Open Source

The digital transformation of small and medium-sized enterprises (SMEs) in the manufacturing sector is hampered by vendor lock-in, high cloud costs, and stringent data sovereignty requirements when implementing Industrial Metaverse solutions. Although the Industrial Metaverse is quickly becoming a key concept in Industry 5.0, SMEs are often at a disadvantage when using proprietary solutions. This paper demonstrates how Industrial Metaverse applications can be realized by combining proven communication standards with open web technologies, thereby reducing barriers. This makes immersive applications for training, maintenance, and monitoring feasible even in SMEs. Using an open-source-based prototype as a best-practice implementation, the paper illustrates how the Industrial Metaverse can be made technologically and economically accessible to SMEs.

In the connected world, digital twins open up completely new possibilities: they virtually replicate physical systems, processes, or products. However, key challenges remain, including the collection of current product data. This issue of Industry 4.0 Science covers a wide range of topics, from the basic concept of the digital twin to its benefits in procurement and its use in supply chain management.

The success of technologies depends not only on their innovative strength and acceptance, but also on their management. Decision-makers evaluate factors like technical framework conditions and organizational requirements, with the demand for flexibility adding to the complexity. Industry 4.0 addresses this with networking, transparency and decentralized decisions. Digital twins, which can be implemented with open source software, play a key role.

Learning factories promote digital transformation through an interdisciplinary approach between lean management, Industry 4.0, energy efficiency, training center or research farm. SME centers are characterized by the on-site integration of small and medium-sized companies. Such a regional strategy, combined with learning factories, promotes a goal-oriented dialog between science and practice where students can put their theoretical knowledge to the test.

Facility managers for industrial properties are faced with the challenge of optimizing the energy efficiency of their facilities in the face of ever-increasing energy demand and rising energy costs. Digital processes that enable the comprehensive monitoring, analysis and control of energy demand offer an effective way to reduce costs, increase energy efficiency and make optimal use of resources. Based on IoT technologies and open-source software, a cost-effective, wireless and flexible retrofit solution for real-time energy data collection has been developed.

Intensified networking and digitalization of systems affect an increasing number of sectors. At the same time a great variety of different concepts, ideas, expectations as well as fears have emerged around Industry 4.0. A look into the newspapers is enough to understand that the profound connection of critical structures does also hold profound dangers. For the future it is crucial to consider a way of using the new mass of data and information to protect these structures. Evaluating big data and transforming it into smart data with support of Artificial Intelligence will be a significant security factor in the future.

The difference between Open and Closed Innovation is that external partners can get actively involved in all stages of the value added process and are not limited to being mere idea generators. Whilst finding solutions for their own problems and needs, creative individuals or groups of individuals can play an active role in the development of innovative products featuring new functional and design elements. In addition, Open Innovation provides further benefits in the field of image building and innovation marketing.

The Autonomous Logistic Engineering Methodology (ALEM), which is developed within the Collaborative Research Center 637, provides several tools for creating models of autonomously controlled logistic systems. To evaluate such models, the ALEM framework is extended to include a simulation component. As the ALEM Models cannot run directly within simulation software, they are transformed using principles of the Model Driven Architecture. To enable the transformation several open source tools can be applied. This article evaluates a selection of such tools with the aim of integrating them into ALEM.

Due to increased market turbulence, firms not only are faced with the decision to use open source software, but also consider releasing their proprietary software under an open source license. Beside the appropriateness as an open source product this raises the question of how to profit from giving something away which then is available for free. This article contributes to this discussion by addressing possible open source business models with regard to the type of software.

Dynamics and complexity of today`s production systems bring established approaches for production planning and control to their limits. Accordingly, developing new concepts and methods is a key point for research in this area. The combination of a decentralized control structure and innovative methods from the field of artificial intelligence seems promising here. Open source tools have proven their applicability to implement those methods. They are disposable and can be flexibly adapted to many problems. This contribution introduces an approach for the decentralized control of a shop floor. Here, artificial neuronal networks are used as adaptive control instruments. The simulation of these networks is performed with the open source tool Stuttgart Neural Network Simulator (SNNS) and its successor Java Neural Network Simulator (JNNS).