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