Data and Knowledge Engineering
Data and Knowledge Engineering · 2023
Predicting and managing stock and demand balance is a critical aspect of supply chain management.In this paper, we propose a predictive analytics approach that leverages deep Q-learning algorithm to optimize stock and demand balance.Traditional forecasting methods often struggle to capture the complex dynamics and uncertainties associated with stock and demand fluctuations.The deep Q-learning algorithm, a reinforcement learning technique, offers a promising solution by learning optimal decision-making policies through interaction with the environment.The proposed approach utilizes historical stock and demand data to train a deep Q-learning model, enabling it to make accurate predictions about future stock levels and demand patterns.By considering factors such as seasonality, trends, and external variables, the model learns to adjust stock levels to meet demand while minimizing excess inventory or stock outs.To validate the effectiveness of the proposed approach, a case study conducted using real-world data from a supply chain network.The results demonstrate that the deep Q-learning algorithm outperforms traditional forecasting methods, achieving higher accuracy in predicting stock and demand balance.The implications of this research are significant for supply chain managers and decision-makers.By incorporating predictive analytics with the deep Q-learning algorithm, companies can enhance their inventory management strategies, reduce holding costs, minimize stock outs, and improve customer satisfaction.