A Demand Estimation Algorithm for Inventory Management Systems Using Censored Data

During the selling time horizon of a product category, a number of products may become unavailable sooner than others and the customers may substitute their desired product with another or leave the system without purchase. So, the recorded sales do not show the actual demand of each product. In this paper, a nonparametric algorithm to estimate true demand using censored data is proposed. A customer choice model is employed to model the demand and then a nonlinear least square method is used to estimate the demand model parameters without assuming any distribution on customer’s arrival. A simple heuristic approach is applied to make the objective function convex, making the algorithm perform much faster and guaranteeing the convergence. Simulated dataset of different sizes are used to evaluate the proposed method. The results show a 23% improvement in root mean square error between estimated and simulated true demand, in contrast to alternate methods usually used in practice.

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