Autor: Alexander Jacob

Engineers are often uncertain about the application of machine learning (ML) due to the amount of different machine learning methods and the complexity of modeling. Thus, the use of ML applications in manufacturing companies remains behind the technical possibilities. This paper presents an intuitive ML guideline for engineers to reduce this uncertainty. The guideline comprises a process model with AI-based solutions to common problems of product development and production. An industrial example is used to demonstrate the functionality and the possibilities of the guide.

The use of key figures allows for a comprehensive consideration of the performance characteristics of a company and serves as basis for decisions. The mapping of systems in a Digital Twin increases the amount of data and also its timeliness. The paper describes a holistic, transferable KPI network that can be used to manage companies. Strategic goals can be operationalized through the KPI network. Using an industry example, advantages and possibilities of using such a network are demonstrated.