Achieving energy efficiency in industrial manufacturing
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, 2025. Vol. 216, article id 115619
Keywords [en]
Energy efficiency, Energy management, Energy waste, Knowledge demands, Manufacturing, Technology use, Digital technologies, Empirical data, Energy, Energy wastes, Industrial manufacturing, Knowledge demand, Predictive capacity, Technical process, Smart manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics Energy Systems
Research subject
User Centred Product Design
Identifiers
URN: urn:nbn:se:his:diva-24980DOI: 10.1016/j.rser.2025.115619ISI: 001488956300001Scopus ID: 2-s2.0-105000946035OAI: oai:DiVA.org:his-24980DiVA, id: diva2:1949610
Projects
Explainable and Learning Production and Logistics by Artificial Intelligence (EXPLAIN)
Funder
Vinnova
Note
CC BY 4.0
© 2025 The Authors
Correspondence Address: T. Schmitt; Scania CV AB, Global Industrial Development, Södertälje, 151 38, Sweden; email: thomas.schmitt@scania.com; CODEN: RSERF
The authors extend their sincere gratitude to all interviewees who generously contributed their time and insights to this study. Special appreciation is owed to the members of the energy, media & supply team, under the leadership of Roland Dahlström, whose invaluable feedback and discussions enriched this research. The authors also acknowledge the support of the Swedish Innovation Agency (VINNOVA). This study is part of the Explainable and Learning Production and Logistics by Artificial Intelligence (EXPLAIN), Sweden project led by Uppsala University, project number 2021-01289.
2025-04-032025-04-032025-09-29Bibliographically approved