Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning - Elaheh Gholamzadeh Nabati; Maria Teresa Alvela Nieto; Dennis Bode; Thimo Florian Schindler; André Decker; Klaus-Dieter Thoben (Production)
Abstract Paper aims Due to increasing energy prices, manufacturers have to pay more attention to the energy efficiency of their production processes. This paper aims to support manufacturers in increasing processes’ energy efficiency by using production data and applying machine learning approaches. Originality Systematic guidelines or standards for minimising the energy consumption of manufacturing processes through machine learning approaches are still lacking. This gap is addressed in this paper. Research method The paper follows a qualitative research method to understand the manufacturing processes and their challenges in improving energy efficiency. The raw data for a 5-step approach were collected in research projects with manufacturing SMEs, and information about the processes through interviews and workshops with them. Then, an analysis of currently available machine learning frameworks and their selection and implementation is conducted. Main findings The main result is a 5-step approach for increasing the energy efficiency of manufacturing processes through machine learning. Essential applications and technical challenges for data mapping, integrating, modelling, implementing, and deploying machine learning algorithms in manufacturing processes for increasing energy efficiency are presented. Implications for theory and practice The findings can guide manufacturers, researchers, and data scientists to use machine learning in practice when they intend to increase the energy efficiency of manufacturing processes.