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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.”
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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.
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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
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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.
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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
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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
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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
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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.
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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.
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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
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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.
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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
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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:
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–
–
–
–
–
–
–
–
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
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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:
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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
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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.
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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
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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.
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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.
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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
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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
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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
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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.
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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.
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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
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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.
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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.
–
–
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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
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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.
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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).
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–
–
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:
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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.
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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.
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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.
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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
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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
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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.
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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.
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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,
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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
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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.
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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:
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–
–
–
–
–
–
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.
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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
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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
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