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

We introduce a new class of adaptive policies called periodic-affine policies, which 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 misspecification. We present a model that can be generalized to multiproduct settings and extended to multiperiod 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-that is, 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.