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In the apparel industry, with demanding customers and an oversupply of products and shopping options, interactivity and individualization offer important opportunities for differentiation. However, digital business-to-consumer interactions have so far been limited to product configuration and viewing. This paper presents new, digitally supported approaches to integrate the customer into the collection development process of the apparel industry.

Against the backdrop of industrial transformation, medium-sized supplier firms can use digital technologies to optimize their work processes. New digital production systems offer one opportunity for organizing factory processes more efficiently. However, their introduction requires a “corporate culture 4.0” that is characterized by trust in decisions concerning the future design of such systems, despite the involved high uncertainties. Using the example of a digital tool management system, this article presents measures for strengthening such a corporate culture.

The analysis of machine data offers a lot of potential for manufacturing companies. It enables the identification and prognosis of deficits in industrial production processes. Using the example of additive manufacturing, a practice-oriented procedure to implement such an analysis is presented in this article. Using a gradient boosting algorithm, it is shown how a leakage error can be identified as well as predicted. Furthermore, requirements for the necessary database are discussed and practical recommendations for manufacturing companies are derived.

Applications such as mobile or industrial robotics gain increasing autonomous capabilities. Autonomous systems can adapt dynamically to user goals, perceive the environment and solve complex tasks without human intervention. During operation, unsafe system states may occur in a priori unknown scenarios, which can lead to an impairment or a safety risk. Self-healing is an inherent and necessary property of these systems. In this article, we present an architecture of self-healing systems. Using the example of an autonomous mobile robot, we present the results and possible application in a smart factory.

Procurement has the task of providing an organization with required but not self-produced goods. Due to the collapse of global supply chains during the SARS-COV2 pandemic, procurement faced major challenges. Goods that were actually easily available on global markets became critical bottlenecks. It turned out that additive manufacturing can mitigate these bottlenecks. For example, medical spare parts were produced using additive manufacturing. This article examines how additive manufacturing is changing the procurement risk of materials. A comparison is made between traditional and additive supply possibilities based on a survey. The result is a combined procurement strategy, which ensures an improved availability of critical goods.

Engineers are often uncertain about the application of machine learning (ML) due to the amount of different machine learning methods and the complexity of modeling. Thus, the use of ML applications in manufacturing companies remains behind the technical possibilities. This paper presents an intuitive ML guideline for engineers to reduce this uncertainty. The guideline comprises a process model with AI-based solutions to common problems of product development and production. An industrial example is used to demonstrate the functionality and the possibilities of the guide.

The introduction of digital technologies will provide completely new possibilities for the design and operation of supply chains in the future. A decisive step should be the decentralization of structures and processes inside firms which also shows effect on the cooperation between companies. It finally offers the opportunity to solve long-standing problems of supply chain management. When are companies ready to take this step? The technologies are.

Customer orientation and individualization will be two of the biggest challenges for companies. They need to expect their business model to become more complex as the range and diversity of their products and services will increase. Those who find a smart approach of managing the complexity will have a decisive competitive advantage. Those who use modularization across products, services, production and the organisation, will be prepared to deliver individual value propositions more quickly and their profitability will not be jeopardised by their own complexity.

The quality of injection-molded components is becoming increasingly important in polymer technology due to extended areas of application with higher mechanical loads. As established methods of quality assurance are increasingly reaching their limits, the digital twin as a basis for cross-process and cross-company data analysis opens up new possibilities in plastics technology for proactive and predictive monitoring and improvement of process and component quality when processing plastics into technical components.

Industry 4.0 is a political concept intended to help German manufacturing companies to exploit data potential. Today, maintenance activities are not proactive by current approaches. Decision support systems based on artificial intelligence allow a change here by even foresighted machine maintenance. However, AI’s opaque decision-making process represents a barrier for users, which endangers its effectiveness. Therefore, this article sheds light on both: the technological as well as social factor for the adoption of AI in Industry 4.0.