Development of an intelligent quality management system using fuzzy association rules H.C.W. Lau a, * , G.T.S. Ho a , K.F. Chu a , William Ho b , C.K.M. Lee c a Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong b Operations and Information Management Group, Aston Business School, Aston University, Birmingham B4 7ET, United Kingdom c Division of Systems and Engineering Management, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore Abstract In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization’s success. In recent years, attention has been focused on intelligent systems which have shown great promise in sup- porting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to over- come the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ‘‘distributed process mining” feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Production quality; Quality improvement; Quality information system 1. Introduction High-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way for an enterprise to survive. In fact, to achieve high-quality is not the responsibility of any one person or functional area; it is everyone’s duty in the entire corporation. Poor process decisions from any individual may lead to poor customer satisfaction. The ultimate goal is to achieve better collabo- ration for making right decisions all the time in every process involved. Although numerous empirical and scien- tific approaches have been developed in the field of quality management, past research has not addressed this issue well enough, nor has actual practice managed to optimize the integrated workflow in order to make sure that all the participants have the possibility to act successfully in their processes. Traditionally, various functional disci- plines have had their own information systems for quality control and monitoring in their own specific process. How- ever, the fact that quality improvement is a distributed and cooperative problem-solving activity has been neglected. Therefore, attention should be paid to capturing the dis- tributed process data to support knowledge discovery within the workflow of the enterprise. The purpose of this paper is to present a methodology for discovering the hidden relationships among all the process variables involved in a distributed and automatic manner. The iterative Process Mining (i-PM) algorithm based on the concept of fuzzy set and association rule method is proposed to extract interesting patterns in terms 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.12.066 * Corresponding author. Tel.: +852 2766 6628; fax: +852 2362 5267. E-mail address: mfhenry@inet.polyu.edu.hk (H.C.W. Lau). www.elsevier.com/locate/eswa Available online at www.sciencedirect.com Expert Systems with Applications 36 (2009) 1801–1815 Expert Systems with Applications