(392m) Decentralized Supply Chain Management for Perishable Products

The effective management of perishable products is a pivotal challenge in both supply chain research and practice due to their inherently rapid quality degradation and sensitivity to ambient conditions. These characteristics amplify the need for tailored supply chain management (SCM) strategies that not only meet product demand but also preserve product freshness and reduce waste. Works in the literature emphasize the significance of decentralized control strategies in complex supply chain networks for addressing uncertainties, enabling localized decision-making, and resolving inter-firm coordination issues (Abdul-Jalbar et al., 2003; Saharidis et al., 2006). For instance, the work by Ketzenberg et al. (2008) illustrates the importance of information sharing in environments with slow-moving perishables to maintain product quality in the face of variable demand.

Decentralized SCM strategies may incur higher coordination costs and potential suboptimal decisions compared to centralized controllers (Abdul-Jalbar et al., 2003) but are essential when facilities operate autonomously and are only able (or willing) to share partial information. This is often the case in the supply chain of perishables, which comprise entities such as growers/producers, distributors and retailers that are owned and operated by different entities. However, studies focusing specifically on decentralized SCM of supply chains of perishable products are limited (Su et al., 2014; Dorneanu et al., 2023), underscoring a significant gap in current research.

Motivated by the above, we introduce a novel framework to decompose the optimal SCM a multi-echelon supply chain—comprising producers, distribution centers (DCs), and retailers—into interconnected subproblems that are tailored to the nuances of perishable goods. The proposed framework prioritizes shipment variables as the primary consensus decision points since these decisions inherently affect multiple facilities and interconnect different echelons. Meanwhile, local variables—such as inventory levels, temperature management, and waste generation—are managed autonomously to better accommodate the deteriorative nature of perishable products. By explicitly accounting for factors like ambient temperature sensitivity and the accelerated aging of inventory, the framework ensures that each echelon can adjust production scheduling, inventory control, and shipment planning to match localized conditions. Within the context of the Alternating Direction Method of Multipliers (ADMM), coordination among facilities is achieved using an augmented Lagrangian formulation with dual terms and 1-norm penalties that synchronize shipment flows between producers, DCs, and retailers.

Several case studies are provided. In general, the decentralized models incur higher overall costs than the centralized approach. However, they have superior computational efficiency—especially at larger scales—and the more realistic representation of autonomous facility operations justifies their adoption in the perishable goods context.

References

• Abdul-Jalbar, B., Gutiérrez, J., Puerto, J., & Sicilia, J. (2003). Policies for inventory/distribution systems: The effect of centralization vs. decentralization. International Journal of Production Economics, 81, 281-293.
• Ketzenberg, M., & Ferguson, M. E. (2008). Managing slow‐moving perishables in the grocery industry. Production and Operations Management, 17(5), 513-521.
• Saharidis, G. K., Dallery, Y., & Karaesmen, F. (2006). Centralized versus decentralized production planning. RAIRO-Operations Research-Recherche Opérationnelle, 40(2), 113-128.
• Dorneanu, B., Masham, E., Keykha, M., Mechleri, E., Cole, R., & Arellano-Garcia, H. (2023). Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models. Supply Chain Analytics, 4, 100043.
• Su, J., Wu, J., & Liu, C. (2014). Research on coordination of fresh produce supply chain in big market sales environment. The Scientific World Journal, 2014(1), 873980.