Optimizing inventory management through reinforcement learning

Oluwatumininu Anne Ajayi *

Department of Industrial Engineering, Faculty of Engineering, Texas A and M University, Kingsville, Texas, United States of America.
 
Research Article
International Journal of Science and Research Archive, 2023, 08(01), 1110-1116.
Article DOI: 10.30574/ijsra.2023.8.1.0137
Publication history: 
Received on 30 December 2022; revised on 25 February 2023; accepted on 27 February 2023
 
Abstract: 
Inventory management remains a cornerstone of effective supply chain performance, directly influencing cost efficiency, service quality, and organizational agility. In today’s hypercompetitive and uncertain market environment, inventory decisions must account for complex variables such as fluctuating demand, supply disruptions, lead time variability, and market seasonality. Traditional inventory control models such as the Economic Order Quantity (EOQ), base-stock policies, and (s, S) strategies are often static in nature. They rely on pre-defined parameters and assume stationarity in demand and supply, limiting their ability to respond dynamically to real-time changes. In contrast, Reinforcement Learning (RL) offers a paradigm shift in how inventory decisions can be optimized. As a subfield of machine learning, RL enables agents to learn optimal strategies through repeated interactions with an environment, using trial-and-error exploration and reward-based feedback. RL agents can observe the system state (e.g., inventory levels, demand signals, lead time status), choose actions (e.g., place an order or wait), and receive feedback in the form of rewards (e.g., service level achievements or cost penalties), thus iteratively improving their policies.
This study explores how RL can be applied to optimize inventory management in environments characterized by uncertainty and real-time decision-making needs. Specifically, we investigate how different RL algorithms such as Q-learning, Deep Q Networks (DQN), and Policy Gradient methods perform in various inventory scenarios. Additionally, we examine the computational and operational implications of deploying RL in real-world settings, including issues of model convergence, exploration-exploitation tradeoffs, data requirements, and scalability. We also discuss how RL can complement other AI techniques such as demand forecasting models and predictive analytics in creating end-to-end intelligent supply chain solutions.
By bridging the gap between theoretical RL frameworks and practical inventory management applications, this paper contributes to both the academic literature and industrial practice. Our goal is to demonstrate that RL is not only a theoretically elegant solution but also a viable tool for achieving inventory efficiency and supply chain resilience.
 
Keywords: 
Inventory Optimization; Reinforcement Learning; Supply Chain Analytics; Deep Q-Learning; Actor-Critic Methods; Demand Volatility
 
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