Figure 21). The case company executed a strategy of innovativeness making product changes as fast as possible. The trial clarified whether a ready-product inventory could be used to speed up product change. This chapter answers research question three by describing the results obtained through research Cycle 3, including both the positive and negative impacts of this trial. Fig. 21. Focus of research Cycle 3. Normally scrapping costs are minimised. In the research Cycle 3, scrapping costs were accepted, while everything was arranged to make product changes as quick as possible. This approach significantly differs from lean and agile. Unit-level ready products were used as inventory. This way the company could stop buying old material earlier and avoid problems in delivering to customers. Before starting zero-series, product new version changeover date was selected and fixed. This fixed cut-off window enabled suppliers to deliver the existing order plus liability. No further orders were placed for the old material, 102 and at a certain point during zero-series, order for new material was placed. This left the company with the possibility of zero-series failing, resulting in a second zero-series and stopping production due to old material running out. This experiment enabled the case company to understand the demand-supply chain better from a wider perspective, thus providing beneficial learning. Negative aspects: This experiment had more negative impacts than positive ones, and consequently this approach was banned after the experiment. From the business perspective, there was no improvement in the sense of cost savings. In this case, at the point when purchasing of old components was stopped, the level of different components was not equal, resulting in expensive scrapping costs. The difference in the levels of different components is caused by different buyers buying in the components they are responsible for in different pace and their activities not being coordinated. 4.2 Managerial implications The results of this study provide tips for global high tech companies. These large international companies typically have manufacturing sites in different parts of the world. Based on the results, mental shift from local optimisation to a global one is required for efficient manufacturing operations. Companies have traditionally considered their strategy as a choice between minimising costs, quick delivery, and rapid product change. Also, companies have believed that one single strategy is adequate and applicable to all of their products. However, according to this study, different products may have a different strategy. This allows companies to flexibly react to the needs of different customer groups, business environments, and different competitors. Strategy can also be changed relatively often, monthly, weekly, or even daily. Companies must consider all the three elements of minimising costs, quick delivery, and rapid product change and to find an adequate balance among these in order to succeed (Figure 22). The arrows in the figure represent flexibility in changing strategy. There can be different strategy for different products and competitive situations. In addition, companies have multiple partners and consequently a suitable balance is required for the entire demand-supply chain. Forecasts are an important, powerful tool for influencing the supply operations, as forecasts give information for suppliers. A company should try to make relevant information, including product change management, visible for both the company itself, and the entire supply chain. This would make it easier for the 103 subcontractors to optimise the entire chain if it has adequate access to critical information. Two-way communication is required to fully optimise the entire demand-supply chain. Fig. 22. Flexible optimisation on situation basis. Once optimising the entire supply-chain, in modern business environment, time is a vital competitive factor and companies must be swift in their moves. This results in optimisation on time basis becoming a key. This type of time-based optimisation means synchronisation of R&D, production, material handling, and related planning. Special attention should be paid to bigger events, such as new product launches, and significant engineering changes, as they have a wide influence. Based on the results of this study, companies must harmonise their product portfolio globally, including all their sites. Once the same product version is at all sites, they can help each other from components supply viewpoint, and consequently product changes can be taken through quicker. Companies must also equalise material status for supply, and follow it weekly. This is as different components of a same product must be seen as dependent on each other, not separately, meaning that if you cannot buy component A, there is no point buying components B, and C either. In a situation with too many components, the component you have least determines the equalised level. If there are any components more than the equalised level, those can be considered as waste. The difference between the equalised level and the original forecasted level can be considered as tolerance margin increasing agility. However, if the company prefers lean over agility, this type of tolerance should be avoided. 104 Above described new kind of thinking require developing IT tools to support global visibility and operations. These IT solutions would enable changing strategy often, even on product basis, resulting in business model agility. The fast industrialisations of R&D achievements constantly into a full scale of own global manufacturing is a stronger competitive advantage in case company, comparing to others in the industry with the production mostly in an outsourced way. The big difference of the speed can bring the success or the failure as the innovation in the industry. If such an advantage is not fully utilised or even gone in the future, the lost of leading position could happen as one of the reasons coming from this battle field. 4.3 Scientific implications The systematic review of the literature identified a number of important research gaps as the opportunities to make scientific contributions. It was lack of academic studies as either or both outside-in and inside-out manners to develop new thoughts along with product innovation in lean or agile manufacturing. The innovation itself was emphasised later even as an independent strategy to affect manufacturing operation beyond the lean or agile thinking box. No matter how harder to show 3-dimensional world, business complexity should be considered and handled in a fresh thinking of right way similar as the Figure 23 (not enough with a 2D view to different 3D realities): Fig. 23. Business complexity as 3-dimensional world out of lean or agile thinking box. Those are the knowledge gaps indentified as an approach to describe business optimisation studied by the research with sufficient scientific purposes: 105 – – – – – – Is one strategy only to avoid “stuck in the middle” still valid or just suitable in some conditions? What can be a new thinking beyond traditional lean or agile manufacturing theories? How to ensure a balance at strategy level to reduce the risk of business failures? What should be the key of optimisation in high-tech manufacturing? What could be an alternative way with more details to the forecast research? How the radical innovation is emphasised and used as a must in high-tech industry? Traditionally, it was thought a company can have only one strategy and that strategy is valid for a long period of time. Porter (1980 & 1998) emphasised that to be successful over the long-term, a firm must select only one of the three generic strategies. Otherwise, with more than one single generic strategy the firm will be "stuck in the middle" and will not achieve a competitive advantage. He argued that firms that are able to succeed at multiple strategies often do so by creating separate business units for each strategy. Similar idea (Treacy et al. 1993) also indicated that a company should have a clear position among the following choices to avoid the stuck-in-the-middle situation due to a lack of focus: – – – Operational Excellence Customer Intimacy Product Leadership. This study indicates the contrary: a company must excel with flexible optimisation choosing from multiple strategies on situation basis. A company should not be stuck in the middle, or only good at one of these strategic choices. For example, lean and agile ingredients should be simultaneously embedded. This aims to break the boundaries even further, because some literature uses the term leagility to describe the simultaneous combination of these two (Mason-Jones et al. 2000). In most literature, the rapid product change viewpoint is not as common as lean or agile studies (Gunasekaran, 1999). This thesis demonstrates that it is not enough to work on the two dimensions of lean and agile, but rather introduces a third dimension of the innovation – rapid product change. Consequently, the manufacturing strategy should be seen as a multidimensional playground, where the optimum can be different in different situations. 106 With above thoughts, this provides newness into scientific thinking. In modern high tech business, the competitive situation is turbulent, resulting in pressures for changing manufacturing strategy more often and even to have separate strategies for different products or product groups. A single strategy for a company or a business unit is not functioning well anymore. An unbalanced status from one of the three elements at the strategy level can cause a disaster for corporate business. For example, Toyota has been proud as the lean & TQC (Total Quality Control) benchmarks in the industries, as well as its Prius Hybrid models leading the innovation in the car industry. However, its business growth without proven design quality to ensure a proper supply & delivery expansion (similar to this study also as a bigger scale actor in another industry) brings Toyota into tremendous troubles. It has made loss during the two last years, after 70 years of outstanding financial results. Toyota is still struggling to recover from its recall disaster and regain a reputation that has made it the biggest car company in the world. This dissertation is thus highlighting that the optimisation of enterprise strategy within multidimensional playground should be conducted on time basis. This view is in line with the fact that time has become an increasingly important factor in high tech business (Christopher 1998). Flexible optimisation in a timely way is thought as a total synchronisation concept, which has been researched further in recent years as next big outcome. This dissertation confirms the findings of Einhorn (1986) from decision research about accepting error to make less error. In dynamic business nowadays, one has to accept inaccurate forecasts due to unpredictable business environment. After it, the opportunities will be identified to ensure the company (or its unit) not stuck in the middle or any “end” point of multi-strategy scope. Such scientific implications can guide the research leading to more solutions. Finally, the radical innovation should be used as a must in high-tech industry to measure and lead business performance in “Red Ocean” of the competition, which is not emphasised enough in the most of manufacturing theories. It can not be outside of the research even its focus as the optimisation for manufacturing operation or demand-supply network. 4.4 Reliability and validity In order to evaluate the results, it is needed to check the validation of the research quality. The definition of validation can be found from many academic resources 107 for different fields. Robert K. Yin (1994) also presents four complementary ways to judge the quality of empirical case study research: (1) reliability, (2) construct validity, (3) internal validity, and (4) external validity. It should be applied here to guide the discussion. In general, reliability is the ability of a system to perform and maintain its functions in routine circumstances, as well as hostile or unexpected circumstances. Reliability is necessary for validity and it is easier to achieve although it does not guarantee validity. Stated another way, reliability can be associated with random error and validity with systematic error. In general, validation is the process of checking if something satisfies a certain criterion. Validation implies one is able to testify that a solution or process is correct or compliant with set standards or rules. With the confirmation by examination and provision of objective evidence, it should conform to user needs and intended uses. The particular requirements implemented through the process can be consistently fulfilled. Validity can be extended to internal validity as internal design of the study and external validity as external generalisation made from results. Internal validity is a form of experimental validity if it properly demonstrates a causal relation between two variables. External validity is also a form of experimental validity if the experiment’s results hold across different experimental settings, procedures and participants. The meaning of the above figure can be understood easily without the need for further explanations. There is a format to review the dimensions of research quality to check the reliability and validity as following Table 6: 108 Table 6. Dimensions of research quality in the evaluation (format from Collin, 2003). Quality Case study tactic (Robert K. dimension Yin, 1994) Reliability Appearance in this study -Develop case study protocol -For all product change cases, the implementation targets of -Develop case study version changeover time and scraping cost are applied in the database same way. -A database of CN (Change Notice) and IN (Implementation Notice) is well constructed. Construct -Use multiple sources of -Many sources of knowledge or information were checked in Validity evidence theoretical and industrial trend review. -Establish chain of evidence -The research is a continuous development based on the -Have key informants review existing body of knowledge. draft case study report Internal -Do pattern matching -The flexible optimisation or synchronisation is a pattern-like Validity -Do explanation building way suitable to many solutions. -Do time series analysis -The systematic principles were built as the abstraction from action research cycles and product change cases (even some were not selected as the cases for research cycles). -Time series analysis was in line with action research cycles. External -Use replication logic in -Replication logic was used in multiple cases of product Validity multiple case studies changes. -Use case study protocol -Generalisation in action research approach is very limited even with many other product cases ongoing at the same time. But, the business situation has been becoming more and more dynamic in global manufacturing, which should be as a factor in the consideration (such as Toyota with profit loss also in 2008 after 70 years of positive results). Here are the key points to discuss validation and reliability in further details for this research with the above concerns: – The research scope was defined at the beginning for a narrow range within suitable industries. It was related to those large corporations who engage in high-tech manufacturing on a global scale. They should have a demandsupply network already as part of their strategy-driven operation with minimum product variation. It is now still valid with all the limitations verified earlier because some companies can be far away to such a maturity if they do not yet meet these pre-requisites. As a concern of validation, they are as essential conditions for repeatable results of product change management or operational improvement towards total synchronisation. 109 Due to the importance of IT support increasing dramatically, it can be another factor in the consideration of validation and reliability. For a global operation, such a competence should be good enough in order to avoid the trouble of synchronising in a manual way. A comparable level of IT competence should be needed along with business re-engineering in the company. Besides, it can also be interesting for business application suppliers or consultant companies as a great opportunity for business concept innovation and technology development direction. For a company in the competition, a different strategy should be used as a situational choice on a case-by-case basis. A business benchmark sample cannot be copied exactly to other companies, even though it was successful under certain conditions. Even for those benchmark companies, they can not keep a same result to themselves. This research proposes a new approach to dealing with the traditional problem of inaccurate forecasts in today’s more dynamic nature environment. Attention should be paid to more than the improvement of forecast accuracy alone if it is not working so well in business practices. – – – Guessing the possible direction of the plane and its speed difference to the rocket or the missile is a challenge. It can hit the target only if they “meet” in the shooting. It can be quite sure that at least there would be no big chance of hitting the plane if one just targets its current position, a situation that is similar to just making a copy in a dynamic business. Therefore, the degree of validation and reliability should be dependent on the abstracting level of the solutions. As a result-oriented way, the copy cannot bring great success in business because there can be no exactly same situation always kept to any companies or even the enterprise benchmark itself. To deal with business uncertainties, there is a need for abstracting the solutions, such as happens with time-based optimisation by multiple strategies. When applying it to the manufacturing operation, it should be as a pattern, with those principles being the baseline only. The nature of autonomic features should be considered in business for the success. 4.5 Research contribution & discussion New contributions of the research can be summarised as a base of further work in the future. It includes not only the insight to some arguments of management 110 theory, but also own discovery from this research. Each point is detailed with the explanations to provide an overall view of research results as Table 7: Table 7. Summarising new contributions from the research. New Contributions of Own Insight & Discovery The Explanations from Research Results Accept inaccurate forecast for the focus moving An empirical research truly aiming for alternative to seek alternative solutions solutions how to survive by synchronising demandsupply pace & flow even with “extra” product changes A practical reference to support the arguments in this field Introduce and prove three ingredients in A study sticking on the complexity of real business and manufacturing strategy (especially with the its key challenge. innovation emphasised) An independent “driver” separated from the leagility for the innovation in manufacturing operation as a strategy – deserving its research much more than what happened in the past. Research the reasons and the solutions for A deep understanding to tangible or intangible status product innovation challenges caused by supply of demand-supply details as the 1st report trying to lead-time gaps and material liability reduce liability effect in product changes Also as the 1st report about empirical research details of using product changes to study manufacturing improvements Identify a good opportunity to develop new theory A simple idea leading to new thoughts of a theory: of total synchronisation and IT solution (business How the principles to achieve no-scraping cost status intelligence automation) as its utilisation in global in product change (equal to ideal synchronisation) are scale for leading companies repeatable and applicable to normal time of global manufacturing? For those leading companies in global business, the innovation should be emphasised as a must at strategy level. The research brings it into whole thinking of manufacturing operation, which can be seen just a corner for the company or its demand-supply network. The innovation can affect much wider range of corporate performance. It explains why lean or agile strategy always has its drawbacks. Besides, product change is only one of the forms for the innovation when it will have radical effects of the differentiation (such as advanced technology, cost saving in big scale …). As a companywide view, any of similar efforts to bring radical differentiation for the company to achieve new competitive advantage is the definition so called “innovation”. It is the key of surviving in global 111 competition especially essential to be leading companies. Otherwise, lack of this ingredient in company’s strategies is a clear sign to the failure or already as a path to the end of industrial life cycle. The innovation is the big thing to determine the winner in global competition sooner or later, which is proved by many facts as the life cycle of industry. It is the time to deal with the complexity of three-dimensional world in real business and explore a new academic theory for it (such as the effort leading to “total synchronisation” oriented by the innovation in this research). 4.6 Future research This study presents new understanding on time-based optimisation of minimising costs, quick delivery, and rapid product change. Further research is however required to fully utilise the presented ideas, especially for what with the innovation as a driving force. For further abstraction, a so called “total synchronisation” concept is under development as next big outcome of the research. In order to better manage in global business, new IT solutions are needed to support this new thinking, requiring future study. The lack of studies about business intelligence automation can be a new opportunity of research field. In addition, the potential of web 2.0 for harnessing the creativity of people to support the type of optimisation discussed in this thesis would be a good topic for future study. The simulation about mobile phone industry by Reiner et al. (2009) can be an interesting sample if applicable in mobile infrastructure manufacturing also as research tool even though big differences do exist. This thesis has been conducted in a single company and one business environment, having more cases and expanding to new business areas would be an interesting topic for future study. Besides, the tendency of overusing the strategy of minimising costs during economic hard times, often results in losses to those leading companies. Global business is constantly under a turbulent change that has become normality, but is, however, too often ignored. “Wonderful” periods between two economic downtimes have become shorter and shorter. Too often companies use excuses that now we have to tighten our belts, accept slower operational speed, less product innovations and lower employee motivation as the times are harder. Instead the companies should accept the reality. People expect that they can apply the other strategies again when a good time is coming. As a result, their leading position in the industry gets literally lost. The leader status is not simply 112 maintained by making structural changes in the business sector e.g. with big acquisitions. The target of total synchronisation concept is to break such thinking and study right manner - always with multi-strategies in mind. It will help leading companies or new-coming challengers in the industry to win in global competition. This is why these aspects should be studied further. 113 114 5 Summary The main motive for this research arises from the fact that ICT has developed into a turbulent, high clock-speed sector. Industrial globalisation has greatly changed high-tech companies while they have created significant operations in multiple countries. Because poor visibility and massive uncertainty are part of the operational nature, new challenges arise continuously for companies who want to internationalise their demand-supply network. ICT companies face challenges in an unpredictable business environment, where demand-supply forecasting is not accurate enough. How to optimally manage product change process and demand-supply chain in this type of environment? Companies face pressures to simultaneously be efficient, responsive and innovative, i.e. to minimise costs, and shorten order delivery and product change periods. The effects of changes in essential parameters of inventory level, order delivery period, and product change time were studied in this dissertation for a real demand-supply chain of a significant international actor. Secondly, based on these analyses, this study attempted to find new means of dealing with complex issues in the unpredictable business environment. This thesis included three action research cycles. Each action research cycle sought answers by going into one extreme of minimising costs, diminishing order delivery period, or shortening product change periods. In practice, these research cycles included the case company changing their business accordingly for each of these cases. Conducting required changes in the case company were economically significant trials. The results of this doctoral dissertation provide tips for global high tech companies. Large international companies typically have manufacturing sites in different parts of the world. According to the results, mental shift from local optimisation to a global one is required for efficient manufacturing operations. Companies have traditionally considered their strategy as a choice between minimising costs, quick delivery, and rapid product change. Also, companies have believed that one single strategy is adequate and applicable to all of their products. However, according to this thesis, different products may have a different strategy. This would allow companies to flexibly react to the needs of different customer groups, business environments, and different competitors. In addition, strategy can be changed relatively often, monthly, weekly, or even daily. 115 Companies typically have multiple partners and consequently a suitable balance is required for the entire demand-supply chain. Forecasts are an important, powerful tool for influencing the supply operations, as forecasts give information for suppliers. A company should try to make relevant information, including product change management, visible for both the company itself, and the entire supply chain. This would make it easier for the subcontractors to optimise the entire chain if it has adequate access to critical information. Two-way communication is required to fully optimise the entire demand-supply chain. Based on the results of this doctoral thesis, companies must harmonise their product portfolio globally, including all their sites. Once the same product version is at all sites, they can help each other from components supply viewpoint. Consequently, product changes can be taken through quicker. Global product portfolio harmonisation can be seen as a new normal situation for the high tech business. This would enable further optimisation, covering all global operations. 116 References Aitken J, Childerhouse P & Towill D (2003) The impact of product life cycle on supply chain strategy. International Journal of Production Economics 85: 127–140. Alfnes E & Strandhagen JO (2000) Enterprise Design for Mass Customisation: The Control Model Methodology. International Journal of Logistics Research and Applications 3(2): 111–125. Amasaka K (2002) New JIT: A new management technology principle at Toyota. International Journal of Production Economics 80: 135–144. Amer Y, Luong L, Lee SH & Ashraf MA (2008) Optimising order fulfilment using design for six sigma and fuzzy logic, International Journal of Management Science and Engineering Management 3(2): 83–99. Amoako-Gyampah K (2003) The relationships among selected business environment factors and manufacturing strategy: insights from an emerging economy. The international Journal of Management Science 31: 287–301. Ashayeri J & Selen W (2005) An application of a unified capacity planning system. International Journal of Operations & Production Management 25(9): 917–937. Askin RG & Krishnan S (2009) Defining inventory control points in multiproduct stochastic pull systems. International Journal of Production Economics 120: 418–429. Auramo J (2006) Implications of Supply Chain Visibility: Benefits in Transaction Execution and Resource Network Management. Dissertation for the degree of Doctor of Science in Technology, Helsinki University of Technology. Auramo J & Ala-Risku T (2005) Challenges for going downstream. International Journal of Logistics Research and Applications 8(4): 333–345. Auramo J, Inkiläinen A, Kauremaa J, Kemppainen K, Kärkkäinen M, Laukkanen S, Sarpola S & Tanskanen K (2005) The roles of information technology in supply chain management. The 17th Annual NOFOMA Conference, Copenhagen, Denmark. Auramo J, Kauremaa J & Tanskanen K (2005) Benefits of IT in supply chain management – an explorative study of progressive companies. International Journal of Physical Distribution & Logistics Management 35(2): 82–100. Baharanchi SRH (2009) Investigation of the Impact of Supply Chain Integration on Product Innovation and Quality. Transaction E: Industrial Engineering 16 (1): 81–89. Bajgoric N (2000) Web-based information access for agile management. International Journal of Agile Management Systems 2(2): 121–129. Banerjee SK (2000) Developing manufacturing management strategies: Influence of technology and other issues. International Journal of Production Economics 64: 79–90. Bengtsson L & Berggren C (2002) Horizontally integrated or vertically divided? (http://www.iei.liu.se/pie/filarkiv/1.124472/nokia___ericsson_wp_final__0211_skydd ad.pdf) Berggren C & Bengtsson L (2004) Rethinking Outsourcing in Manufacturing: A Tale of Two Telecom Firms. European Management journal 22(2): 211–223. Bhasin S & Burcher P (2006) Lean viewed as a philosophy. Journal of Manufacturing Technology Management 17(1): 56–72. 117 Bilbrey, S (2000) Product Development at Dell. (Also from Braeger John P. (2003) International e-Business: Opportunities and Threats for Dell Inc.). http://www.cvn.columbia.edu/Courses/Summer2001/B8827_course_notes/19october2 000.pdf Blankenship JC (2004) Competitive Advantage through Business Performance Management. Issues in Information Systems V(1): 29–35. Bolander SF & Taylor SG (2000) Scheduling Techniques: A Comparison of Logic. Production and Inventory Management Journal, 41(1): 1–5. Bolarin FC, Mcdonnell LR & Garcia JM Reducing the impact of demand process variability within a multi-echelon supply. The 26th International Conference of the System Dynamics Society, Athens, Greece. Bonney MC, Zhang Z, Head MA, Tien CC & Barson RJ (1999) Are push and pull systems really so different? International Journal of Production Economics 59: 53–64. Boonyathan P & Power D (2007) Impact of Supply Chain Uncertainty on Business Performance and the Role of Supplier and Customer Relationships: Comparison between Product and Service Organisation. Proceedings of the DSI Mini Conference on Services Management, Pittsburgh, USA. Bozarth CC, Warsing DP, Flynn BB & Flynn EJ (2009) The impact of supply chain complexity on manufacturing plant performance. Journal of Operation Management 27: 78–93. Braithwaite I (2007) Apple iPhone: Reshaping the Strategic Network. ANZMAC 2007 Reputation, Responsibility & Relevance. New Zealand, ANZMAC: 849–857. Brassler A & Schneider H (2001) Valuation of strategic production decisions. International Journal of Production Economics 69: 119–127. Brennan RW & Foroughi B (1999) A control framework to support responsive manufacturing. International Journal of Agile Management Systems 1(3): 159–168. Brown S & Bessant J (2003) The manufacturing strategy-capabilities links in mass customization and agile manufacturing – an exploratory study. International Journal of Operations & Production Management 23(7): 707–730. Brown S, Squire B & Blackmon K (2007) The contribution of manufacturing strategy involvement and alignment to world-class manufacturing performance. International Journal of Operations & Production Management 27(3): 282–302. Buxey G (2006) Reconstructing inventory management theory. International Journal of Operations & Production Management 26(9): 996–1012. Chan FTS (2003) Performance Measurement in a Supply Chain. International Journal of Advanced Manufacturing Technology 21: 534–548. Chang H-C & Horng D-J (2010) The High-Quality Low-Price Strategy in Penetrating Emerging Market: A Case of Nokia’s Business Strategy in China. The Journal of International Management Studies, 5(2): 37–43. Chopra S & Meindl P (2001) Supply Chain Management, Prentice Hall Chopra S & Sodhi MS (2004) Managing Risk To Avoid Supply-Chain Breakdown. MIT Sloan Management Review 46(1): 53–61. 118 Christensen CM & Raynor ME (2003) The Innovator’s Solution, Creating and Sustaining Successful Growth. Harvard Business School Press. Christopher M (1998) Logistics and Supply Chain Management, Strategies for Reducing Cost and Improving Service. Financial Times/Prentice Hall. Christopher M & Lee H (2004) Mitigating Supply Chain Risk Through Improved Confidence. International Journal of Physical Distribution & Logistics Management 34(5): 388–396. Christopher M & Peck H (2004) Building the Resilient Supply Chain. International journal of Logistics Management 15(2): 1–14. Christophoer M & Towill DR (2000) Supply chain migration from lean and functional to agile and customised. International Journal of Supply Chain Management 5(4): 206– 213. Christophoer M & Towill DR (2002) Developing Market Specific Supply Chain Strategies. The International Journal of Logistics Management 13(1): 1–14. Collin J (2003) Selecting the Right Supply Chain for a Customer in Project Business, An Action Research Study in The Mobile Communications Infrastructure Industry. Helsinki University of Technology. Collin J & Lorenzin D (2005) Plan for Supply Chain Agility, Lessons from Mobile Infrastructure Industry. International Society for Agile Manufacturing. Copeland A & Shapiro AH (2010) The Impact of Competition on Technology Adoption: An Apples-to-PCs Analysis. Federal Reserve Bank of New York Staff Reports, no. 462. Corbett T & Mario J (2001) Analysis of the effects of seven drum-buffer-rope implementations. Production and Inventory Management Journal 42(3/4): 17–23. Coronado M. AE, Sargadu M & Millar C (2002) Defining a framework for information systems requirements for agile manufacturing. International Journal of Production Economics 75: 57–68. Corti D, Pozzetti A & Zorzini M (2006) A capacity-driven approach to establish reliable due dates in a MTO environment. International Journal of Production economics 104: 536–554. Coughlan P & Coghlan D (2002) Action Research for Operations Management. International Journal of Operations & Production Management 22(2): 220–240. Curry J & Kenney M (1999) Beating the Clock: Corporate Responses to Rapid Changes in the PC industry. California Management Review, 41(1): 8–36 Cusumano MA (1992) Japanese Technology Management: Innovations, Transferability, and the Limitations of "Lean" Production”, Massachusetts Institute of Technology, Sloan School of Management. Written for the MIT Symposium on "Managing Technology: The Role of Asia in the 21st Century," Davidrajuh R & Deng Z (2000) An autonomous data collection system for virtual manufacturing systems. International Journal of Agile Management Systems 2(1): 7– 15. Dell Corporation (2003): Accessed in 2003 http://dellapp.us.dell.com/careers/ professionals/manufacturing/index.asp 119 Dickson K & Fang F (2008) Management of R&D within a Dynamic Standardization Environment. The 5th International Conference of Innovation & Management, The Netherlands: 623–629. Disney SM, Naim MM & Potter A (2004) Assessing the impact of e-business on supply chain dynamics. International Journal of production economics. 89(2): 109–118. Dong JQ (2010) How Does Information Technology Enable Innovation in Supply Chains? Globelics 2010 – 8th International Conference, University of Malaya. Dooley L & O’Sullivan D (2003) Developing a software infrastructure to support systemic innovation through effective management. Technovation 23(8): 689–704. Doran D (2002) Manufacturing for synchronous supply: a case study of Ikeda Hoover Ltd. Integrated Manufacturing Systems 13(1): 18–24. Dreyer HC, Bakås O, Alfnes E, Strandhagen O & Kollberg M (2007) Global supply chain control: A conceptual framework for the Global Control Centre (GCC). International Federation for Information Processing 246: 161–170. Drzymalski J & Odrey NG (2006) Development of a Process reference Model and Performance Measures For Use in a Synchronised Supply Chain. Report No. 06W002, Lehigh University. Einhorn HJ (1986) Accepting Error or Make Less Error. Journal of personality assessment 50(3): 387–395. Ervolina TR, Ettl M, Lee YM & Peters DJ (2006) Simulating Order Fulfilment with Product Substitutions in an Assemble-to-order Supply Chain. The 2006 Winter Simulation Conference: 2012–2020. Esper TL, Ellinger AE, Stank TP, Flint DJ & Moon M (2010) Demand and supply integration: a conceptual framework of value creation through knowledge management. Journal of the Academy of Marketing Science 38 (1): 5–18. Ettl M, Huang P, Sourirajan K, Ervolina TR & Lin GY (2006) IBM Research Report: Supply and Demand Synchronisation in Assemble-To-Order Supply Chains. IBM Technical Paper. Falasca M & Zobel CW (2008) A Decision Support Framework to Assess Supply Chain Resilience. The 5th International ISCRAM Conference – Washington DC, USA. Falck M, Holmström J & Tanskanen K (2003) Research Agenda: Making Supply Chain Processes Work on Network Level. The 8th International Symposium on Logistics, Seville, Spain. Fildes R & Kumar V (2002) Telecommunications demand forecasting – a review. International Journal of Forecasting 18(4): 489–522. Flynn BB (1994) The Relationship between Quality Management Practices, Infrastructure and Fast Product Innovation. Benchmarking for Quality Management & Technology 1(1): 48–64. Foster WA (2010) Huawei's Leadership Role in IMS standards development and in its own proprietary Softswitch. http://www.fosterandbrahm.com/docs/HuaweisSoftswitchandIMS.pdf (accessed in December 2010). 120 Forrester J (1958) Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review, 36(4):37–66. Forza C (2002) Survey research in operations management: a process-based perspective. International Journal of Operations & Production Management 22(2): 152–194. Frazier GV & Reyes PM (2000) Appling synchronous manufacturing concepts to improve production performance in high-tech manufacturing. Production and Inventory Management Journal 41(3): 60–65. French WL & Bell C (1973) Organization development: behavioural science interventions for organization improvement. Prentice-Hall. Frohlich MT & Westbrook R (2002) Demand chain management in manufacturing and service: web-based integration, drivers and performance. Journal of Operations Management 20: 729–745. Gadde L & Håkansson H (2001) Supply Network Strategies, John Wiley & Sons, Ltd Ghazawneh A (2010) The Role of Platforms and Platform Thinking in Open Innovation Networks. The 43rd Hawaii International Conference on System Sciences. Goh M, Lim JYS & Meng F (2007) A stochastic model for risk management in global supply chain networks. European Journal of Operational Research 182: 164–173. Gottfredson M, Schaubert S & Saenz H (2008) The New Leader’s Guide to Diagnosing the Business. Harvard Business Review, February: 63–73. Govindu R & Chinnam RB (2007) MASCF: A generic process-centred methodological framework for analysis and design of multi-agent supply chain systems. Computers & Industrial Engineering 53: 584–609. Graman GA & Magazine MJ (2006) Implementation issues influencing the decision to adopt postponement. International Journal of Operations & Production Management 26 (10): 1068–1088. Guess V (2002) Change Management”, Institute of Configuration Management. http://www.cmiiresearch.com/CMII%20White%20Papers1/Change_Management.pdf Gunasekaran A (1999) Just-in-time purchasing: An investigation for research and applications. International Journal of Production Economics 59: 77–84. Gunasekaran A (1999) Agile manufacturing: A framework for research and development. International Journal of Production Economics 62: 87–105. Gunasekaran A, Patel C & Tirtiroglu E (2001) Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management 21 (1/2): 71–87. Gustafsson J & Norrman A (2001) Network Managed Supply – Execution of Real Time Replenishment in Supply Networks. The 6th International Symposium on Logistics. Haan J & Masaru Y (1999) Zero inventory management: facts or fiction? Lessons from Japan. International Journal of Production Economics 59: 65–75. Hahn RW & Singer HJ (2009) Why the iPhone Won’t Forever and What the Government Should Do to Promote its Successor. Accessed in November 2010 / Available at SSRN: http://ssrn.com/abstract=1477042. Hallgren M & Olhager J (2006) Quantification in manufacturing strategy: A methodology and illustration. International Journal of Production Economics 104: 113–124. 121 Halonen J (editor), UDOI project partners (authors) (2010) Research Framework and Methods Overview for User Driven Open Innovation. http://www.flexibleservices.fi/files/file/pdf/UDOI_B_deliverable_310410_final.pdf (accessed in December 2010) Heikkilä J (2002) From supply to demand chain management: efficiency and customer satisfaction. Journal of Operations Management 20: 747–767 Helo P, Xiao Y & Jiao JR (2006) A web-based logistics management system for agile supply demand network design. Journal of Manufacturing Technology Management 17 (8): 1068–1077. Hilletofth P (2009) How to develop a differentiated supply chain strategy. Industrial Management and Data Systems, 109(1), 16–33. Hilletofth P (2010) Demand-Supply Chain Management. Chalmers University of Technology, Sweden. Hilletofth P, Ericsson D & Lumsden K (2010) Coordinating new product development and supply chain management. International Journal of Value Chain Management 4 (1/2): 170–192. Hilletofth P & Hilmola O-P (2010) Role of Emerging Markets in Demand-Supply Chain Management. 15th Cambridge International Manufacturing Symposium. Hill T (2000) Manufacturing Strategy. Palgrave Macmillan. Hilmola O-P, Ma H & Datta S (2008) A Portfolio Approach for Purchasing Systems: Impact of Switching Point. Research paper for MIT Forum for Supply Chain Innovation. Hinterhuber HH & Friedrich SA (2002) The technology dimension of strategic leadership. International Journal of Production Economics 77: 191–203. Ho CF, Chi YP & Tai YM (2005) A Structural Approach to Measuring Uncertainty in Supply Chains. International Journal of Electronic Commerce 9 (3): 91–114. Hoek RI (2000) The thesis of leagility revisited. International Journal of Agile Management Systems 2 (3): 196–201. Hoek RI (2001) Measuring agile capabilities in the supply chain. International Journal of Operations & Production Management 21 (1/2): 126–148. Holmström J, Främling K, Kaipia R & Saranen J (2002) Collaborative planning forecasting and replenishment: new solutions needed for mass collaboration. Supply Chain Management: An International journal 7 (3): 136–145. Holmström J, Hoover Jr, Eloranta E & Vasara A (1999) Using value reengineering to implement breakthrough solutions for customers. International Journal of Logistics Management 10 (2): 1–12. Holmström J, Korhonen H, Laiho A & Hartiala H (2006) Managing product introductions across the supply chain: findings from a development project. Supply Chain Management: An International Journal 11 (2): 121–130. Holmstrőm J, Småros J, Disney SM & Towill DR (2003) Collaborative Supply Chain Configurations: The Implications for Supplier Performance in Production and Inventory Control. The 8th International Symposium on Logistics, Seville, Spain. 122 Holweg M (2005) The three dimensions of responsiveness. International Journal of Operations & Production Management 25 (7): 603–622. Holweg M (2006) The genealogy of lean production”, Journal Of Operations Management 25(2): 420–437. Holweg M, Disney S, Holmström J & Småros J (2005) Supply Chain Collaboration: Making Sense of the Strategy Continuum. European Management Journal 23 (2): 1– 33 Hoover WE, Eloranta E, Holmström J & Huttunen K (2001) Managing the Demand-supply Chain: Value Innovations for Customer Satisfaction. John Wiley & Sons Inc. Hoque MA & Kingsman BG (2006) Synchronisation in common cycle lot size scheduling for a multi-product serial supply chain. International Journal of Production Economics 103: 316–331. Huawei (2010) Milestones of Huawei (accessed in November, 2010). http://www.huawei.com/corporate_information/milestones.do Hui LT (2004) Business timeliness: the intersections of strategy and operations management. International Journal of Operations & Production Management 24 (7): 605–624. Ismail AA (2009) A Simulation Model to Investigate Critical Factors influencing the Bullwhip Effect in a Supply Chain. Master Thesis, The French University in Egypt. Jalote P, Palit A, Kurien P & Peethamber VT (2004) Timeboxing: a process model for iterative software development. Journal of Systems and Software 70(1–2): 117–127. Jammernegg W, Reiner G (2007) Performance improvement of supply chain processes by coordinated inventory and capacity management. International Journal of Production Economics 108: 183–190. Joshi VY (2000) Information Visibility and Its Effect on Supply Chain Dynamics. MIT, Massachusetts, Kaipia R (2009) Coordinating material and information flows with supply chain planning. International Journal of Logistics Management 20 (1):144–162. Kaipia R & Hartiala H (2006) How to benefit from Visibility in Supply Chains. International Journal of Agile Manufacturing 9 (1): 9–18. Kaipia R, Holmström J & Hellström M (2007) Measuring the benefit of changing the value offering in supply chains. Production Planning and Control 18 (2): 131–141. Kaipia R & Laiho A (2009) Differentiation of Supply Management processes in a Global Manufacturing Company. 16th International Annual EurOMA 2009 Conference, Sweden. Karemer KL, Dedrick J & Yamashiro S (2000) refining and Extending the Business Model With Information Technology: Dell Computer Corporation. The Information Society 16: 5–21. Kauremaa J, Auramo J, Tanskanen K & Kärkkäinen M (2004) The use of information technology in supply chains: transactions and information sharing perspective. Logistics Research Network Annual Conference, Dublin, Ireland. 123 Kemppainen K & Vepsäläinen APJ (2004) Differentiation for Integration of Supply Networks. Second World Conference on POM and 15th Annual POM Conference, Cancun, Mexico, Ketchen DJ Jr., Rebarick W, Hult GTM & Meyer D (2008) Best value supply chains: A key competitive weapon for the 21st century. Business Horizons 51: 235–243. Kim WC & Mauborgne R (2005) Blue Ocean Strategy: From Theory to Practice. California Management Review 47 (3): 105–121. Kim WC & Mauborgne R (2005) Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant. Harvard Business Press. Kim WC & Mauborgne R (2005) Value Innovation: a leap into the blue ocean. Journal of business Strategy 26 (4): 22–28. Knight T (2003) Best Practices Using CM II. www.ptcuser.org/2003/CMII.ppt Knowles G, Whicker L, Femat JH & Canales FDC (2005) A conceptual model for the application of Six Sigma methodologies to supply chain improvement. International Journal of Logistics 8 (1): 51–65. Koh SCL & Gunasekaran A (2006) A knowledge management approach for managing uncertainty in manufacturing. Industrial Management & Data Systems 106 (4): 439– 459. Kopczak LR, Balaji A, Ellis M & Macial A (1998) Materials Management at Lucent Technologies: 3C vs. MRP, Stanford Global Supply Chain Forum. Krishnamurthy R & Yauch CA (2007) Leagile manufacturing: a proposed corporate infrastructure. International Journal of Operations & Production Management. 27 (6): 588–604. Kumar S & Meade D (2002) Has MRP run its course? A review of contemporary developments in planning systems. Industrial Management & Data Systems 102 (8): 453–462. Lau C-M, Lu Y, Makino S, Chen X & Yeh R-S (2002) Knowledge Management of HighTech Firms in China. Management of Enterprises in People’s Republic of China: 183– 210. Lee H (2002) Aligning Supply Chain Strategies with Product Uncertainties. California management review 44 (3): 105–119 Lee HL, Hoyt D, Siu P & Tseng MM (2010) Shanzhai ("Bandit") Mobile Phone Companies: The Guerrilla Warfare of Product Development and Supply Chain Management. Harvard Business Publishing. Lee H, Padmanabhan V & Wang S (1997) The Bullwhip Effect in Supply Chains, MIT Sloan Management Review 38 (3): 93–102 Lehtonen J-M, Småros J & Holmström J (2005) The effect of demand visibility in product introductions. International Journal of Physical Distribution & Logistics Management, 35 (2): 101–115. Li Y (2010) “Shanzhai“ as an innovation in a competitive market environment in a competitive market environment. Thesis, University of Iceland. Lin CT (2010) Smoothing Demand Disruption in Collaborative Planning, Forecasting and Replenishment Model Development. Journal of Quality 17(2): 115–129. 124 Little D, Peck M, Rollins R & Porter K (2001) Responsive manufacturing demands alignment of production control methods to business drivers. Integrated Manufacturing Systems 12 (3): 170–178. Liu X (2005) China’s Development Model: An Alternative Strategy for Technological Catch-Up. Working paper, Institute of Innovation Research, Hitotsubashi University, Japan. Lo C-P (2008) Global Outsourcing or FDI: How Did Apple Launch its iPod? Presented at Western Economic Association International 83 Annual Conference, June 29-July 3, Hawaii, U.S. Loch CH & Tapperb UAS (2002) Implementing a strategy-driven performance measurement system for an applied research group. The Journal of Product Innovation Management 19 (3): 185–198. Lummus RR, Vokurka RJ & Alber KL (1998) Strategic supply chain planning. Production and Inventory Management Journal 39(3): 49–58. Lyu JJ & Su H-Y (2009) Lead Time Reduction by Extended MPS System in the Supply Chain, The book of Global Perspective for Competitive Enterprise, Economy and Ecology: Advanced Concurrent Engineering 11: 593–600. Mandal P & Gunasekaran A (2002) Application of SAP R/3 in on-line inventory control. International Journal of Production Economics 75: 47–55. MASCADA (1998) WP1 Dissemination Report: Analysis and Evaluation of Change and Disturbances in Industrial Plants. http://www.mech.kuleuven.be/mascada/dissemination/dissemination.html Mason-Jones R, Naylor B & Towill DR (2000) Engineering the leagile supply chain. International Journal of Agile Management Systems 2 (1): 54–61. Mason-Jones R, Naylor B & Towill DR (2000) Lean, agile or leagile? Matching your supply chain to the marketplace. International Journal of Production Research 38 (17): 4061–4070. McCullen P & Towill D (2001) Achieving lean supply through agile manufacturing. Integrated Manufacturing Systems 12 (7): 524–533. Min S & Mentzer J (2000) The role of marketing in supply chain management. International Journal of Physical Distribution and Logistics Management 30(9): 766– 787. Mohebbi E, Choobineh F & Pattanayak A (2007) Capacity-driven vs. demand-driven material procurement systems. International Journal of Production Economics 107: 451–466. Mohr J, Sengupta S & Slater S (2010) Mapping the Outsourcing Landscape. Accepted for publication in Journal of Business Strategy. Monczka R & Morgan J (2000) Competitive Supply Strategies for the 21st Century, Purchasing, January 13: 48–59. Monroe RW & Martin PR (2009) Addressing Supply Chain Risks Through Agile Strategies. The 2009 Southeast Decision Sciences Institute Conference, Savannah, Georgia, USA. 125 Mulrennan T (2010) The Human and Exploitative Side of Digital Capitalism: The iPod’s Journey Along the Globalisation Trail. Limerick Student journal of Sociology 2 (2): 89–102. Naruse T (2003) Equalized and Synchronized Production: The High-Mix Manufacturing System that Moves Beyond JIT. New York, NY: Productivity Press. Naylor JB, Naim MM & Berry D (1999) Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. International Journal of Production Economics 62: 107–118. Nielsen P & Hanseth O (2010) Towards a design theory of usability and generativity. 18th European Conference on Information Systems, Pretoria, South Africa. Nightingale D (2009) Principles of Enterprise Systems. Second International Symposium on Engineering Systems MIT, Cambridge, Massachusetts, USA. Nilsson F & Darley V (2006) On complex adaptive systems and agent-based modelling for improving decision-making in manufacturing and logistics settings. International Journal of Operations & Production Management 26 (12): 1351–1373. Nishimura A (2008) Effect of Management System on Management Accounting: The Case of Chinese Cellular Phone Terminal Unit Manufacturers. Asia-Pacific Management Accounting Journal 3 (1): 87–105. O’Brien, R (1998) An overview of the methodological approach of action research. Toronto, Canada University of Toronto, Faculty of Information studies, 22p. (also from http://www.web.net/~robrien/papers/arfinal.html) Olhager J, Rudberg M & Wikner J (2001) Long-term capacity management: Linking the perspectives from manufacturing strategy and sales and operations planning. International Journal of Production Economics 69: 215–225. Ottosson S (2004) Dynamic product development — DPD. Technovation 24 (3): 207–217. Pagell M, Newman WR, Hanna MD & Krause DR (2000) Uncertainty, flexibility, and buffers: Three case studies. Production and Inventory Management Journal 41 (1): 35–43. Papadopoulou TC & Özbayrak M (2005) Leanness: experiences form the journey to date. Journal of Manufacturing Technology Management 16 (7): 784–807. Piotrowski C & Guyette RW (2010) Toyota Recall Crisis: Public Attitudes on Leadership and Ethics. Organization Development Journal 28 (2): 89–97. Pisano GP & Shih WC (2009) Restoring American Competitiveness. Harvard Business Review July-August: 114–125. Popovic A, Turk T & Jaklic J (2010) Conceptual Model of Business Value of Business Intelligence Systems. Management 15(1): 5–30. Porter ME (1980) Competitive Strategy. Techniques for Analyzing Industries and Competitors. New York: Free Press. Porter ME (1998) Competitive Advantage. Creating and Sustaining Superior Performance. With a new Introduction. New York: Free Press. Prater E, Biehl M & Smith MA (2001) International supply chain agility - Tradeoffs between flexibility and uncertainty. International Journal of Operations & Production Management 21 (5/6): 823–839. 126 Prince J & Kay JM (2003) Combining lean and agile characteristics: Creation of virtual groups by enhanced production flow analysis. International Journal of Production Economics 85: 305–318. Rabta B, Alp A & Reiner G (2009) Queuing Networks Modelling Software for Manufacturing. Chapter 2 in the book of Rapid Modelling for Increasing Competitiveness, Springer, London. Radhakrishnan P, Prasad VM & Gopalan MR (2009) Inventory Optimisation in Supply Chain Management using Genetic Algorithm. International Journal of Computer Science and Network Security 9(1): 33–40. Raisinghani MS & Hanebeck H-CL (2002) Rethinking B2B E-Marketplaces and Mobil Commerce: From Information to Execution. Journal of Electronic Commerce Research 3 (2): 86–97. Raj TS & Lakshminarayanan S (2008) Entropy Based Optimisation of Decentralised Supply Chain Networks. 17th World Congress The International Federation of Automatic Control, Korea. Ranjan J (2009) Business Intelligence: Concepts, Components, Techniques and Benefits. Journal of Theoretical and Applied Information Technology 9 (1): 60–70. Rantala L & Hilmola O-P (2005) From manual to automated purchasing – Case: middlesized telecom electronics manufacturing unit. Industrial Management & Data Systems 105 (8): 1053–1069. Rantala L & Hilmola O-P (2010) Analysis of two different automated purchase order systems in telecom electronics manufacturing unit. International Journal of Manufacturing Technology and Management 19(1/2): 140–164. Reichhart A & Holweg M (2007) Creating the customer-responsive supply chain: a reconciliation of concepts. International Journal of Operations & Production Management 27(11): 1144–1172. Reiner G (2005) Customer-oriented improvement and evaluation of supply chain processes supported by simulation models. International Journal of Production Economics 96: 381–395. Reiner G & Fichtinger J (2009) Demand forecasting for supply processes in consideration of pricing and market information. International Journal of Production Economics 118: 55–62. Reiner G, Natter M, Drechsler W (2009) Life cycle profit – reducing supply risks by integrated demand management. Technology Analysis & Strategic Management 21(5): 653–664. Rigby C, Day M, Forrester P & Burnett J (2000) Agile supply: rethinking systems thinking, systems practice. International Journal of Agile Management Systems 2 (3): 178–186. Rixner B, Hubka A, Booth A (2007) Unlocking the Value of a Technological Portfolio. Oliver Wyman Journal 33–38. Robertson M & Jones C (1999) Application of lean production and agile manufacturing concepts in a telecommunications environment. International Journal of Agile Management Systems 1(1): 14–16. 127 Roshan G & Viswanadham N (2004) Working Paper Series: A Conceptual and Analytical Framework for the Management of Risk in Supply Chains. http://www.isb.edu/faculty/Working_Papers_pdfs/A_Conceptual_and_Analytical_Framew ork.pdf Ruffa SA (2008) Going Lean: How the Best Companies Apply Lean Manufacturing Principles to Shatter Uncertainty, Drive Innovation, and Maximise Profits. Aerican Management Association. Ryu S-J, Tsukishima T & Onari H (2009) A study on evaluation of demand informationsharing methods in supply chain. International Journal of Production Economics 120: 162–175. Saab J & Correa H (2004) The Forrester effect reduction: one size fits all? Second World Conference on POM and 15th Annual POM Conference, Cancun, Mexico, Sahin F (2000) Manufacturing competitiveness: different systems to achieve the same results. Production and Inventory Management Journal 41(1): 56–65. Sako M (2009) Global Strategies in the Legal Services Marketplace: Institutional Impacts on Value Chain Dynamics. Working Paper, Saïd Business School, University of Oxford, UK. Salmi L & Holmström J (2004) Monitoring new product introductions with sell-through data from channel partners. International Journal of Supply Chain Management 9(3): 209–212. Santoso T, Ahmed S, Goetschalckx M & Shapiro A (2005) A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research 167: 96–115. Sapkauskiene A & Leitoniene S (2010) The Concept of Time-Based Competition in the Context of Management Theory. The journal of Engineering Economics 21(2): 205– 213. Saunders M, Lewis P & Thornhill A (2007) Research methods for Business Students, 4th edition, Prentice Hall. Schmitt BH (2007) Big Think Strategy, Harvard Business School Press. Sepehri M, Fayazbakhah K & Ghasemzadeh F (2010) A Corporate Supply Optimiser with Flow Network. Transaction E: Industrial Engineering 17(1): 70–83. Shahbazpour M & Seidel RH (2006) Using Sustainability for Competitive Advantage. 13th CIRP International Conference on Life Cycle Engineering 287–292. Sharifi H, Ismail HS & Reid I (2006) Achieving agility in supply chain through simultaneous “design of” and “design for” supply chain. Journal of Manufacturing Technology Management 17(8): 1078–1098. Småros J, Lehtonen J-M, Appelqvist P & HolmstrÖm J (2003) The impact of increasing demand visibility on production and inventory control efficiency. International Journal of Physical Distribution & Logistics Management 33(4): 336–354. Snyder LV, Scaparra MP, Daskin MS & Church RL (2006) Planning for Disruptions in Supply Chain Networks. INFORMS Annual Meeting 128 Spink CA & Krudewagen U (2009) From Acquired Rights to Reverse Tupe: Employment Law Issues in global Outsourcing Transactions. Chicago-Kent Journal of International & comparative Law: 46–99. Srikanth ML (1997) Synchronous Management: Profit-Based Manufacturing for the 21st Century. Spectrum Pub Co. Stevenson M & Spring M (2007) Flexibility form a supply chain perspective: definition and review. International Journal of Operation & Production Management 27(7): 685– 713 Stohr EA & Zhao JL (1997) A Technology Adaptation Model for Business Process Automation. 30th Hawaii International Conference on System Sciences (HICSS) Volume 4: Information Systems Track - Internet and the Digital Economy. Stratton R & Warburton RDH (2003) The strategic integration of agile and lean supply. International Journal of Production Economics 85: 183–198. Stratton R & Warburton RDH (2006) Managing the trade-off implications of global supply. International Journal of Production Economics 104: 667–679. Subramoniam R, Huisingh D & Krishnankutty KV (2008) Mass Customisation: A Key Driver for the Emerging Automotive Aftermarket Business Model. International Journal of Global Business 1 (1): 1–25. Suri R (1998) Quick Response Manufacturing – A Companywide Approach to Reducing Lead Times, Productivity Press. Suri R (2002) Quick Response Manufacturing: A Competitive Strategy for the 21st Century. Proceedings of the 2002 POLCA Implementation Workshop. Suri R (2003) QRM and POLCA: A Winning Combination for Manufacturing Enterprises in the 21st Century. Technical Report, Centre for Quick Response Manufacturing. Suri R & Krishnamurthy A (2003) How to Plan and Implement POLCA: A Material Control System for High-variety or Customer-Engineered Products. Technical Report, Centre for Quick Response Manufacturing. Susarla A, Barua A, Konana P & Whinston AB (2004) Operational Impact of Information Sharing between Firms. WISE (Workshop on Information Systems and Economics, University of Maryland, USA. Susman, GI & Evered, RD (1978) An Assessment of the Scientific Merits of Action Research, Administrative Science Quarterly 23: 582–603. Swafford PM, Ghosh S & Murthy NN (2006) A framework for assessing value chain agility. International Journal of Operations & Production Management 26 (2): 118– 140. Takahashi K & Nakamura N (2000) Agile control in JIT ordering systems. International Journal of Agile Management Systems 2 (3): 242–252. Tan H & Mathews JA, (2010) Cyclical industrial dynamic: The case of the global semiconductor industry. Technological Forecasting & Social Change 77: 344–353. Tan H & Mathews JA, (2010) Identification and analysis of industry cycles. Journal of Business Research 63: 454–462. 129 Terwiesch C, Chea KS & Bohn RE (2001) International product transfer and production ramp-up: a case study from the data storage industry. R&D Management 31 (4): 435– 451. Thite M (2003) Strategic Positioning of HRM in the Knowledge Economy. The Fourth European Conference on Organizational Knowledge, Learning, and Capabilities (OKLC 4), Barcelona, Spain (IESE Business School, University of Navarra). Thomke S & Fujimoto T (2000) The Effect of “Front-Loading” Problem-Solving on Product Development Performance. Journal of Product Innovation Management 17 (2): 128–142. Treacy, M & Wiersema, F, (1993) Customer Intimacy and Other Value Disciplines, Harvard Business Review Jan-Feb: 84–93. Udin ZM, Khan MK & Zairi M (2006) A collaborative supply chain management framework. Business Process Management Journal 12(3): 361–376. Umble MM & Srikanth ML (1996) Synchronous Manufacturing: Principles for World Class Excellence. Spectrum Pub Co. Utterback JM (1996) Mastering the Dynamics of Innovation, Harvard Business School Press. Vandaele N, Claerhout D & Nieuwenhuyse IV (2005) E-POLCA to control multi-product, multi-machine job shops. Research Paper, University of Antwerp, Belgium. Vokurka RJ & Lummus RR (2000) The Role of Just-In-Time in Supply Chain Management. The International Journal of Logistics Management 11 (1): 89–98. Vonderembse MA, Uppal M, Huang SH & Dismukes JP (2006) Designing supply chains: Towards theory development. International Journal of Production Economics 100: 223–238. Voss C. A. (1987) Just-In-Time Manufacture (International Trends in Manufacturing Technology). Springer-Verlag New York. Walker WT (2002) Practical application of drum-buffer-rope to synchronize a two-stage supply chain. Production and Inventory Management Journal 43(3/4): 13–23. Watad M (2009) The organizational dynamics of knowledge and IT-enabled innovations. Journal of Technology Research 2: 1–12. Walters D & Rainbird M (2008) The demand chain and response management: New directions for operations management? Working Paper ITLS-WP-08–17, The University of Sydney, Australia. Wazed MA, Ahmed S & Yusoff N (2009) Uncertainty Factors in Real Manufacturing Environment. Australian Journal of Basic and Applied Sciences 3 (2): 342–351. Webster M (2002) Supply system structure, management and performance: a conceptual model. International Journal of Management Reviews 4 (4): 353–369. Welker GA & Vries J (2005) Formalizing the ordering process to achieve responsiveness. Journal of Manufacturing Technology Management 16 (4): 396–410. Williams S, Williams N (2003) The Business Value of Business Intelligence. Business Intelligence Journal, Fall: 1–11. Yin RK. (1994) Case study research. Design and methods. Sage Publications. 130 Wu D & Zhao F (2007) Entry Modes for International Markets: Case Study of Huawei, a Chinese Technology Enterprise. International Review of Business Research Papers 3 (1): 183–196. Wu X & Zhang W (2009) Business Model Innovation in China: From a Value Network Perspective. The conference of “US-China Business Cooperation in the 21st Century: Opportunities and Challenges for Entrepreneurs,” Indiana University, USA. Yusuf YY, Gunasekaran A, Adeleye EO & Sivayoganathan K (2004) Agile supply chain capabilities: Determinants of competitive objectives. European Journal of Operational Research 159: 379–392. Yusuf YY, Sarhadi M & Gunasekaran A (1999) Agile manufacturing: The drivers, concepts and attributes. International Journal of Production Economics 62: 33–43. Zivojinovic S & Stanimirovic A (2009) Knowledge, intellectual capital and quality management As well as balanced scorecard lead to improved Competitiveness and profitability. International Journal for Quality Research 3(4): 339–351. Zhou KZ (2006) Innovation, imitation, and new product performance: The case of China. Industrial Marketing Management 35: 394–402. 131 132 C378etukansi.kesken.fm Page 2 Tuesday, December 21, 2010 3:43 PM ACTA UNIVERSITATIS OULUENSIS SERIES C TECHNICA 362. Sahlman, Kari (2010) Elements of strategic technology management 363. Isokangas, Ari (2010) Analysis and management of wood room 364. Väänänen, Mirja (2010) Communication in high technology product development projects : project personnel’s viewpoint for improvement 365. Korhonen, Esa (2010) On-chip testing of A/D and D/A converters : static linearity testing without statistically known stimulus 366. Palukuru, Vamsi Krishna (2010) Electrically tunable microwave devices using BSTLTCC thick films 367. Saarenpää, Ensio (2010) Rakentamisen hyvä laatu : rakentamisen hyvän laadun toteutuminen Suomen rakentamismääräyksissä 368. Vartiainen, Johanna (2010) Concentrated signal extraction using consecutive mean excision algorithms 369. Nousiainen, Olli (2010) Characterization of second-level lead-free BGA interconnections in thermomechanically loaded LTCC/PWB assemblies 370. Taskila, Sanna (2010) Improved enrichment cultivation of selected foodcontaminating bacteria 371. Haapala, Antti (2010) Paper machine white water treatment in channel flow : integration of passive deaeration and selective flotation 372. Plekh, Maxim (2010) Ferroelectric performance for nanometer scaled devices 373. Lee, Young-Dong (2010) Wireless vital signs monitoring system for ubiquitous healthcare with practical tests and reliability analysis 374. Sillanpää, Ilkka (2010) Supply chain performance measurement in the manufacturing industry : a single case study research to develop a supply chain performance measurement framework 375. Marttila, Hannu (2010) Managing erosion, sediment transport and water quality in drained peatland catchments 376. Honkanen, Seppo (2011) Tekniikan ylioppilaiden valmistumiseen johtavien opintopolkujen mallintaminen — perusteena lukiossa ja opiskelun alkuvaiheessa saavutettu opintomenestys 377. Malinen, Ilkka (2010) Improving the robustness with modified bounded homotopies and problem-tailored solving procedures Book orders: Granum: Virtual book store http://granum.uta.fi/granum/ C378etukansi.kesken.fm Page 1 Tuesday, December 21, 2010 3:43 PM C 378 OULU 2011 U N I V E R S I T Y O F O U L U P. O. B . 7 5 0 0 F I - 9 0 0 1 4 U N I V E R S I T Y O F O U L U F I N L A N D U N I V E R S I TAT I S S E R I E S SCIENTIAE RERUM NATURALIUM Professor Mikko Siponen HUMANIORA University Lecturer Elise Kärkkäinen TECHNICA Professor Hannu Heusala ACTA UN NIIVVEERRSSIITTAT ATIISS O OU ULLU UEEN NSSIISS U Dayou Yang E D I T O R S Dayou Yang A B C D E F G O U L U E N S I S ACTA A C TA C 378 OPTIMISATION OF PRODUCT CHANGE PROCESS AND DEMAND-SUPPLY CHAIN IN HIGH TECH ENVIRONMENT MEDICA Professor Olli Vuolteenaho SCIENTIAE RERUM SOCIALIUM Senior Researcher Eila Estola SCRIPTA ACADEMICA Information officer Tiina Pistokoski OECONOMICA University Lecturer Seppo Eriksson EDITOR IN CHIEF Professor Olli Vuolteenaho PUBLICATIONS EDITOR Publications Editor Kirsti Nurkkala ISBN 978-951-42-9354-2 (Paperback) ISBN 978-951-42-9355-9 (PDF) ISSN 0355-3213 (Print) ISSN 1796-2226 (Online) UNIVERSITY OF OULU, DEPARTMENT OF MECHANICAL ENGINEERING; DEPARTMENT OF INDUSTRIAL ENGINEERING AND MANAGEMENT C TECHNICA TECHNICA