Management Science and Information Technology

Management Science and Information Technology

Overproduction in Manufacturing Systems: Transitioning from Traditional Control to Predictive Artificial Intelligence

Document Type : Review Paper

Authors
Bekkour Youssef
chamat Abderrahim
ennadi abdelali
boutayeb abdelouahed
Laboratoire des Techniques Industrielles, Faculté des Sciences et Techniques, Université Sidi Mohamed Ben Abdellah (USMBA), Fès, Morocco
10.22034/ISS.2026.10682.1078
Abstract
Objective: Overproduction is one of the most critical and costly forms of waste in automotive manufacturing, directly generating excess inventory, increased operational costs, and reduced market responsiveness. This article examines how predictive artificial intelligence (AI) can address the reactive, deterministic nature of traditional production planning and control systems that makes overproduction a systemic rather than an isolated operational outcome.
Methods: The article develops a structured, waste-oriented analytical framework that explicitly connects predictive AI techniques to the root causes and operational mechanisms of overproduction. Drawing on a structured review of the recent literature, the framework integrates demand forecasting, production planning, inventory control, and AI-based decision-making into a unified model, and comparatively analyzes machine learning, deep learning, hybrid AI–optimization, and reinforcement learning approaches. 
Results: Machine learning models improve forecast accuracy by 10–25% over classical methods, while deep learning and reinforcement learning approaches enable dynamic inventory adjustment and real-time production scheduling, yielding significant reductions in excess inventory. The framework links each AI method category to specific overproduction root causes and managerial decision levels, bridging the gap between technical models and operational practice.
Conclusion: The study identifies critical research gaps related to multi-source data integration, scalability, explainability, and industrial deployment, and proposes concrete future research directions. It demonstrates that predictive AI, embedded within Lean Manufacturing and Industry 4.0 practices, represents a key enabler for the transition from reactive to proactive, sustainable production management.
Keywords
Predictive artificial intelligence
Overproduction reduction
Automotive manufacturing
Production planning
Demand forecasting
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Management Science and Information Technology
Volume 3, Issue 1
Winter 2026
Pages 16-29