Design

Public discussions on the future of labour in the era of digitalization are dominated by notions of self-driving cars, factories without workers or fully automatic logistics. This creates fears of mass destruction of jobs and shrinking employment rates in the future. At the same time, it has led to intense debates on an unconditional basic income: While productivity would rise, a substantial drop in the number of jobs would question the income distribution mechanism our working societies are currently built on. This article argues that while replacement of existing jobs - or at least tasks - by technology will happen and has always happened, this is only one side of the coin. The future of labour markets will be more complex. This is discussed in a macroeconomic, firm and international dimension.

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.

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.

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?