Autor: Wilfried Sihn

The engineer-to-order (ETO) production approach is gaining relevance in response to increasing demand for individualized products and small batch sizes. However, ETO inherently reduces the economies of scale typically achieved in series production, as each order requires tailored engineering and production steps. This loss of efficiency can be mitigated through demand-driven and context-aware information provision throughout the product development process. A recommendation system based on semantic artificial intelligence (AI) and machine learning can support this by i) analyzing historical data and prior knowledge, for example drawings or a bill of materials from previous projects, and ii) making automated suggestions, like reusing existing designs or proposing design alternatives, thus compensating for the aforementioned effects.

For decades, different approaches have been designed and implemented for optimizing maintenance management systems in manufacturing, but maintenance is still known as a cost driver rather than an enabler or driver of value creation in production management. The challenges for companies facing this change are many, ranging from doubts about the economic efficiency of maintenance to lack of data and flexibility in the maintenance organization to shortage of skilled workers and lack of prioritization of sustainability and resilience. The vision of the Maintenance-Free-Factory (M2F) is to harness the potential of digitalisation as a driver of sustainability and resilience, enabling a fundamental transformation of maintenance. At the heart of the M2F is value-added production without maintenance disruption. Maintenance activities are carried out during unproductive production times. Therefore, five elements – digital maintenance processes, data-based maintenance, decision support ...

The continuous acquisition of new digital competences and the development of situational learning assistance systems will become more important than ever in the coming years, because the world of work is becoming more complex, more informative and all above more data-driven. Jobs are changing due to increasing digitalisation, whereby the use of modern technologies must be designed in a way, that employees can continue to work productively in the company despite these changes and benefit purposefully from digital solutions. The research results presented under the main topic „Competence development and learning assistance systems for the data-driven future“ address this problem of state of the art technologies in the workplace and their effects on workers. The members of the Scientific Society for Work and Business Organisation (WGAB) present innovative concepts and research results for practitioners and scientists and thus provide valuable input for current challenges.

Precise stochastic demand calculation is the key to successful material planning, i. e. to always have exactly the right quantity on hand. However, decision-makers are faced with the dilemma of which of the many forecasting methods they should use, adapted to the item properties as much as possible. This paper examines the optimization potential of a self-developed automatically optimizing forecasting approach based on ten common forecasting methods, which are evaluated using two case studies from the capital goods industry.