Digitalisierung

Companies are increasingly acquiring new technologies that enable higher quality and efficiency. Every technology adoption is also a change process which affects the employees and their work and thus needs to be managed in an optimal way. This article is focused on the importance of goal definition during a change process. To facilitate this process, a checklist with 81 goals is presented. The checklist was developed based on scientific literature and practice-oriented tools and can be used by project teams to focus their activities on a holistic change process and track the goal progress.

The complexity of logistic networks and systems is growing in today’s globalized world. Consequently, there is a rising demand on digitalization in this sector in order to gain a competitive advantage on the market. This paper introduces policy instruments suitable to boost the demand for uptake of information and communication technologies (ICT) in the logistics sector. Experts and stakeholders have been consulted on this topic and an EUwide survey has been launched. The consultations indicated barriers for the implementation of relevant technologies and showcased the appropriate demand-side policy measures able to overcome these barriers, thus motiva-ting companies to digitalize. As a result, several instruments to boost ICT-implementation in transport and logistics were recommended to the policy makers.

Digitalization in logistics is progressing. Cyber-Physical logistics systems are emerging and opening up new opportunities in the management of logistics processes. The associated potentials for error-free and efficient logistics is enormous - but the potentials do not open up automatically, despite initial quick wins in KPIs. More than ever, executives play a key role on the path to excellence in logistics performance. They must learn to „proactively manage“ in an increasingly networked workplace. Which new possibilities the increasing digitization offers for this difficult development process and how these potentials can be exploited, is the subject of the following article.

Digitalization changes industrial value-added processes as well as the expectations and requirements of customers, staff, suppliers and partners. For industrial companies, these changes bring risks and opportunities. They need to react and adapt their products and services to remain competitive and survive in the digital age. With service design and design thinking, companies can identify future-proof business areas to develop products and services that bring users added value. We will introduce you to the nature and effects of such design processes and outline the benefits for your company.

Social media and the digitalisation of products allow a direct view into product and service use and customer experience. This Product Usage Information may be used systematically in product and service development. However, this requires its interoperability with relevant processes and IT systems. This paper presents an approach and its implementation as an IT platform which fulfils these prerequisites.

Requirements for efficiency in manufacturing processes are steadily increasing in global competition. Through digitization and data analytics, not only efficiency but also flexibility and quality are to be increased. Cyber-physical systems, which use smart sensors and communication with manufacturing systems to independently identify, document and control process steps, can help solve this problem. These can be attached to many types of tools and thus also capture manual work processes. This article shows how a smart object with communication and positioning functionality accompanies a product to be manufactured through the manufacturing process and how e.g. manual screwing processes are recorded and analyzed by smart sensors.

The topic Internet of Things (IoT) is omnipresent and has already found its way into many living rooms under the term Smart Home [1]. Be it lamps, heaters or the television set - everything is interconnected and easy to control from anywhere. The corresponding counterpart in manufacturing is Industry 4.0, sometimes named the Industrial Internet of Things. Companies and research institutions are working intensively on the possibilities offered by the so-called fourth industrial revolution. The forward-looking positioning of the German economy in international competition requires that the development and distribution of concepts and solutions in the context of Industry 4.0 be actively shaped by the industry itself and thereby to play a pioneering role in innovation [2]. Due to the rapid pace of development, it is essential for German companies to identify, develop and adapt new methods and technologies at an early stage and to transform them into marketable solutions.

The core task of logistics can be described by the so-called “seven R”. IT means that the right product must be available in the right condition at the right time and at the right place. In addition, further key factors of a well-functioning logistics system are the right quantity, the right costs and the right information. Providing right or incorrect information at the wrong time can have a significant repercussion on the entire process. In particular, the human being who is the recipient of the information has the demanding task of correctly select and interpret it. Therefore, the human-machine interface plays a decisive role in the communication success between human employees and the digital systems surrounding the human at work. On that account, new user interfaces that do not burden people and provide information in a way that it can be perceived intuitively are needed in the future. This is where data glasses come into play.

Industrie 4.0 with the digitisation of products and processes offers companies a large pool of information for process optimization. In many cases these information cannot be used directly in the textile industry, as raw materials are subject to natural fluctuations and the influencing factors and interactions of many product and process parameters are only partially known. In this contribution, an approach is presented that combines information from production with the experience of the employees and thus supports product and process optimization. The approach is based on the machine learning method “Case-Based Reasoning”.