Modeling resilient functions in perishable food supply chains: transition for sustainable food system development

Abstract

Purpose

Natural disasters cause serious operational risks and disruptions, which further impact the food supply in and around the disaster-impacted area. Resilient functions in the supply chain are required to absorb the impact of resultant disruptions in perishable food supply chains (FSC). The present study identifies specific resilient functions to overcome the problems created by natural disasters in the FSC context.

Design/methodology/approach

The quality function deployment (QFD) method is utilized for identifying these relations. Further, fuzzy term sets and the analytical hierarchy process (AHP) are used to prioritize the identified problems. The results obtained are employed to construct a QFD matrix with the solutions, followed by the technique for order of preference by similarity to the ideal solution (TOPSIS) on the house of quality (HOQ) matrix between the identified problems and functions.

Findings

The results from the study reflect that the shortage of employees in affected areas is the major problem caused by a natural disaster, followed by the food movement problem. The results from the analysis matrix conclude that information sharing should be kept at the highest priority by policymakers to build and increase resilient functions and sustainable crisis management in a perishable FSC network.

Originality/value

The study suggests practical implications for managing a FSC crisis during a natural disaster. The unique contribution of this research lies in finding the correlation and importance ranking among different resilience functions, which is crucial for managing a FSC crisis during a natural disaster.

Citation

Daultani, Y., Dwivedi, A., Pratap, S. and Sharma, A. (2024), "Modeling resilient functions in perishable food supply chains: transition for sustainable food system development", Benchmarking: An International Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BIJ-05-2023-0310