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Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in

Yongbin Yang1, Mengdie Wang2, Jiyuan Wang3

  • 1Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep reinforcement learning framework for retail supply chains, improving demand forecasting and inventory management. The model reduces forecast errors by 18.2% and stockouts by 23.5% using real-time sensor data.

Keywords:
demand forecastinginventory optimizationmulti-agent reinforcement learningsupply chain management

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Area of Science:

  • Supply Chain Management
  • Artificial Intelligence
  • Operations Research

Background:

  • Retail supply chains face significant challenges in demand forecasting and inventory management due to dynamic consumer behavior and market volatility.
  • Existing statistical and machine learning models struggle to capture complex temporal dependencies and optimize inventory decisions concurrently.
  • Integration of Internet of Things (IoT) sensors, RFID, and smart shelves offers new data streams for enhanced supply chain visibility.

Purpose of the Study:

  • To propose a novel multi-agent deep reinforcement learning (DRL) framework for integrated demand forecasting and inventory management in retail.
  • To leverage diverse data sources, including historical sales and real-time sensor data, for improved operational efficiency.
  • To address the limitations of traditional methods in handling complex temporal patterns, promotional effects, and environmental factors.

Main Methods:

  • A hybrid approach combining transformer-based sequence modeling for demand forecasting with hierarchical reinforcement learning agents for inventory control.
  • Utilization of attention mechanisms to process historical sales data and real-time sensor measurements (temperature, humidity) for pattern recognition.
  • Coordination of inventory decisions across distribution networks by multi-agent reinforcement learning.

Main Results:

  • Achieved an 18.2% reduction in demand forecast error compared to state-of-the-art baselines.
  • Reduced stockout rates by 23.5% through optimized inventory management.
  • Demonstrated significant improvements in forecasting and inventory control during promotional events and seasonal transitions.

Conclusions:

  • The proposed multi-agent DRL framework offers a scalable and effective solution for optimizing integrated demand forecasting and inventory management in sensor-enabled retail supply chains.
  • Leveraging real-time sensor data and advanced AI techniques provides a competitive advantage in dynamic retail environments.
  • This research advances the application of DRL in optimizing complex, real-world supply chain operations.