Single-period stochastic demand fulfillment in customer hierarchies

Abstract

To maximize profits, given limited resources, companies commonly divide their overall customer base into different segments and use allocation policies to prioritize the most important segments. Determining the optimal allocations is challenging due to supply lead times and uncertain demand. Another challenge is the multilevel hierarchical structure of the customer segments. In general, available quotas are not determined by a central planner with full visibility of all individual customer segments but rather result from a sequence of allocation steps with an increasing level of granularity. In this paper, we investigate this sequential allocation process. Specifically, we identify crucial information for making allocation decisions in customer hierarchies and propose decentralized methods that lead to near-optimal allocations while respecting the requirements for information aggregation. We evaluate the methods by comparing their information requirements and reflect on the role of information sharing in hierarchical allocation decisions.