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Achieving energy efficiency in industrial manufacturing
Scania CV AB, Sweden; Uppsala University, Sweden.
RISE Research Institutes of Sweden, Materials and Production, Methodology, Textiles and Medical Technology.ORCID iD: 0000-0001-8694-4122
KTH Royal Institute of Technology, Sweden.
University of Skövde, Sweden.
2025 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 216, article id 115619Article in journal (Refereed) Published
Abstract [en]

This paper explores the use of digital technology stages and knowledge demand types for achieving energy efficiency. Digital technology stages are the steps toward developing an intelligent and networked factory: computerization, connectivity, visibility, transparency, predictive capacity, and adaptability. Knowledge demand types refer to the knowledge and skills needed to implement energy management through technical, process, and leadership knowledge. Empirical data were collected from a critical single case study at an industrial manufacturing company. The study made two significant contributions. Firstly, it identifies fourteen challenges and improvement potentials when working with energy monitoring, evaluation, and optimization, demonstrating the critical role of digital technology stages and knowledge demand types. Secondly, the study presents a conceptual framework indicating how companies could overcome pitfalls and enhance energy efficiency by combining digital technologies and knowledge demands. Future work will include technical implementations and its connection to knowledge management. 

Place, publisher, year, edition, pages
Elsevier Ltd , 2025. Vol. 216, article id 115619
Keywords [en]
Digital technologies; Empirical data; Energy; Energy wastes; Industrial manufacturing; Knowledge demand; Predictive capacity; Technical process; Technology use; Smart manufacturing
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:ri:diva-78395DOI: 10.1016/j.rser.2025.115619Scopus ID: 2-s2.0-105000946035OAI: oai:DiVA.org:ri-78395DiVA, id: diva2:1999219
Note

 The authors also acknowledge the support of the Swedish Innovation Agency (VINNOVA).This study is part of the Explainable and Learning Production andLogistics by Artificial Intelligence (EXPLAIN), Sweden project led byUppsala University, project number 2021-01289. 

Available from: 2025-09-19 Created: 2025-09-19 Last updated: 2025-09-23Bibliographically approved

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Mattsson, Sandra

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