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Enhancement of Operational Efficiency in a Plastic Manufacturing Industry Through TPM, SMED, and Machine Learning—Case Study

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  • Smith Eusebio Lino Moreno

    (Industrial Engineering Program, Universidad Peruana de Ciencias Aplicadas (UPC), Lima 15023, Peru)

  • Brayan Leandro Navarro Ayola

    (Industrial Engineering Program, Universidad Peruana de Ciencias Aplicadas (UPC), Lima 15023, Peru)

  • Rosa Salas

    (Industrial Engineering Program, Universidad Peruana de Ciencias Aplicadas (UPC), Lima 15023, Peru)

  • S. Nallusamy

    (Department of Adult, Continuing Education and Extension, Jadavpur University, Kolkata 700032, India)

Abstract

The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of classic continuous improvement tools (TPM and SMED) with advanced data science techniques (machine learning) forms a synergistic approach capable of significantly increasing operational efficiency in manufacturing environments. The study was conducted at a Peruvian plastic container manufacturing company with a first overall equipment efficiency (OEE) of 61.87%, affected by low availability of injection and blow molding machines and a high rework rate. Total Productive Maintenance (TPM) strategies were implemented to improve equipment maintenance, the SMED method to reduce setup times, and a machine learning model to predict defects and burs in products. The effectiveness of the approach was confirmed through simulations in Arena and analysis of historical data. As a result, OEE increased to 80.86%, reducing downtime and rework. In conclusion, this study shows that the combination of TPM, SMED, and machine learning not only improves operational performance but also offers a replicable and robust methodological framework for process optimization in the manufacturing industry.

Suggested Citation

  • Smith Eusebio Lino Moreno & Brayan Leandro Navarro Ayola & Rosa Salas & S. Nallusamy, 2025. "Enhancement of Operational Efficiency in a Plastic Manufacturing Industry Through TPM, SMED, and Machine Learning—Case Study," Sustainability, MDPI, vol. 17(16), pages 1-30, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7445-:d:1726611
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    References listed on IDEAS

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    1. Karol Tucki & Olga Orynycz & Andrzej Wasiak & Arkadiusz Gola & Leszek Mieszkalski, 2022. "Potential Routes to the Sustainability of the Food Packaging Industry," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    2. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
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