Typeset

Current State of Disturbance Management Disturbances in production systems can lead to massive costs for enterprises. It is a highly relevant ability for manufacturing companies to cope with the effects of occurring disturbances and their prevention. A survey on manufacturing companies draws a current picture of disturbance management and identifies challenges for coping with disturbances.

The aim of circular economy is to return a used product to the product and production life cycle several times in a value-adding way. This article presents the current industrial practice for a successful circular economy and the associated potentials, based on a definition of circular economy and its principles. Afterwards, existing challenges of the circular economy are analyzed. The article intends to support decision-makers in recognizing the advantages of circular economy and to tackle challenges for a successful introduction at an early stage.

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.

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.

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.

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.

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.

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.

The digital transformation of the industry, with its technological components, has a direct impact on the alignment of logistics processes within companies as well as in entire corporate networks. The development and integration of new technologies is triggering more and more rigid corporate structures and control architectures. The vision ranges from decentralized networks of modular conveyor and storage technology to the application of artificial intelligence for smart services in logistics. There is a requirement to identify the logistic objects, to locate them, to control them and to record their states, in order to achieve a goal-oriented interaction in the sense of a holistic networking.