Abstract for: Algorithmic Intervention to Mitigate Inventory and Ordering Amplification in Multi-Echelon Supply Chains
The ‘bullwhip effect’ is a classic, yet persisting, problem with reverberating consequences in inventory management and refers to how forecast errors and safety stock builds yield increasing amplitudes in both orders and on-hand inventory positions the further one moves away from a source of order variability. In this paper, the author develops algorithmic approaches to mitigating bullwhip using simulation modeling and then interprets the results in the context of existing models of human heuristics in ordering decisions. In all methods developed, inventory and ordering oscillations are minimized in the simulated environment. The parameters that emerge in the developed algorithm are mapped to previously observed modes of behavior that mitigate the effects of bullwhip. The resulting algorithms act in a manner analogous to those exhibiting high levels of trust within the supply chain, coupled with a cautious approach to information signals outside of the supply chain. Desired stock levels of the resulting algorithms approach those found in optimal base-stock replenishment policies. Finally, it is observed that the algorithm does not fall prey to supply line under-weighting, and can act to offset the ordering decisions that typically result in bullwhip in a simulated model of a multi-echelon supply chain.