Identifying critical enablers and pathways to high performance supply chain quality management

Chinho Lin, Chu hua Kuei, Kang Wei Chai

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)


Purpose: The aim of this paper is threefold: first, to examine the content of supply chain quality management (SCQM); second, to identify the structure of SCQM; and third, to show ways for finding improvement opportunities and organizing individual institution's resources/actions into collective performance outcomes. Design/methodology/approach: To meet the goals of this work, the paper uses abductive reasoning and two qualitative methods: content analysis and formal concept analysis (FCA). Primary data were collected from both original design manufacturers (ODMs) and original equipment manufacturers (OEMs) in Taiwan. Findings: According to the qualitative empirical study, modern enterprises need to pay immediate attention to the following two pathways: a compliance approach and a voluntary approach. For the former, three strategic content variables are identified: training programs, ISO, and supplier quality audit programs. As for initiating a voluntary effort, modern lead firms need to instill "motivation" into a supply chain quality system. Practical implications: The findings based on the abductive model reveal numerous strategic and tactical enablers, key sequences to move firms from their current situation to their preferred one, and critical opportunities for supply chain-wide quality system designs. Originality/value: This study will be of great value to supply chain policy makers, supply chain operators, and decision makers in lead firms in a supply chain setting and their channel partners. The proactive use of the authors' proposed research procedure is indispensable to effective supply chain quality planning.

Original languageEnglish
Pages (from-to)347-370
Number of pages24
JournalInternational Journal of Operations and Production Management
Issue number3
Publication statusPublished - 2013 Feb

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

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