As published in arXiv.org: April 27, 2021
From cutting costs to improving customer experience, forecasting is the crux of retail supply chain management 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 and product end-of-life. However, the biggest challenge lies in matching supply with demand during disruptions. 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 build supply chain resilience.
This white paper explores the application of RL in supply chain forecasting and describes how to build suitable reinforcement learning algorithms and models by using the OpenAI Gym toolkit.
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