On the Potential of Sustainable Software Solutions in Smart Manufacturing Environments

Managing and using business data is considered as key success factor of modern companies and enterprises. Application domains range from the medical sector (cf. smart healthcare) to automated driving and digital manufacturing (cf. smart manufacturing and German Industry 4.0). Internet of things (IoT) landscapes are often built for data acquisition and process monitoring, in which many small distributed devices collect information about business-relevant processes. After data collection, so-called data-driven applications are used, for example, to support planning activities and strategic decisions, to optimize internal processes, or to help uncover sources of error and thus reduce error rates during production. In the latter two cases, the use of digital technologies also results in more sustainable resource utilization of important raw materials. At the same time, digital technologies are responsible for a significant share of energy consumption. Based on a country’s energy mix, ICT applications are thus responsible for a non-negligible share of climate-damaging emissions such as carbon dioxide (CO \(_2\) ). These emissions are widely considered avoidable, as they have been shown to contribute negatively to climate change. In our work, we highlight approaches to make ICT more sustainable. To this end, we address, among other things, data validation in smart manufacturing and explain the advantages and disadvantages of semantic methods.