Fuzzy rule sets for enhancing performance in a supply chain network

Abstract

Purpose

This paper aims to develop a genetic algorithm (GA)‐based process knowledge integration system (GA‐PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network.

Design/methodology/approach

The proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items.

Findings

The application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality.

Research limitations/implications

In order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes.

Originality/value

Because of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA‐based approach can help to optimize the process parameters along the supply chain network.

Citation

Ho, G.T.S., Lau, H.C.W., Chung, S.H., Fung, R.Y.K., Chan, T.M. and Lee, C.K.M. (2008), "Fuzzy rule sets for enhancing performance in a supply chain network", Industrial Management & Data Systems, Vol. 108 No. 7, pp. 947-972. https://doi.org/10.1108/02635570810898017