Identifying and managing supply quality risk. Issue 3 (14th November 2016)

• Record Type: Journal Article
• Authors: Zsidisin, George A.; Petkova, Boyana; Saunders, Lance W.; Bisseling, Mark
• Is Part Of: International journal of logistics management. Volume 27:Issue 3(2016)
• Page Start: 908
• Page End: 930
• Publication Date: 2016-11-14
• Subjects: Sourcing -- Risk management -- Quality; Business logistics -- Management -- Periodicals; Physical distribution of goods -- Management -- Periodicals; Web sites; 658.5
• DOI: 10.1108/IJLM-02-2015-0043
• Languages: English
• ISSNs: 0957-4093

Abstract

Abstract : Purpose: The purpose of this paper is to provide a framework for identifying and managing supply quality risk (SQR). Design/methodology/approach: The research method began with a literature study to form a grounded model of how organizations identify and perceive SQR and associate various supplier quality management practices (SQMPs) with SQR sources. The second phase consisted of structured interviews at three companies in the food machinery industry in order to refine these concepts and examine causal relationships. Findings: The findings from this study indicate that firms may be more likely to implement integrative supply chain practices when supplier or component sources of SQR are considered to be a significant threat. Conversely, market sources of SQR were generally not perceived as being significant, and therefore do not warrant as much direct intervention in their management. Research limitations/implications: Most previous studies on supply chain risk focus on delivery disruptions. However, there is a lack of knowledge on identifying, assessing, and managing supply risk associated with quality. By addressing these issues and outlining future research directions the authors help provide a starting point for contributing to this line of study in supply risk theory and practice. Practical implications: The framework developed in this paper can aid supply chain professionals in understanding what constitutes SQR and providing insight to approaches for managing this form of supply risk. Originality/value: This is the first empirical study that the authors are aware of that links various sources of SQR to specific SQMPs.