Sustainable Inventory with Robust Periodic-Affine Policies and Application to Medical Supply Chains

TL;DR

This paper introduces periodic-affine policies for managing large-scale newsvendor networks under demand uncertainty, emphasizing robustness, data-driven modeling, and sustainability, with demonstrated advantages in medical supply chain applications.

Contribution

It proposes a novel class of adaptive, distribution-free policies called periodic-affine policies, applicable to multi-product and multi-period settings, with efficient algorithms for real-world use.

Findings

Policies are robust to demand parameter mis-specification.

Demonstrated superior profit and scalability in medical supply chain data.

Policies are time consistent and sustainable across planning horizons.

Abstract

We introduce a new class of adaptive policies called periodic-affine policies, that allows a decision maker to optimally manage and control large-scale newsvendor networks in the presence of uncertain demand without distributional assumptions. These policies are data-driven and model many features of the demand such as correlation, and remain robust to parameter mis-specification. We present a model that can be generalized to multi-product settings and extended to multi-period problems. This is accomplished by modeling the uncertain demand via sets. In this way, it offers a natural framework to study competing policies such as base-stock, affine, and approximative approaches with respect to their profit, sensitivity to parameters and assumptions, and computational scalability. We show that the periodic-affine policies are sustainable, i.e. time consistent, because they warrant optimality both within subperiods and over the entire planning horizon. This approach is tractable and free of distributional assumptions, and hence, suited for real-world applications. We provide efficient algorithms to obtain the optimal periodic-affine policies and demonstrate their advantages on the sales data from one of India's largest pharmacy retailers.