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Improvement in the food losses in fruits and vegetable supply chain - a perspective of cold third-party logistics approach

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  • Raut, Rakesh D.
  • Gardas, Bhaskar B.
  • Narwane, Vaibhav S.
  • Narkhede, Balkrishna E.

Abstract

The maximum amount of food losses in the fruits and vegetable supply chain due to quality and mismatch between supply and demand. As per the Global Agenda Council on Logistics and Supply Chains indicated that fruits and vegetables food losses due to improper handling and lack of proper cold transportation such as cold-logistics facilities/providers or inadequate infrastructure. In this article, a unique fuzzy Multi-Criteria Decision Making approach is proposed for improving the food losses through cold-third party logistics providers (CTPLs) evaluation and selection process. Through a literature survey and expert opinion, five criteria and thirty sub-criteria were identified for reducing the food losses in fruits and vegetable supply chain. The proposed tool use of fuzzy-Decision making trial and evaluation laboratory tool for selection and evaluation the priority weights of the factors and fuzzy-analytical hierarchy process tool assessing the best CTPLs according to factors. The result shows that ‘Refrigerator and loading capacity’ and ‘Knowledge and Information technology management’ were most significant in the selection of CTPLs. The findings of this paper are anticipated to guide managers of the food industry, CTPLs, and government agencies in formulating of strategies for the practical food supply chain. Future scope includes extending the study for other developing countries, validating the proposed methodology, and improving the reliability of the model.

Suggested Citation

  • Raut, Rakesh D. & Gardas, Bhaskar B. & Narwane, Vaibhav S. & Narkhede, Balkrishna E., 2019. "Improvement in the food losses in fruits and vegetable supply chain - a perspective of cold third-party logistics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
  • Handle: RePEc:eee:oprepe:v:6:y:2019:i:c:s2214716019300028
    DOI: 10.1016/j.orp.2019.100117
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    8. Kalyana C. Chejarla & Omkarprasad S. Vaidya, 2024. "A hybrid multi-criteria decision-making approach for longitudinal data," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1013-1060, September.
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