A mixed-model multi-objective analysis of strategic supply chain decision support in the Thai silk industry

Natawat Jatuphatwarodom, Dylan Jones, Djamila Ouelhadj

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Abstract

This paper presents a methodology for combined usage of data envelopment analysis (DEA), analytical hierarchy process (AHP) and extended goal programming (EGP) in order to provide managerial decision support. The methodology allows the three techniques to be used in a coordinated manner to give an enhanced level of holistic decision support. DEA is first used in a descriptive sense in order to provide information regarding the efficiency of a set of units. The AHP is then used in order to determine the importance of criteria arising from decision problem(s) related to the improvement of unit efficiency. Finally, EGP is used in a prescriptive sense in order to select a set of specific actions for improving unit efficiency. Two specific multi-objective situations arising from the Thai Silk industry are used as case studies for the proposed methodology. These involve supplier selection and inventory management system management in the presence of multiple conflicting goals and objectives. In the case studies, DEA is used to provide efficiency estimates of current suppliers and processes. AHP is then used in order to determine the relative importance of criteria for supply chain efficiency improvement. Adaptations are made to an automated inconsistency reduction algorithm in order to resolve high levels of inconsistency found. The relation between decision maker confidence and consistency is investigated. Finally, an EGP model is built in order to suggest improvement actions to the supply chain processes. Results are given for a set of eight Thai silk manufacturers and conclusions are drawn.
Original languageEnglish
Number of pages27
JournalAnnals of Operations Research
Early online date31 Jan 2018
DOIs
Publication statusEarly online - 31 Jan 2018

Keywords

  • Thai silk industry
  • goal programming
  • analytic hierarchy process
  • data envelopment analysis
  • supply chain management

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