Analysis of fuzzy applications in the agri-supply chain: A literature review

With the increasing global demand for fresh food, the importance of the mass agri-producing countries has also increased. Agri-production has undergone a significant transformation from traditional practices to precise practices with the help of new technologies such as the IoT, big data, and GIS. However, the impact of such precision on the overall supply chain is insufficient due to the low adaptability of these expensive technologies. Fuzzy logic, known for being able to handle uncertainty in different fields of science and technology, may be able to address the highly uncertain factors of the agri-supply chain, such as the soil content, rainfall, humidity, and production and yield prediction. Thus, this study reviews fuzzy applications in the agri-supply chain considering land suitability, production techniques, irrigation, cold storage deficiencies, transportation, waste management, environmental and sustainability issues, and drought management and establishes an integrated framework. The integrated framework, which aims to analyse the overall supply chain performance, finally concludes that there is a lack of efficient knowledge-based models in the domain. Thus, using collaborative fuzzy applications with big data and GIS with a higher degree of heuristic and meta-heuristic simulations in the agri-supply chain can develop more robust models. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study explores the application of fuzzy logic in the agri-supply chain by considering factors such as land suitability, production techniques, irrigation, and transportation, and establishes an integrated framework. It points out the lack of efficient knowledge-based models in the field at present.