Development of an intelligent business process management decision support system for mould manufacturer

Abstract

Manufacturers outsource mould manufacturing processes to professional subcontractors so that it is able for them to focus on core competence of their businesses. In general, a mould is manufactured in make-to-order (MTO) mode in which each product is unique but the production sequence is product oriented. Thus, it involves the collaboration of various parties like different internal functions, subcontractors and customers. In order to plan and manage the whole process that fits customers' requirements within a short lead time, mould manufacturers need to gather different production information from all parties. However, such information is usually abundant in quantity and scattered in various production sites. Therefore, it is difficult to plan and make decision within a short period of time. Hence, a system which helps (i) capture real-time production information, (ii) facilitate information sharing, and, (iii) support decision making in planning, scheduling and production operation, becomes valuable. In order to enhance the utilization of production resources and reduce the production lead time, an Intelligent Business Process Management Decision Support System (IBPMS) is proposed. The IBPMS consists of four modules: Information Collection module, Data Warehouse module, Scheduling module and Decision Support module. In the Information Collection module, Radio-Frequency Identification technology is adopted to capture real-time production information such as, staff availability, machine availability and product status information. The captured information is then stored in the Data Warehouse module and passed to the Scheduling module which is responsible to rearrange the master production schedule using an artificial intelligence technique, i.e., Genetic Algorithm. In the Decision Support module, Case-Based Reasoning technology is adopted to provide decisions, such as who to outsource, overtime level, or make a reschedule plan, for solving production planning and scheduling problems. To validate the feasibility of IBPMS, a case study has been conducted by adopting it in a local mould manufacturing company. A performance analysis showed that the performance of production planning and scheduling activities was greatly enhanced. In summary, the value of this research is in two folds: (i) an effective and efficient system, IBPMS, was developed to minimize the communication time among different parties during the manufacture of a mould; (ii) the decision making process when formulating all production and scheduling activities of a mould was made both efficient and effective by the appropriate integrative use of artificial intelligence.