LAUSR

Abstract Manufacturing processes are becoming increasingly complex. In order to stay competitive, speeding up knowledge generation about the processes, products and their interdependencies is necessary. Digitalization and data analytics provide the required toolset to uncover hidden relations within data. Since it is not feasible to collect and store all data generated within large scale manufacturing, data analytics projects, which are initiated reactively to solve existing manufacturing problems, are often limited by the existing data and resources. Hence, required data as well as the IT infrastructure have to be designed for data analytics. Therefore, proactive specification of relevant use cases and their prioritization during early development phases of the manufacturing system is necessary. Due to synergies within the data, the use case prioritization is a complex multicriteria combinatorial problem. This paper presents an approach to solve this problem by means of optimization. In the given example, a 28% more cost effective solution over existing prioritization methods was achieved.