Risk Assessment in Inventory Models: The Case of Myopic Policies

Abstract

In the operations management literature, the financial risk in an inventory model is usually assumed to be captured by the (constant) weighted average cost of capital of the firm. This assumption is, at best, an approximation, since this cost depends on the risk of the cash flows, which, in turn, depends on the inventory policy. We study an inventory model with a generic inventory cost function where risk depends on the inventory decision made. In addition to the usual sources of variability, i.e., demand and market returns, we include additive and multiplicative financial noise functions and assess the impact of these on both the cost of capital of the firm and the optimal inventory policy. The model unifies and generalizes several existing models in the literature, from the economic order quantity model to a newsvendor model with endogenous risk. We find that risk is not in general a monotone function of inventory and that all sources of variability may have a significant impact on the cost of capital of the firm. In contrast, the impact of the financial noise functions on the optimal inventory policy is either non-existent (for additive shocks) or quite limited (for multiplicative shocks) if demand is stochastic, and irrelevant if demand is deterministic. We present two real-life applications to illustrate that accurately capturing risk may lead to significant improvements in firm value.