K.V.M. Naidu
ABSTRACT
A real world supply chain planning starts with the demand forecasting as a key input. In most scenarios, especially in fields like e-commerce where demand patterns are complex and are large scale, demand forecasting is done independent of supply chain constraints. There have been a plethora of methods, old and recent, for generating accurate forecasts. However, to the best of our knowledge, none of the methods take supply chain constraints into account during forecasting. In this paper, we are primarily interested in supply chain aware forecasting methods that does not impose any restrictions on demand forecasting process. We assume that the base forecasts follow a distribution from exponential family and are provided as input to supply chain planning by specifying the distribution form and parameters. With this in mind, following are the contributions of our paper. First, we formulate the supply chain aware forecast improvement of a base forecast as finding the game theoretically optimal parameters satisfying the supply chain constraints. Second, for regular distributions from exponential family, we show that this translates to projecting base forecast onto the (convex) set defined by supply constraints, which is at least as accurate as the base forecasts. Third, we note that using off the shelf convex solvers does not scale for large instances of supply chain, which is typical in e-commerce settings. We propose algorithms that scale better with problem size. We propose a general gradient descent based approach that works across different distributions from exponential family. We also propose a network flow based exact algorithm for Laplace distribution (which relates to mean absolute error, which is the most commonly used metric in forecasting). Finally, we substantiate the theoretical results with extensive experiments on a real life e-commerce data set as well as a range of synthetic data sets.
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Index Terms
- Network Aware Forecasting for eCommerce Supply Planning
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