A typology of supply chain resilience: Recognizing the multi- capability nature of proactive and reactive contexts

Faruquee, Murtaza; Paulraj, Antony; Irawan, Chandra Ade

Abstract

Even though resilience has received ample attention in recent literature, there is still a dearth of research when it comes to theorisation of supply chain resilience capabilities. Against this background, we aim to develop a framework for supply chain resilience capabilities based on proactive and reactive contexts. Apart from using ANOVA, we also perform a nuanced analysis using the response surface methodology. The analysis is done based on a survey dataset collected from 291 manufacturing firms. The results indicate that different combinations of proactive and reactive resilience capabilities can have a differential impact on the performance indicators. Although both proactive and reactive capabilities are essential for ultimate resilience strategies, supply chains might initially benefit more from reactive capabilities than proactive ones. The comprehensive framework proposed in our research addresses a vital gap in current supply chain resilience theorisation and could pave the way for further well-informed research on the evolving research domain. Moreover, this framework could serve as a powerful tool for supply chain managers to design and plan the development/improvement of resilience capabilities in collaboration with supply chain partners. They will be able to easily evaluate the current condition as well as targets for resilience capabilities.

Citation

Faruquee, M., Paulraj, A., & Irawan, C. A. (2023). A typology of supply chain resilience: Recognizing the multi- capability nature of proactive and reactive contexts. Production Planning and Control, 35(12), 1503-1523. https://doi.org/10.1080/09537287.2023.2202151

• Journal Article Type: Article
• Acceptance Date: Mar 21, 2023
• Online Publication Date: Apr 20, 2023
• Publication Date: Apr 20, 2023
• Deposit Date: Sep 17, 2024
• Publicly Available Date: Oct 15, 2024
• Journal: Production Planning and Control
• Print ISSN: 0953-7287
• Electronic ISSN: 1366-5871
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 35
• Issue: 12
• Pages: 1503-1523
• DOI: https://doi.org/10.1080/09537287.2023.2202151
• Keywords: Resilience, Typology, Framework, Polynomial regression, Performance
• Public URL: https://nottingham-repository.worktribe.com/output/39719496
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/09537287.2023.2202151