A real-time decision support framework to mitigate degradation in perishable supply chains. (December 2020)

• Record Type: Journal Article
• Authors: Xu, Xinglu; Guo, Weihong Grace; Rodgers, Mark D.
• Is Part Of: Computers & industrial engineering. Volume 150(2020)
• Publication Date: 2020-12
• Subjects: Perishable supply chains -- Real-time decision making -- Vehicle routing -- Inventory allocation -- Quality control -- Optimal environment factors; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106905
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We investigate how the quality of perishable products degrades through the journey. Real-time sensing data are analyzed to aid dynamic decisions. Quality indices are defined to reflect degradation in ever-changing environment. Dynamic environmental control can effectively reduce quality loss and total cost. Pre-cooling and pre-humidification are helpful to slow down the degradation. Abstract: Perishable products are highly susceptible to degradations in quality due to unstable environmental conditions. From an operations management perspective, retailers may be left with spoiled products with no salvage value. To mitigate these challenges, we develop a step-by-step analytical framework that enables managers to make dynamic tactical decisions for perishable products, to preserve their quality as they move through a supply chain network. First, by identifying key quality indicators and environmental factors, a data-driven quality degradation prediction model under a stochastic environment is established. The combined impact of volatile environmental factors on various quality indices are studied. Next, we utilize this degradation model as an input to a preliminary planning model, which determines a vehicle routing schedule and inventory quality targets throughout the network at least cost, subject to retailer service levels. Lastly, using the optimized supply chain network design, we dynamically optimize the environmental controls throughout the transportation process to ensure product quality. By applying this framework to a fresh apple supply chain case, we obtain valuable insights on product quality degradation along the supply chain, which aids practitioners in developing targeted solutions that enhance quality levels economically. With extended analyses, we observed the reaction of different quality indices to environmental variations, and the benefits brought by establishing initial environment conditions in delivery vehicles.