ecology

This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems.

Industry 4.0 describes the digitization and interconnection of industrial value creation. So far, economic and technological potentials have been considered in particular, while ecological opportunities through industry 4.0 receive less attention. This article examines the ecological potentials of Industry 4.0 in terms of resources, energy consumption, logistics and transport, waste and emissions, from which propositions are derived. The study attempts to better understand of the ecological potentials of Industry 4.0 in order to contribute to more sustainable industrial value creation.