Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers

Henry Ekwaro-Osire (), Dennis Bode, Klaus-Dieter Thoben and Jan-Hendrik Ohlendorf
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Henry Ekwaro-Osire: BIBA—Institut für Produktion und Logistik, Hochschulring 20, 28359 Bremen, Germany
Dennis Bode: BIBA—Institut für Produktion und Logistik, Hochschulring 20, 28359 Bremen, Germany
Klaus-Dieter Thoben: BIBA—Institut für Produktion und Logistik, Hochschulring 20, 28359 Bremen, Germany
Jan-Hendrik Ohlendorf: Department of Integrated Product Development, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany

Sustainability, 2022, vol. 14, issue 23, 1-19

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.

Keywords: manufacturing; data-driven sustainability; machine learning; energy efficiency; resource efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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