Künstliche Intelligenz

The paper specifies the genAI support needs for industrial maintenance against the background of a sociotechnical systems perspective. Emphasizing two needs, accessing implicit operator knowledge and prioritizing complex regulatory knowledge, a multi-layer architecture is outlined for an AI-based context-sensitive maintenance assistance system (MAS). The main purpose is to bridge knowledge gaps with genAI if human expertise and human implicit knowledge are not available and to cope with sub-process-specific challenges of multiple regulations. The MAS facilitates access to technical knowledge, distributes expertise, and shares implicit knowledge of experienced operators across different layers of information processing. The approach goes beyond standardization and has a high potential to enhance organizational as well as individual resilience.

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.

Artificial intelligence is increasingly implemented in companies, but often without clear organizational anchoring. This article evaluates centralized, decentralized, hybrid, and project-based frameworks for the structural integration of artificial intelligence in corporate organizations. A decision table provides guidance for selecting suitable models. In the conclusion, further open research questions are posed.

Despite the commercial availability of simulation tools, using of discrete-event simulation for complex production and logistics systems is becoming increasingly challenging. It requires extensive expertise, high data quality, and considerable time and financial resources. For many years, therefore, there has been high demand for methodological and organizational support for the conduction of simulation studies. This article is based on an analysis of relevant publications and aims to classify previous research on improving the use of simulation. It also raises the question of the need for assistance in applying discrete event simulation and identifies areas for action.

The work system is the key unit of analysis within the discipline of human factors/ergonomics (HFE); it is also considered a fundamental element for value creation within other domains. Its concept is based on sociotechnical systems theory and, within HFE, it conveys a distinctly human-centered perspective. So far, work system models have focused on one or several people working within a defined setting as the only (intelligent) actors within the system. The introduction of generative artificial intelligence (genAI) into work systems, particularly as an intelligent and autonomous actor (agent) with potentially specific social abilities and personality traits, calls for reconceptualization. This article elaborates on the new requirements related to the introduction of genAI and develops a human-centered AI-paired work system model that recognizes the significantly expanded capabilities of AI-enabled collaborative social robots.

Dashboards play a key role in informed business decisions. Based on findings from an action research process, this article shows how company-specific solutions can be systematically developed and bad investments avoided. The provision of IT capacities, securing data access, formulating requirements, and developing the data model prove to be particularly critical.

Personnel deployment planning in industrial maintenance is a complex challenge, as dispatchers often have to match incomplete customer requests with the appropriate employee skills. An AI-based assistance system can help by automatically analyzing relevant data and providing well-founded suggestions for employee selection. This article describes the user-centered development and introduction of such a system as part of a pilot project at a medium-sized service provider. The user-centered design ensures that dispatchers retain their autonomy. Involving employees from the outset creates acceptance and promotes a deeper understanding of the system’s advantages.

How can artificial intelligence (AI) automate production planning and control? This study examines its potential to enhance efficiency in modern production environments. The focus is on establishing a robust data infrastructure that integrates real-time, historical, and contextual data to create a solid basis for AI models. Reinforcement learning (RL) is applied to aid automation. A roadmap for implementation, focusing on practical application, is presented. This roadmap incorporates simulation-based training methods and outlines strategies for continuous improvement and adaptation of production processes.