Typeset

The potential of platform-based business models in the context of Industry 4.0 has not yet been fully realized. Approaches for platforms and ecosystem- based value creation vary between industries. The plastics industry has so far been widely ignored in this respect. Due to its industrial structure, in particular the uniform value creation structures, the plastics industry is suitable for digital platforms. In addition to approaches for platforms in the injection molding industry, the article offers a procedure model for the expansion of established business models. This can facilitate the entry into the field of platform-based business models for SMEs.

New types of value creation characterized by extensive data use and cross-company data sharing are becoming increasingly important for companies. However, many barriers slow down the path towards data-based value creation, especially for SMEs. Companies often lack specific ideas for data usage or digitalization and data competencies. As a result, there is often untapped value creation potential in companies. By describing a real laboratory setting with real experiments, this article demonstrates support options for companies to identify their own "data treasure" and to lift it.

The combination of product service systems and big data requires a change in the existing, traditional business models and a repositioning of the companies. Since these changes are often a challenge, this article uses the example of predictive maintenance to present the influences of big data and product service systems on the business models of medium-sized companies in mechanical and plant engineering. Based on a systematic literature review in combination with expert interviews, numerous practical business model implications were obtained, providing sound guidance for industry representatives.

Precise stochastic demand calculation is the key to successful material planning, i. e. to always have exactly the right quantity on hand. However, decision-makers are faced with the dilemma of which of the many forecasting methods they should use, adapted to the item properties as much as possible. This paper examines the optimization potential of a self-developed automatically optimizing forecasting approach based on ten common forecasting methods, which are evaluated using two case studies from the capital goods industry.

Digital twins (DT) for circular economy (CE) offer a promising approach as part of digital data ecosystems for more sustainable value creation. By mapping and analyzing product, component and material specific data along the li- fecycle, it is possible to address current challenges such as climate change and resource scarcity. Within Catena-X, specific solutions based on this cross-company exchanged data and information are developed. Here, the “R-Strategy Assistant” is presented. It is an application, which identifies the best CE-Strategy based on DT data at the end of a vehicle's life.

Resource-efficient businesses have become increasingly important for companies in recent years. Although this brings new potentials, the practical implementation in the form of suitable business models is accompanied by challenges. In this paper, we will examine which concepts and methods already exist for the development of circular and resource- efficient business models and show approaches and solutions from science and practice using the example of interdisciplinary research projects.