Impact of Bullwhip Effect in Quality and Waste in Perishable Supply Chain

Designing a sustainable fish closed-loop supply chain network under uncertainty

There is increasing attention to the sustainable development of supply chain (SC) and reverse logistics (RL) in the contemporary competitive economy, notably in the food sector, by scholars and stakeholders. This study investigates a sustainable closed-loop supply chain (CLSC) for fish due to its high value in the family food basket, its perishability, and the importance of waste product recycling. A multi-objective mathematical model is developed under uncertainty and sustainability criteria to optimize production rates with the aim of better distribution among different demand markets, total costs, social issues, and negative environmental effects (e.g., CO2 emissions and unused/waste products). A combination of exact, meta-heuristic, and hybrid meta-heuristic algorithms are used to solve the suggested model. Then, the optimal solutions are found using the Taguchi method by evaluating the best initial replies. The solutions are evaluated based on various performance metrics. The analysis of variance (ANOVA) and the "filtering/displaced ideal solution" methods determine the best solution approach. Moreover, a case study with a trout CLSC in Northern Iran is examined. In addition, the Lingo software utilizes the ε-constraint method to evaluate and check the performance of the algorithms under different levels of uncertainty. Finally, sensitivity analyses are carried out to confirm the efficacy of the proposed algorithms. The findings demonstrate the proposed network's outstanding consistency with the algorithms used and its application and efficiency.

Identification of the Critical Enablers for Perishable Food Supply Chain Using Deterministic Assessment Models

Today’s perishable food supply chains must be resilient to handle volatile demands, environmental restrictions, and disruptions in order to meet customers’ requirements. The enablers of the perishable food supply chain have not yet been explored. In this paper, a bibliometric systematic literature review has been conducted to identify the articles related to the perishable food supply chain. Next, with these identified articles, a map is created with bibliographic data using Vosviewer network visualization software, and then the enablers were identified by conducting keyword co-occurrence analysis. Later, a total interpretive structural modeling (TISM) is employed to analyze the interrelationships among enablers and then determine each enabler’s hierarchies, further representing them in a diagraph. Finally, the identified enablers are classified using cross-impact matrix multiplication applied to classification (MICMAC) analysis, and the graph is plotted. The results obtained from the deterministic assessment model provide the critical enablers for the perishable food supply chain. The obtained critical enablers and their hierarchies provide valuable insights for researchers in the context of perishable food supply chain for further study.