Open Access

A rising complexity in production, volatility of orders and abundance of variation of products while time of planning is shortened calls for a flexible and powerful maintenance management. The goal of this paper is to introduce a concept which maximizes availability of production and minimizes contingency risk, maintenance expenses and cost of operation. Neither established reactive maintenance nor stand-alone condition-monitoring-systems deliver viable and efficient solutions. Most of the time, Condition-Monitoring Systems (CMS), Computerized Maintenance Management Systems (CMMS) and Manufacturing Execution System (MES) are custom-designed stand-alone solutions and thus far cannot be used as integrated components of one optimized maintenance management system. Our concept will use “Smart Objects” to illustrate the individual machines and their maintenance-relevant components in one software application. These Smart Objects are able to declare their own need of maintenance as well as automatically allocate maintenance priorities. This allows for the first time the coordination of maintenance measures depending on machine states and production with the help of powerful, multi-objective optimization algorithms and an integrated software platform. Our concept introduces a way to realize this concept. It is methodically structured by the following procedure: The core of the concept is the Smart Object that contains a database of information that can be fed from CMS, CMMS and MES. At the same time the smart objects serve as intermediaries and data sources, thus conducting a bilateral communication between the three systems and smart objects. The smart object receives input from the CMS which determines the attrition pattern of the tool depending on the attrition curve and the worked on component. The CMS additionally transmits a forecast of the state of the tool. This forecast can be accurately enriched by the data (order and associated component) from the MES and hence be more precise. The Smart Object can therefore give an accurate forecast of when the tool will reach a certain level of attrition depending on the processed jobs. Using this data, the IPS can again set a timeframe for the next maintenance task and transmit this to the smart object. This maintenance task can be read by the MES and interpreted as a production order but with special requirements and parameters. Ideally, the MES plans this order into the setup or waiting periods of the machine, thus guaranteeing maximum machine availability.

Climate change, one of the most significant threats to humanity, underscores the urgent need to advance the circular economy, particularly in promoting sustainability. Implementing R-strategies such as refurbishment and remanufacturing within the home appliances industry is central to establishing sustainable and environmentally friendly business models. Key processes, such as workpiece disassembly, reassembly, and component status evaluation, are inherently more complex and variable than conventional production workflows. This requires workers with extensive experience handling comparable products and a deep understanding of critical decision-making processes. Due to the skilled worker shortage and the impact of demographic shifts, developing precise and comprehensive work instructions is increasingly vital for maintaining process quality. A worker assistance system is essential to meet this need, providing robust decision support to ensure accurate and sufficient decision-making. This paper proposes a framework for a worker assistance system tailored to the application of R-strategies in washing machine production. Data from an industrial scenario validates the framework and identifies critical information that should be incorporated into the system, enhancing the applicability and feasibility of the framework in real-world contexts. Within the framework, potential data sources and information flows are proposed, and illustrative processes are employed to demonstrate the derivation of work instructions utilizing the framework.

Aufgrund verschiedener Faktoren gestalten Unternehmen ihre Wertschöpfungsketten nachhaltiger. In diesem Zusammenhang gewinnt die Rückführung von Altprodukten an Bedeutung. Die Reverse-Logistics optimiert die damit verbundenen Prozesse. Damit einher gehen Herausforderungen wie schwankende Mengen an zurückgehenden Produkten und unvorhersehbare Rückläufe. Im Rahmen des Forschungsprojekts DiCES entstehen digitale Modelle, mit denen sich eine Kreislaufwirtschaft kostengünstig umsetzen lässt.

Private 5G networks, which are localized and designed for specific organizations or facilities, enable industrial production environments to transform by providing unparalleled opportunities for enhanced operational efficiency, flexibility, and digital connectivity. Based on four expert interviews and a detailed literature review for further validation, this paper presents a holistic roadmap for implementing private 5G networks in production settings. This roadmap adopts a phased approach, beginning with awareness and needs assessment, followed by the development of specific technical concepts, pilot implementation, network scalability, and ongoing optimization. Each phase is designed to address unique challenges and critical decisions involved in the successful deployment of 5G, including use case identification (e.g., predictive maintenance and autonomous systems), network architecture, partnership models, and integration with legacy IT infrastructure. This guided approach provides production facilities a pragmatic framework to navigate the technical, organizational, and economic complexities of 5G implementation, ensuring alignment with industry requirements and future technological developments. The proposed roadmap synthesizes insights from subject matter experts and recent literature, providing a sustainable, adaptable, and resilient strategy for 5G network deployment in production environments.