Deep reinforcement learning for supply chain and price optimization

Supply chain and price management were among the first areas of enterprise operations that adopted data science and combinatorial optimization methods and have a long history of using these techniques with great success. Although a wide range of traditional optimization methods are available for inventory and price management applications, deep reinforcement learning has the potential to substantially improve the optimization capabilities for these and other types of enterprise operations due to impressive recent advances in the development of generic self-learning algorithms for optimal control. In this article, we explore how deep reinforcement learning methods can be applied in several basic supply chain and price management scenarios. The traditional price optimization process in retail or manufacturing environments is typically framed as a what-if analysis of different pricing scenarios using some sort of demand model. In many cases, the development of a demand model is challenging because it has to properly capture a wide range of factors and variables that influence demand, including regular prices, discounts, marketing activities, seasonality, competitor prices, cross-product cannibalization, and halo effects. Once the demand model is developed, however, the optimization process for pricing decisions is relatively straightforward, and standard techniques such as linear or integer programming typically suffice. For instance, consider an apparel retailer that purchases a seasonal product at the beginning of the season and has to sell it out by the end of the period. Assuming that a retailer chooses pricing levels from a discrete set (e.g., \$59.90, \$69.90, etc.) and can make price changes frequently (e.g., weekly), we can pose the following optimization problem: The first constraint ensures that each time interval has only one price, and the second constraint ensures that all demands sum up to the available stock level. This is an integer programming problem that can be solved using conventional optimization libraries.