Typeset

Emergency organizations such as fire departments or technical relief organizations are expected to react very quickly – sometimes to unknown situations – and provide the appropriate assistance. Can principles used in these organizations be transferred to IT support, e.g. for ERP systems? An experiment in an IT service unit investigates this question – with surprising results.

With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.

The use of artificial intelligence (AI) is becoming more important for a variety of industries, which is why enterprise resource planning (ERP) systems also offer many possible uses of AI. Due to their newly acquired, AI-based adaptability and learning abilities, modern AI-integrated ERP systems are able to develop competencies, plan processes, make forecasts and interact intelligently with humans. It is not uncommon for such systems to initiate major structural changes for companies and to open up new markets and design areas [1]. In order to measure the progress of an ERP system in terms of AI, the Center for Enterprise Research (CER) has developed an AI maturity model. Building on this model, a tool for evaluating AI integration in an ERP system should be able to showcase potential for development and enable market comparison.

Any networked information system that is used in the context of manufacturing and logistics in a factory can be referred to as factory software. This article describes six trends that will significantly influence the way software is used in factories in the near future. The trends are described in ascending order in terms of significance of impact.

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.

Digital twins (DT) for circular economy (CE) offer a promising approach as part of digital data ecosystems for more sustainable value creation. By mapping and analyzing product, component and material specific data along the li- fecycle, it is possible to address current challenges such as climate change and resource scarcity. Within Catena-X, specific solutions based on this cross-company exchanged data and information are developed. Here, the “R-Strategy Assistant” is presented. It is an application, which identifies the best CE-Strategy based on DT data at the end of a vehicle's life.