Open-Source Implementation of the Industrial Metaverse

Industrial platforms are fundamentally transforming the organization and structure of value creation. Beyond serving as intermediaries that connect machine manufacturers, suppliers, and service providers, they enable data-driven decision-making in production while fostering innovation in product development. In doing so, they create new opportunities for collaboration and value creation through enhanced connectivity and interaction across the platform ecosystem.

Digital collaboration and innovation platforms offer SMEs significant potential to compensate for structural resource weaknesses and to participate in innovation ecosystems. Nevertheless, adoption in the B2B context remains low. This paper examines adoption barriers based on a systematic literature review using Teece’s dynamic capabilities approach. The analysis suggests that recurring obstacles can be structured along three dimensions: Sensing—lack of ecosystem awareness, absence of scanning routines; Seizing—IP concerns, governance uncertainty, adoption fatigue; Reconfiguring—closed-innovation culture, lack of absorptive capacity. Building on this, a practice-oriented capability-building framework is developed with recommendations for action.

Assessing the impact of digital industrial platforms on the dynamics and resilience of supply chains requires clear classification of such platforms. This article examines the extent to which a current classification proposal from the field of supply chain management must be further developed in the context of digital platforms for implementing the circular economy (CE). The authors conclude that a fundamental revision is not necessary, as the digital CE platforms under consideration fit well into the existing classification system. However, new research questions arise regarding the distinction between digital service platforms and digital data-oriented platforms, as well as the link between the circular economy and supply chain management—particularly in connection with supply chain control towers, which are becoming increasingly established in supply chain management practice.

The networking meetings of the research funding program “Dynamics of Digitally Networked Value Creation Systems (DynaVer)” have shown that different disciplines disagree on what interoperability is and whether it is ultimately desirable. This article aims to shed light on the difficulties involved in developing a universally applicable and, at the same time, legally sound definition of interoperability. Furthermore, drawing on insights from competition economics, the article assesses whether and under what circumstances interoperability is a desirable outcome of regulation.

Despite its potential, the introduction of artificial intelligence (AI) in industry is often delayed, primarily due to perceived complexity, high costs, and a lack of expertise. This article presents a modular demonstrator reference architecture that provides practical, low-cost access to industrial AI applications. Developed within a design science research approach, it specifically supports experimentation, learning, and gradual integration into existing production processes. The focus is on machine vision, implemented using cost-effective hardware and open-source software. Its applicability is demonstrated in three scenarios: quality control, chip classification, and in-company training. Initial evaluations confirm the technical feasibility, didactic relevance, and transferability to a variety of industrial contexts.