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As transportation is accountable for around 87 % of total logistics emissions globally, scientific focus in the past laid on the moving elements of the supply chain and not the stop points in between, namely the warehouses. However, responsible for 13 % of emissions, logistics real estate should not be neglected. Thus, based on an extensive literature research, the article summarizes the current state of science in green logistics buildings. By discussing certain aspects of supply chain strategy development, location planning and warehouse construction, possibilities aiming to minimize the ecological lifecycle footprint are elaborated.

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.

The advancing technological change, the globalization of markets as well as increasing customer requirements have led to a significant increase in complexity in manufacturing companies and their supply chains. Companies and entire value chains are countering this development with product modularization strategies. In this context, however, the investigation of the influences of product modularization on the supply chain receives little attention. This can lead to unused potentials and additional risks, such as the loss of core competencies. Therefore, this article deals with necessary processes and success factors that result from a joint consideration of product modularization along the supply chain. On the basis of a systematic analysis of scientific literature and guideline-supported expert interviews, a process model with different phases and steps was developed and currently necessary success factors were identified.

Media disruption interferes with consistent and universal digitalization. Data is easily lost, time and resources wasted. Heterogeneous and isolated applications produce partial relief; however they fail to integrate redundant information from separately operated systems into a homogeneously processible data mass. The employment of a centralized data hub proposes a strategy to effectively advance digitalization in process management, connecting scheduling of involved parties, defect tracking and progress processes. It also automatizes reportings on project progress.

The growing amount of available data due to the digitalization of value creation is accelerating the transformation of manufacturing industries into providers of customer-oriented services. Smart services, currently the most highly developed level of data-based digital services to complement physical products for specific customer expectations, are an example of this. However, the analysis of expert interviews as well as of use cases from business practice shows that the knowledge of how such smart services can be developed is still rudimentary. This article presents a framework for Smart Service Lifecycle Management that supports the systematic development of Smart Services, taking into account business models and the value network. The framework concept will be implemented and validated based on an application example from the textile industry.

Blockchain (BC) comprises features that are relevant for supply chain management. Product recalls continue to increase due to complex supply chains. The challenge is to efficiently prepare a product recall, perform it adequately and execute the root cause analysis. The BC can support as a tool and create transparency. A reaction to required product recalls can occur timely, cost efficiently and situationally appropriate.

Data sets increasing data bases and computing power as well as decreasing costs for computing and storage capacities form the basis for the use of Machine Learning (ML) in production. The challenges are the identification of promising application areas, the recognition of the associated learning tasks as well as the uncovering of suitable data sets. This article therefore answers the following questions: Which application areas in production offer the greatest potential for the use of ML? Which freely accessible data sets are suitable for gaining experience and which learning tasks are associated with them? What are best practices for the application areas?

Digitalization is a strategic core issue that has to be anchored in the strategy of every company. The challenge in this context is that there is no uniform pattern for the digital transformation of a company. Instead, each company has to develop its own company-specific plan how it will position itself in the context of digitalization. Furthermore, the development of an individual digitalization strategy is required. The following article presents a planning approach for the development of such a digitalization strategy, based on three major steps.

Companies have always been in a constant state of change. This change is today closely linked to the buzzword’s “digitization” and “agility”. Agile methods, especially in complex projects, can pave the way for targeted digitization and, on the other hand, provide a more agile way of working for digital technologies. Through group discussions with managers from small and medium-sized IT companies, this article focuses on the question of whether agility is the precondition or consequence of targeted digitization. This article is aimed at decision-makers from SMEs who want to increase the degree of agility in the company in the context of increasing digitization.

The integration of industrial laser assistance systems for worker guidance into the control layer opens up possibilities of digital transformation for manufacturing companies. These are illustrated using the example of the Digital Factory of the Leuphana University Lüneburg. In a practice project a manual assembly station using an industrial laser assistance system is developed and integrated into the SIMATIC control level of the digital factory. The worker interacts with the assistance system and is guided by the latter through the order-related assembly process. The worker stands in the center of action.