توسعه یک سیستم هوشمند مدیریت کیفیت با استفاده از قوانین انجمن فازی

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 supporting 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 overcome 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.

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 collaboration for making right decisions all the time in every process involved. Although numerous empirical and scientific 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 disciplines have had their own information systems for quality control and monitoring in their own specific process. However, the fact that quality improvement is a distributed and cooperative problem-solving activity has been neglected. Therefore, attention should be paid to capturing the distributed 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 of fuzzy rules, from centralized process data stored as quantitative values. An intelligent prototype system called the Intelligent Quality Management System (IQMS) has also been developed to support knowledge discovery within the workflow of the enterprise. This paper is divided into four main sections. Section two is a literature review conducted on existing quality improvement methods adopted in the industries by different kinds of intelligent systems, knowledge discovery technologies and the data exchange standard. The basic algorithm and the architectural structure of IQMS are described with the detailed explanation of two main modules in Sections 3 and 4. Final section concludes the entire paper by presenting the key findings and future work.

This research provided a generic methodology for the development of an intelligent quality management system with knowledge discovery and with the cooperative ability for monitoring the process effective, efficient, and adaptable. It will help to achieve dramatic improvements in critical contemporary measures, such as quality, cost, time of delivery, and utilization. Generally, process engineers need to assemble a group of experts to identify and evaluate improvement opportunities. The proposed i-PM algorithm provides an effective mechanism for doing this as it can describe how well the process is doing and set the stage for process analysis and improvement. The proposed PQL enables efficient data to be exchanged instantly with corporate headquarters and other divisions. This is a significant contribution to standardizing the process data exchange format for identifying potential process problems so that improvement opportunities can be reviewed effectively. Further research on the structural configuration of the system is needed in order to further enhance its benefits. In general, this model paves the way for a novel approach to deal with total quality management by using artificial intelligence with the proposed i-PM algorithm. It is recommended that researchers utilize this innovative information technology to create value for customers, with TQM principles, that ultimately can help the organization to provide customer satisfaction.