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Business Processes Reengineering Based on Data Mining DENG Zhonghua, SUN Limei Center for Studies of Information Resources of Wuhan University, Wuhan, P. R. China [email protected] Abstract: As market competition intensifying, Business Process Reengineering (BPR) has become the inevitable choice for enterprises to develop their competitive advantages. Effectively promoting the implementation of BPR is an important way to scale the business for enterprises. As a new type of data-processing tools in dealing with massive data, Data Mining technology has an important role in transforming data into valuable information, supporting enterprises in business process reengineering, and so on. This paper discusses the data mining technology applied to support BPR with the help of the data analysis software -- SPSS. In this article presents the data mining technology in analyzing of critical success factors, identifying core business processes, optimizing the flow of information and feedback. Keywords: Data Mining, Data Warehouse, BPR, Core Business Process 1 Introduction Business Processes Reengineering was proposed by Dr. Michael Hammer in 1999. The core of BPR is considered as that facing to the fierce market competition, enterprises should strengthen the process control, keep re-think and reorganizing the existing business processes so as to improve the cost, quality, service and speed that are the elements reflecting enterprises competitive abilities [1]. The rapid development of information technology makes the content and function of the information systems have been considerable progress in breadth and depth. Information systems, management models and management thinking are gradual integrated, such as the ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), and SCM (Supply Chain Management) and so on. With the scale expanding, more and more companies become produce diversified and scale, the present business process of the company can no meet the needs of market competition, thus it is necessary for enterprises to implement BPR. However, in the current economic times characteristic of the “customers”, “change” and “competition”, enterprises are facing a data processing mixed and diversified. As a result, how to face the fierce and changing market competitive environment, diverse and frequently changing business data, have become the bottleneck of the constraints for enterprises in effective implement BPR[2]. Clearly, the traditional operation of the database cannot meet the Business Process Re-engineering the information needs; we need to import data mining. And how to extract the needed information from the large amounts of data and how to find the potential link between information, to guide production, to forecast production targets and to provide decision support for policy makers, all of these are the issue solved by data mining. Therefore, the decision of the enterprise business process reengineering based on data mining must enhance the competitiveness of enterprises. 2 BPR and its implementation 2.1 BPR and its characters BPR is the conjoint result of inner and outer environmental interval affections, but its direct driving force is for enterprises to meet customers’ changing needs much better. In today’s consumer-oriented era, rapid response to changing market environment and effectively satisfying customers with products and services are the fundamental pursuit of modern enterprises. The characteristics of BPR are as following: With the center of core business processes: During the process of BPR, we should be in the light of the enterprise’s development agencies to carrying out strategic restructuring. The present management 164 resources should be integrated to carding business processes and to establish core business processes with the center of customers demands. With the character of flat-organization[3]: BPR wants to achieve customer-oriented, flat level organization so that to increase control scope, to reduce the middle management level, and then to reflect the market more sensitive, to improve work efficiency and at the meantime, to reduce management staff. With the oriented of resources integration [4]: The competition of current era is not a single department competition or a single internal enterprise competition, it is unitary competition among corporations, between client-oriented and suppliers, which requires integrate resources appearing to be scattered to enable enterprise not only remain flexible in services but also access scale economies. With the target of customer satisfaction: Customers play a decisive role in enterprise’s development. Process reengineering takes customer demand into consideration to determine the content of business, in order to update business process thoroughly. Through the adjustment, information feedback, full participation and so on to get durative improvement. 2.2 The implementation steps of BPR There are 6 steps in implementation course of BPR, as follows [5-6]: Stage 1: Planning and preparation. The implementation of BPR must be in the consideration of the height of the strategy from enterprise executives: whether the present processes needed to be changed fundamentally? Which process is the core business process of the current enterprise? Only on these issues have a clear understanding, the follow-up process of reengineering can be conducted in an orderly manner. What’s more, corporate executives must set a clear goal of the reorganization process, set up a special BPR leading group to develop detailed project planning. Stage 2: Process diagnosis. It is analyzing the existing processes and its sub-processes to built model: analyzing the processes and finding the bottleneck of the process, discovering the existing problems in the process, and then anchor the business process reengineering accurately, which make preparations for the next stages. Stage 3: Process design and optimization. Process design and optimization, which also can be called the new process design, is on the basis of the analysis of the original process to built new processes prototype and support its IT infrastructure. Stage 4: Examining and approval. Examining and approval stage assess whether the re-designed processes have achieved the executives’ strategic goals. If this approval is not passed, then the flow has to go back to the second stage or the third stage on some conditions; but if this approval has adopted, then go to the next stage. Stage 5: Putting the new process into practice. During this stage, process reengineering is put into practice, formally implementation. Stage 6: Evaluation and feedback. After the formal stages, it is to assess the processes with the goals setting at the beginning of the project to see whether this reengineering has optimized the structure, whether this reengineering has improve the income, whether the new process has achieved the expected results. Besides, it is necessary to put some experiences and results of this BPR implementation, such as diagnosis, process design and optimization, and information feedback, into next BPR implementation for next reengineering. In addition, the Business Process Re-engineering is not an overnight project, one implementation does not mean the BPR completed, otherwise, the completion of the entire project needs continuous improvement, which means constant analysis and re-design. 3 The application of data mining Data Mining (DM), known as data exploitation or data excavating, is the process that extract connotative, useful information and knowledge which is unknown in advance from large incomplete, noisy, fuzzy random data[7]. It is the product combined from database technology, artificial intelligence, machine learning, statistical analysis, and fuzzy logic. 165 As the continue expansion of enterprise business scale, the volume of data in the organization is very huge but in contrast, the valuable information is really limited. And most of the existing databases remain in the query, retrieval stage; abundant knowledge hidden in the database is far not fully explored and used. Therefore it is stared in the face that getting decision-making information conductive to business operation and competitiveness improvement from the databases by data mining to support the business process reengineering. In the shallow level, it makes use of the query, retrieval and reporting functions included in the existing database management system, combined with multi-dimensional analysis and statistical analysis to carry through on-line analytical processing (OLAP), from which statistical analysis of information available for decision-making can be obtained; In the deeper layer, it finds the unprecedented and implicit knowledge from the database. Data mining technology has the following characteristics [8-10]: (1)The scale of processed data can be very large, which can reach to TB, GB level. In addition, it can make rapid and accurate response to the data that is dispersive sourced, large scaled, nonstandard formatted and frequent updating to provide decision support, and this data can not be processed by the traditional database management methods. (2)It can study independently in accordance with the requirements of users, to complete imprecise requirements for users. (3)In data mining, the founding of rules is based on the statistics of the rules. Therefore, the discovered rules need not to be applied to the whole data, and it can be seen as effective at the point of reaching a certain threshold. That means a lot of rules can be found through the data mining technology. Generally speaking, data mining consists of three main stages which are data preparation, mining operation, and interpretation of the results [11]. In the stage of data preparation, the work needed to do is to resolve the semantic ambiguity between phases, process the omission data, and clean dirty data. Mining operations is the course that hypothesis producing, synthesis, verification and amendment diffusing, aiming at building a model. Expression and interpretation of results stages are to express the extracted useful information in accordance with the purpose of decision-making for the user. The data mining process is not linear, but the repeated cross-cutting cycle. The commonly used data mining technologies includes sequence analysis, classification analysis, forecasting, clustering, association rules analysis, rough-and-theory methods. This paper used the cluster analysis provided by SPSS mathematical statistics analysis software to identify the core business processes of the enterprises to support the business process reengineering. Basing on the principle of “Birds of a feather flock together”, Clustering refers to the process that gather the samples which are not grouped and in a natural free state into different groups, and describe each of those groups [12]. Its purpose is to make the samples similar to each other of the same group and the samples belong to different groups not to similar as possible, that is, the smaller the similarity between different groups and the greater the similarity in the same group. Different from analysis of classification, in the process of clustering it isn’t known that how many and what groups the samples will be divided into in advance, neither or the rules to define groups. Its purpose is to find the function relationship between data attributes. After bringing the data mining techniques into business process reengineering, in the planning and preparation stage, decision-making information required by the high-level managers and related indictors are delivered to the data mining group, and then the group diagnose the original business processes and determine the enterprise’s core business processes basing on the analysis of key success factors. In short, data mining technology runs through the entire business process reengineering, and meanwhile the implementation of data mining can be uncovered by the feedback of effect of process reengineering. The steps of implementation can be described in the Figure 1. 166 preparation preparation 1. strategic decision 2. project beginning key factors 3.process diagnosis implemention core business process Data Mining 4. design and optimization auditing 5.put into practice Project over 6.system switching data warehouse 7.evaluation and feedback Figure 1: The implementation steps of BPR, based on DM technology 4 Case study 4.1 Background of the case A key high-tech enterprise mainly produces fine chemical materials. Through 12 years development, it has possessed a quite big scale for sale and has become a leading enterprise with fairly good profitability in industry [13]. With the scale expansion, the enterprise is now promoting two strategic changes with full strength by effective managements, one change from a median-small enterprise to a big one, and the other from a regional enterprise to a global one. However, the problems come continuously, such as managements getting out of touch, failure on decision information’s delivery on time, decrease in response to customer demands, etc. In other words, the former business processes have become unable to meet the demands for the scale expansion of the enterprise itself, with quantities of problems appearing in the business processes. This is an enterprise that mainly produces fine chemical materials. The stock processes are like this: Sale Department’s receiving the customer’s order—Quality Supervision Department’s providing the sample—to send the sample to warehouse for material choice—to produce the goods in workshops--Logistics company’s delivery and distribution. During the expansion, the enterprise often receives the customers’ complaints that the delivery time is too long so that the company is unable to achieve the promise of sending the goods in 2 days after the cash arrives. 4.2 Former solutions To solve the problem, the broad of directors retained a professional consulting company to reform the company’s management. Through overall inspection to the company for two months, the programmer brings up that the original stock process should be rearranged to solve some key problems such as the speed, the costs and the service. Especially the speed problem should be solved, only by this could add to the customers’ satisfaction. (1) Problems Through the analysis of the existing business process model of the enterprise, problems are mainly 167 concentrated in the following areas: From a managerial point of view, it is lack of professional management persons, management system is imperfect, top management has never seriously considered the optimization of business processes or the integration of information resources. In accordance with their respective management experiences and habits, various departments are lack of appropriate monitoring mechanisms, in which information feedback seriously lagged behind and the phenomenon of inter-departmental shift responsibility often occurred. In the current market competition, market changes more quickly, customer needs more diversified, under the condition of the original inefficient management system, the too-long reaction cycle undermined the competitiveness of enterprises. From the perspective of business operations, sales and workshop processing sector are seriously out of line, sales department lacks the capabilities of market research and forecast, which can not provide the market demand forecasts, making workshop processing sectors not be able to adjust production and often resulting in delays in delivery times. In addition, the workshop production sector and sales sector lack of information communication, not sharing information and the information can not be conveyed as soon as possible also lead to the delays in delivery times. From the view of enterprise organizational structure, the current organizational structure is small, while its senior management is large, and its reaction to market is insensitive, work efficiency is low. By the way, there were also several problems, such as no customer demand-oriented, non-core business processes and so on. (2) Process diagnosis After investigation and analysis of the factors of each department one by one, main factors affecting the speed are concluded: Sale Department: 1) speed of checking the orders; 2) speed of checking the cash; 3) speed of sending the notice for stock to the Quality Supervision Department. Quality Supervision Department: 1) getting samples to match the orders; 2) providing the parameters of matching techniques; 3) sending the parameters to warehouse for records. Warehouse management Department: 1) choosing the materials according to the technique parameters; 2) preparing the materials according to the order number; 3) sending the materials to workshops for records. Workshop: 1) speed of stopping producing semi-finished goods to spare the machines; 2) speed of the processing machines; 3) speed of packing the finished products; 4) speed of informing the Quality Supervision Department for scientific examinations. Connecting with the Logistics company: 1) Quality Supervision Department’s informing the Sale Department to arrange goods delivery; 2) Sale Department’s informing the Logistics company to load goods; 3) Logistics company’s delivery and distribution. 4.3 Identify core business processes using data mining (1) Principal component analysis Critical success factors are the key management points, which ensure the organization of the implementation of development strategies and achieve its objectives. According to the analysis of the enterprise through the critical success factors, we can understand where the enterprise's key business process is [14]. Business process reengineering aims to faster and better satisfy customer’s needs. Therefore, when analyzing the critical success factors, we would convert customer satisfaction to response time, and set the various departments, the people number of departments, work content, the transmission time of information between departments, and departments’ preparing production time as ordered factors. According to the key success factors analysis, the people number of departments and departments within the normal preparing production time can be attributed to the department of internal factors. And then the SPSS11.5, statistical analysis software of data mining, is used to do an analysis of the main factors. The main factors affecting response time are concluded, as following: Sales department: the speed of checking orders and ordered a notice served on the Quality and Technical Supervision Bureau. 168 Store management department: the speed of preparation of materials and the time of sending the preparing materials to workshop. Workshop: stop the production of semi-finished, the speed of releasing the plant; the speed of finishind packing and notify quality scientific examination. (2) Identification of the core business processes After the analysis, we can find that critical success factors can be affected by many factors. However, by comparing these factors, we can find out where the enterprise’s larger issues are, whether it became bottleneck of seriously impacting the development of the enterprise’s scale economies, which leads to determine the business processes in the need to restructure the core business processes. Here, we are doing the cluster analysis from the efficiency, effectiveness, efficiency, encoding the critical factors from the previous analysis of workers in several departments and preparing time respectively into 1, 2 in SPSS; encoding sales, quality inspection branch, store management branch, the workshop, the TAC before the convergence of logistics companies into another variable y of 1, 2, 3, 4, 5. In the analysis of core business processes, it turns out to be the workshop and warehouse. 5 Summaries By means of data mining technology, we can quickly identify the critical success factors in the many impact factors and then re-establish its core business processes. On the one hand, two months of workload of professional consulting firm can be completed efficiently within two weeks, and this two weeks mainly for information-gathering personnel to collect the factors that may affect the enterprise; On the other hand, it is higher accuracy that using data mining technology to identify key factors and core business processes. We can learn from the case above, five main factors were obtained by using the old method, but only three by data mining analysis. It can greatly reduce the workload and more efficiently in identifying the core business processes latter. It is a try to using data mining in BPR, using cluster analysis in determining the core business processes. Since the case in this paper is belong to small and medium enterprise, the complexity and the diversity of its data is limited, its integrity of function is not embodied. But the advantages in its business processes reengineering reflected well: data mining technology was used to determine the critical success factors and core business processes, which are two important contents in BPR. References [1] JingZhou Rong. What is BPR? Beijing: Beijing Financial and Economic Press. 2004 [2] GuoLiang Li. Process winning: business process optimization and reengineering. China Development Press. 2005 [3] RanRan Sun. BPR of Water & Electric Power in Beijing Electric Power Company [nh]. 2007 [4] XiaoBo Tang. Management Information System. Science Press. 2005 [5] Li Chen, XiaoHui Chen. Analysis of BPR Implementation for a Machinery Maintenance Enterprise [J]. Industrial Engineering Journal, 2008(1) [6] YAO Juan, XU Da-hao. New Trend of BRP [J], Commercial Research, 2006(7) [7] Jiawei Han, Micheline Kamber. Data mining: Concepts and techniques. High Education Publishing Company, 2001 [8] Shan Wang. Data Warehouse and On-Line Analytical Processing. 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