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Predictive modelling for sustainable production enhancing: Study of metalworking Industry

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  • Patalas-Maliszewska, Justyna
  • Łosyk, Hanna
  • Dix, Martin
  • Rehm, Matthias

Abstract

Currently, managers of manufacturing enterprises expect solutions that help them monitor and increase the level of sustainable production (SP) so they can find a balance between energy related challenges, employee satisfaction, enterprise resource planning, economic performance, environmentally friendly and product development. Understanding the linkage between SP and energy-related challenges is an essential factor in recent research. The main goal of this study is to establish the new approach to increase SP capabilities of the manufacturing company and to determine the decision-making support tool for raising the level of SP. The research was based on analysis of the scientific literature and application of empirical research results from eighty-four small and medium sized (SME sector) manufacturing enterprises metalworking Industry and a real-life case study, an enterprise of the SME sector of the machining technology. The results achieved highlighted the new method for predictive SP with decision trees and, secondly, determined the decision trees-based predictive models for SP. The findings of this research revealed the main challenges and directions: (1) develop the guidelines for SP enhancing for manufacturers according to the concept Industry 5.0, and (2) create an integrated computerized approach for monitoring and predicting SP level. Finally, the research results establish a connection between renewable and sustainable energy focusing on applications in metalworking industry.

Suggested Citation

  • Patalas-Maliszewska, Justyna & Łosyk, Hanna & Dix, Martin & Rehm, Matthias, 2025. "Predictive modelling for sustainable production enhancing: Study of metalworking Industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:rensus:v:210:y:2025:i:c:s1364032124009146
    DOI: 10.1016/j.rser.2024.115188
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