Data-Driven Optimization of Overall Equipment Effectiveness in Pharmaceutical Manufacturing Systems

Chukwunedum Ogochukwu Chinedum¹, Charles Chikwendu Okpala², Chukwudi Emeka Udu³

¹ Lecturer, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Anambra State, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Anambra State, Nigeria. 

³ Researcher, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Anambra State, Nigeria.    

Abstract

Pharmaceutical manufacturing faces increasing pressure for the improvement of productivity while meeting stringent regulatory, quality, and sustainability requirements. Overall Equipment Effectiveness (OEE), which is a composite metric that integrates availability, performance, and quality has emerged as a critical indicator of operational excellence. However, traditional OEE improvement initiatives rely on siloed analyses and heuristic interventions that fail to exploit the growing availability of high-frequency production, quality, and energy data. This study proposes a data-driven, multidisciplinary optimization framework for OEE enhancement in pharmaceutical manufacturing systems. The framework integrates industrial engineering, machine learning, operations research, and sustainability assessment to quantify and optimize trade-offs among throughput, compliance, and environmental impact. Using a multi-line solid-dosage manufacturing case study with over 12 million time-stamped records, the research demonstrated that the proposed approach improves OEE by 9.8–14.6%, reduces energy intensity by 11.2%, and reduces material waste by 17.4%, without compromising product quality or regulatory constraints. The results provide a scalable and reproducible pathway for sustainable, high-performance pharmaceutical operations.     

Keywords: Overall equipment effectiveness, Pharmaceutical manufacturing, Machine learning, Sustainability, Optimization, Industry 4.0

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