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Ghadge, A, Fang, X, Dani, S and Antony, J (2017) Supply chain risk assessment approach for process quality risks. International Journal of Quality & Reliability Management, 34 (7). 940 - 954. ISSN 0265-671X
Full text not available from this repository.Abstract
Purpose
The purpose of this paper is to proactively analyse and mitigate the root causes of the product and security risks. The case study approach examines the effectiveness of the fuzzy logic approach for assessing the product and process-related failure modes within global supply chain context.
Design/methodology/approach
The case study of a Printed Circuit Board Company in China is used as a platform for conducting the research. Using data triangulation, the data are collected and analyzed through interviews, questionnaires, expert opinions and quantitative modelling for some interesting insights.
Findings
Fuzzy logic approach for failure mode and effect analysis (FMEA) provides a structured approach for understanding complex behaviour of failure modes and their associated risks for products and processes. Today’s managers should conduct robust risk assessment during the design stage to avoid product safety and security risks such as recalls.
Research limitations/implications
The research is based on the single case study and multiple cases from different industry sectors may provide some additional insights.
Originality/value
The study attempts to mitigate the root causes of product and processes using fuzzy approach to FMEA in supply chain network.
Item Type: | Article |
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Additional Information: | The final version of this article can be accessed at https://www.emerald.com/insight/content/doi/10.1108/IJQRM-01-2015-0010/full/html |
Uncontrolled Keywords: | FMEA; case study; fuzzy logic; risk mitigation; supply chain risk |
Subjects: | H Social Sciences > H Social Sciences (General) |
Divisions: | Faculty of Humanities and Social Sciences > Keele Management School |
Depositing User: | Symplectic |
Date Deposited: | 15 Jun 2020 09:24 |
Last Modified: | 04 Nov 2020 14:58 |
URI: | https://eprints.keele.ac.uk/id/eprint/8146 |