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Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers

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  • Henry Ekwaro-Osire

    (BIBA—Institut für Produktion und Logistik, Hochschulring 20, 28359 Bremen, Germany
    Department of Integrated Product Development, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany)

  • Dennis Bode

    (BIBA—Institut für Produktion und Logistik, Hochschulring 20, 28359 Bremen, Germany
    Department of Integrated Product Development, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany)

  • Klaus-Dieter Thoben

    (BIBA—Institut für Produktion und Logistik, Hochschulring 20, 28359 Bremen, Germany
    Department of Integrated Product Development, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany)

  • Jan-Hendrik Ohlendorf

    (Department of Integrated Product Development, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany)

Abstract

Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resources in manufacturing systems. However, the hype around ML in the context of Industry 4.0 in the past few years has led to blind usage of the approach, occasionally resulting in usage when another analysis approach would be better suited. The research presented here uses a novel matrix approach to address this lack of differentiation of when to best use ML for improving energy and resource efficiency in manufacturing, by systematically identifying situations in which ML is well suited. Seventeen generic levers for improving manufacturing energy and resource efficiency are defined. Next, a generic list of six manufacturing data scenarios for when ML is a good method of choice for analysis is created. This results in a comprehensive matrix in which each lever is evaluated along each ML scenario and given a point, providing a quantitative ML suitability score for each lever. The evaluation is conducted by drawing on past studies demonstrating whether ML is appropriate. Specifically, operation parameter and input material optimization, as well as intelligent maintenance, are the levers that score the highest and are thus identified to be most suitable for machine learning. The majority of the remaining levers is deemed to have low suitability for machine learning. This simple yet informative matrix can be used as a guideline in data-driven manufacturing energy and resource efficiency projects to provide an appraisal on the applicability of ML as the initial analysis tool of choice.

Suggested Citation

  • Henry Ekwaro-Osire & Dennis Bode & Klaus-Dieter Thoben & Jan-Hendrik Ohlendorf, 2022. "Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15618-:d:982688
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    References listed on IDEAS

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    1. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    2. Rajeev Rathi & Dattatraya Balasaheb Sabale & Jiju Antony & Mahender Singh Kaswan & Raja Jayaraman, 2022. "An Analysis of Circular Economy Deployment in Developing Nations’ Manufacturing Sector: A Systematic State-of-the-Art Review," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    3. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    4. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).
    5. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    6. May, Gökan & Stahl, Bojan & Taisch, Marco, 2016. "Energy management in manufacturing: Toward eco-factories of the future – A focus group study," Applied Energy, Elsevier, vol. 164(C), pages 628-638.
    7. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    8. Jessica Wehner, 2018. "Energy Efficiency in Logistics: An Interactive Approach to Capacity Utilisation," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
    9. Máša, Vítězslav & Stehlík, Petr & Touš, Michal & Vondra, Marek, 2018. "Key pillars of successful energy saving projects in small and medium industrial enterprises," Energy, Elsevier, vol. 158(C), pages 293-304.
    10. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    11. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
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    1. Marta Daroń & Monika Górska, 2023. "Relationships between Selected Quality Tools and Energy Efficiency in Production Processes," Energies, MDPI, vol. 16(13), pages 1-20, June.

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