Fuzzy rule sets for enhancing performance in a supply chain network

G. T.S. Ho, H. C.W. Lau, Sai Ho Chung, R. Y.K. Fung, T. M. Chan, Ka Man Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)947-972
Number of pages26
JournalIndustrial Management and Data Systems
Volume108
Issue number7
DOIs
Publication statusPublished - 19 Sep 2008

Keywords

  • Advanced manufacturing technologies
  • Fuzzy logic
  • Quality
  • Quality improvement
  • Supply chain management

ASJC Scopus subject areas

  • Management Information Systems
  • Industrial relations
  • Computer Science Applications
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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