Purpose: This study aims to develop a maturity model to assess manufacturing companies’ adoption of digital technologies for energy efficiency and to formulate strategies to facilitate progress toward higher maturity levels. To achieve this goal, the study will identify and analyze the challenges inherent in the adoption and implementation of digital technologies for energy efficiency. Design/methodology/approach: This study used a mixed methodology, combining analysis of the literature for building a maturity model and a questionnaire for validating the model and developing strategies for advancing maturity. The questionnaire was answered by 101 Swedish manufacturing companies. Findings: The findings reveal that the aluminum industries and iron and steel industries exhibit higher maturity levels in adopting digital technologies. Most companies are intermediate adopters utilizing core technologies such as the Internet of things, cloud and big data for energy use monitoring, analysis and reporting. A smaller subset of companies, identified as leading adopters, reached the highest maturity level, integrating artificial intelligence, predictive analytics and machine learning into their energy management systems to optimize both production and energy use. A key challenge identified is the “lack of knowledge” regarding the adoption and implementation of these technologies. Research limitations/implications: It is essential to emphasize that the developed maturity model does not prioritize the adoption of multiple types of digital technologies. From a maturity standpoint, what truly matters is how effectively the information obtained from digital technologies is utilized in energy efficiency and energy management work to create knowledge and, thus, add value to the organization. Practical implications: The maturity model and the strategies for advancing maturity related to the adoption of digital technology for energy efficiency are designed to be applicable to all types of manufacturing industries regardless of what sector or country the company is active in. The model can also be used by academia or other actors interested in evaluating the maturity level for the adoption of digital technologies for energy efficiency in companies in the manufacturing industry. The developed strategies offer guidance on determining which activities to undertake within the organization based on its current level of maturity. Originality/value: This study’s main contributions are: (1) the maturity model to assess digital technology adoption for energy efficiency, (2) a set of strategies to advance maturity in adoption and (3) empirical investigation of maturity levels in the adoption of digital technologies for energy efficiency in 101 Swedish manufacturing companies.

Energy efficiency (EE) improvement is one of the most crucial elements in the decarbonization of industry. EE potential within industries largely remains untapped due to the lack of information regarding potential EE measures (EEM), knowledge regarding energy use, and due to the existence of some inconsistencies in the evaluation of energy use. Classification of energy end-using processes would increase the understanding of energy use, which in turn would increase the detection and deployment of EEMs. The study presents a novel taxonomy with hierarchical levels for energy end-use in manufacturing operations for the engineering industry, analyzes processes in terms of energy end-use (EEU) and CO₂ emissions, and scrutinizes energy performance indicators (EnPIs), as well as proposing potential new EnPIs that are suitable for the engineering industry. Even though the study has been conducted with a focus on the Swedish engineering industry, the study may be generalizable to the engineering industry beyond Sweden.

Increasing energy efficiency within the industrial sector is one of the main approaches in order to reduce global greenhouse gas emissions. The production and processing of aluminium is energy and greenhouse gas intensive. To make well-founded decisions regarding energy efficiency and greenhouse gas mitigating investments, it is necessary to have relevant key performance indicators and information about energy end-use. This paper develops a taxonomy and key performance indicators for energy end-use and greenhouse gas emissions in the aluminium industry and aluminium casting foundries. This taxonomy is applied to the Swedish aluminium industry and two foundries. Potentials for energy saving and greenhouse gas mitigation are estimated regarding static facility operation. Electrolysis in primary production is by far the largest energy using and greenhouse gas emitting process within the Swedish aluminium industry. Notably, almost half of the total greenhouse gas emissions from electrolysis comes from process-related emissions, while the other half comes from the use of electricity. In total, about 236 GWh/year (or 9.2% of the total energy use) and 5588–202,475 tonnes CO_2eq/year can be saved in the Swedish aluminium industry and two aluminium casting foundries. The most important key performance indicators identified for energy end-use and greenhouse gas emissions are MWh/tonne product and tonne CO₂-eq/tonne product. The most beneficial option would be to allocate energy use and greenhouse gas emissions to both the process or machine level and the product level, as this would give a more detailed picture of the company’s energy use and greenhouse gas emissions.