Determining maximum shipping age requirements for shelf life and food waste management
Production and Operations Management · 2023 · Volume 32 · Issue 7
Abstract
Products approaching the end of their shelf life on retail store shelves are more likely to result in food waste. For this reason, manufacturers establish shipping policies related to the age of the products that leave their warehouses, that is, they set a maximum age beyond which shipping to retail stores is no longer allowed. In practice, most existing policies are simple one-size-fits-all rules that do not accommodate the varying characteristics of the products. We offer a framework that manufacturers can use to determinemaximum shipping age thresholdsbased on a Markov chain model, where the objective is to maximize profit, net of the cost of expiration at the warehouse and retail stores. We derive analytical insights about the impact of a shipping age threshold on food waste in the supply chain and obtain sufficient conditions under which a maximum shipping age threshold is suboptimal within the class ofShip-Oldest-First(a.k.a.First-In-First-Out) issuing policies. We also numerically investigate the relationship between different system parameters (e.g., demand rate, waste cost at warehouse and retail, warehouse inventory, total shelf life) and the optimal shipping age threshold. Using real data from our industry collaborator, we compute the optimal shipping age thresholds at the stock-keeping-unit (SKU) level for over 450 products and find that 9–10% of the SKUs currently have suboptimal shipping age thresholds. This presents an opportunity to improve profits by up to 8.7% and reduce food waste by up to 14.7%. These improvements correspond to up to $292,561 savings and 1846 truckloads of waste reduction annually. Our framework can be adopted by any firm and satisfies a much emphasized need in industry to control food waste through shelf life management.
Keywords
consumer packaged goods, data-driven research, food waste, inventory issuance, Markov chain analysis, perishable inventory, practice-driven research
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