maturity model

The fischertechnik® Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments.

Small and medium-sized enterprises still face the challenge of shaping their digital transformation. Maturity models offer a way to capture the situation within a company and support the formation of an Industry 4.0 vision. This paper presents a methodology that companies can use to develop a roadmap for shaping digital transformation by enabling the transfer of this vision into concrete decision-making steps.

The use of artificial intelligence (AI) is becoming more important for a variety of industries, which is why enterprise resource planning (ERP) systems also offer many possible uses of AI. Due to their newly acquired, AI-based adaptability and learning abilities, modern AI-integrated ERP systems are able to develop competencies, plan processes, make forecasts and interact intelligently with humans. It is not uncommon for such systems to initiate major structural changes for companies and to open up new markets and design areas [1]. In order to measure the progress of an ERP system in terms of AI, the Center for Enterprise Research (CER) has developed an AI maturity model. Building on this model, a tool for evaluating AI integration in an ERP system should be able to showcase potential for development and enable market comparison.

Using industry 4.0 maturity models, companies can systematically record their performance in the context of industry 4.0. When the status quo is determined, the question “Where do we want to be in future?” is usually associated at the same time. However, companies are not always in a position to introduce what is fundamentally possible. Therefore, this question is not trivial. If a company is supposedly aware of its I4.0 target position, external influences often lead to the fact that the achievement of the target is made more difficult or hindered. It is therefore important to take these circumstances into account. This paper shows how environmental developments can be taken into account when determining a promising I4.0 target position. The target position forms the starting point for the implementation of industry 4.0 in the company.