Multi criteria decision analysis for improving cold chain sustainability

Cold chains are responsible of significant energy requirements at each stage of the chain (i.e., refrigerated transport, processing, and storage) with large potentials for savings. An accurate refrigeration is required for the optimal preservation of perishable goods. A critical aspect is represented by the trade-off between the energy required for refrigeration and quality issues. The present study aims to assess the improvement that can be reached in terms of sustainability of cold chains, while varying the temperature set for the raw material, the finished product, and the lot size (which impacts the storage time). Since sustainability can be achieved while optimizing different aspects, a multi criteria decision analysis (MCDA) is implemented. The MCDA is a well-recognized approach for solving complex issues and supporting the decision-making process, which allows selecting the most optimal choice determined primarily by a weighted set of criteria. The TOPSIS approach has been selected since it is recognized as a comprehensive method that gives a complete ranking of alternatives and avoids complex evaluation of each criterion in the selection process and the need for a large quantity of information in assessing these criterions. The MCDA analysis is based on the results coming from two supply chain models developed during the H2020-ICCEE project: i.e., the energy impact model which assess the energy flow and the quality losses, and the life cycle assessment model, which evaluates the environmental performance. In particular, the criteria used for the evaluation of the different scenarios are the specific energy consumption, the quality losses along the cold chain, the global warming potential, the cumulative energy demand, and the water scarcity. From the insights of the case studies, it is evident how the MCDA analysis is relevant for cold chains due to the double effect of refrigeration: i.e., increased quality at the cost of increased energy consumption. The proposed TOPSIS method can, thus, be useful for prioritizing energy efficiency measures