Autor: Joachim Metternich

Manufacturing Analytics is the evaluation and use of data in the production context. This article shows which potentials can be realised by Manufacturing Analytics in the context of reactive quality management. First, a general definition of the term Manufacturing Analytics is given and then its classification in the context of quality management is carried out. On the basis of a literature analysis and the evaluation of existing use cases, findings regarding the potentials for reactive quality processes are derived. This shows that Manufacturing Analytics is particularly promising and can be used in root cause analysis, defect detection and avoidance. Subsequently, an application example is presented.

In the project CrimpProd-S the integration of decentralized, self-learning production control systems in the context of Industry 4.0 in combination with additive manufacturing is investigated. The overall target is the economic evolutionary integration of lot size 1 in existing production facilities. Therefore a procedure model and customized methods are developed to analyze the potentials regarding the existing company’s strategy, value chain as well as the business model. This integrated procedure guarantees the efficient and customer-oriented transformation.

In order to improve action-oriented trainings, in this paper an approach for results-based evaluation by using evaluation tasks is presented. Thus weaknesses of current programs can be identified and trainings can be continuously improved.