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From cutting costs to improving customer experience, forecasting is the cruxof retail supply chain management (SCM) and the key to better supply chainperformance. Several retailers are using AI/ML models to gather datasets andprovide forecast guidance in applications such as Cognitive Demand Forecasting,Product End-of-Life, Forecasting, and Demand Integrated Product Flow. Earlywork in these areas looked at classical algorithms to improve on a gamut ofchallenges such as network flow and graphs. But the recent disruptions havemade it critical for supply chains to have the resiliency to handle unexpectedevents. The biggest challenge lies in matching supply with demand.  Reinforcement Learning (RL) with its ability to train systems to respond tounforeseen environments, is being increasingly adopted in SCM to improveforecast accuracy, solve supply chain optimization challenges, and trainsystems to respond to unforeseen circumstances. Companies like UPS and Amazonhave developed RL algorithms to define winning AI strategies and keep up withrising consumer delivery expectations. While there are many ways to build RLalgorithms for supply chain use cases, the OpenAI Gym toolkit is becoming thepreferred choice because of the robust framework for event-driven simulations.  This white paper explores the application of RL in supply chain forecastingand describes how to build suitable RL models and algorithms by using theOpenAI Gym toolkit.

Implementing Reinforcement Learning Algorithms in Retail Supply Chains with OpenAI Gym Toolkit