Knowledge-based customization of enterprise systems for business process improvement

Pao Yue-kong Library Electronic Theses Database

Knowledge-based customization of enterprise systems for business process improvement

 

Author: Tsoi, Siu-ki
Title: Knowledge-based customization of enterprise systems for business process improvement
Degree: M.Phil.
Year: 2005
Subject: Hong Kong Polytechnic University -- Dissertations.
Reengineering (Management)
Knowledge management.
Decision support systems.
Department: Dept. of Industrial and Systems Engineering
Pages: xi, 155 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1896786
URI: http://theses.lib.polyu.edu.hk/handle/200/119
Abstract: The rapid change of the business environment has had a dramatic effect on the organization and the information infrastructure of enterprises. Many companies are attempting to implement business process improvement (BPI) projects and streamline the business processes to realize cost reduction, quality improvement, and the ability to respond faster to business problems and opportunities. Many managers recognize the advantages of the adoption of new business processes but they may worry about the influences, benefits and drawbacks after the implementation of the new processes or systems in their company. Moreover, it requires a long and tedious implementation that usually involves many people, considerable time, and it is trial-and-error in nature. In addition, the selection and customization of the new applications and technologies is often not easy because there are many software packages available in the market. However, most of them are too complicated and only a small part of the functions in the software package is suitable for direct adoption. Therefore, the new system may not integrate well with the existing system or it may not fit into the current practice of the company. In this thesis, the knowledge-based approach is proposed for managing, storing, retrieving and distributing the valuable experience of past BPI projects in the consultancy companies. The knowledge involves planning, integrating and configuring different components of an enterprise system. Such knowledge needs to be continuously updated and made readily accessible to other users. Based on the knowledge-based approach, the consultancy companies tend to adopt the strategy of "Sharing existing knowledge better". A knowledge-based customization system (KBCS) is proposed to enhance the business process in an enterprise. The KBCS is built based on knowledge-based system architecture and incorporates various artificial intelligence (Al) technologies such as a rule-based expert system and case-based reasoning (CBR). Three modules are included in the KBCS: the Business Analysis Module (BAM), the Process Improvement Module (PIM) and the Customization Module (CM). The system allows the capture of the valuable experience and tacit knowledge involved in planning, integrating and configuring different components of an enterprise system. Therefore, the companies can generically and rapidly develop the new business processes and customize the enterprise systems which best fit the current practice and business flow of the company. In order to verify the suggested methodology, a case study was done in Kaz (Far East) Limited (formerly Honeywell Consumer Products (HK) Ltd.) so as to validate the performance of KBCS. The results show that the KBCS can generate a BPI solution as well as the detailed configuration of the components for the implementation of the scan-based warehouse management system for the company. It significantly reduces the time for the design and implementation of the whole project as compared with the traditional BPI method. With the successful development of the platform, it is believed that the time, the cost and the risk for developing a complex enterprise system can be significantly reduced. As a result, a total solution can be determined for the company based on their strategic objectives and existing resources. All potential problems and conflicts can also be predicted without the need for conducting an expensive trial implementation.

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