Amid growing environmental and societal concerns about energy use, companies face increasing pressure to adopt sustainable manufacturing practices. The European Union’s guiding principles, aimed in part at achieving climate neutrality and fostering green growth, underscore the need for systematic, data-driven approaches to energy efficiency. This involves the measurement, monitoring, and analysis of energy data. However, identifying efficiency potentials often relies on expert knowledge, which is becoming increasingly scarce due to skilled labor shortages. Expert systems offer a solution by consolidating and analyzing data to automatically identify energy-saving opportunities. These systems leverage stored expertise, applying it to measurement data to generate actionable insights, while their explicit knowledge representation and transparent reasoning facilitate knowledge transfer. Despite their potential, most expert systems are developed intuitively and tailored to specific applications, limiting their broader adoption. To address this, we propose a holistic framework for systematic expert system development, supported by defined personas and an expert system shell serving as a software template. The framework is demonstrated and evaluated through its application in a metalworking process chain.

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems.

As part of the European Green Deal, Germany is committed to greenhouse gas neutrality by 2045. To accommodate these goals, manufacturing enterprises are aiming to futureproof their sites through site transformations towards sustainability. Such efforts are affected by an inherent competition for usable space, as each successive transformation changes the overall site composition. Despite economic sustainability having the highest priority for enterprises, land is nonetheless being allocated to socio-environmental measures. To gauge current industrial efforts for sustainable transformation, a interview study with 19 experts was conducted. The experts represented the fields of site planning, facility management, and sustainability management. The study primarily focused on plans for next generational energy systems, including challenges faced during their planning and execution, supplemented by how land is used for further sustainability measures. The interviews were analyzed using Kuckartz’ qualitative content analysis. The results show proactive engagement by enterprises to meet climate goals ahead of regional policies. Within this scope, the goal of ensuring economic competitiveness is pursued through a variety of different means, such as security of energy supply, self-sufficiency, adaptability to new technologies and regulations, and staff retention. Organizational challenges were found to outweigh financial ones.

Amid growing environmental and societal concerns about energy use, companies face increasing pressure to adopt sustainable manufacturing practices. The European Union’s guiding principles, aimed in part at achieving climate neutrality and fostering green growth, underscore the need for systematic, data-driven approaches to energy efficiency. This involves the measurement, monitoring, and analysis of energy data. However, identifying efficiency potentials often relies on expert knowledge, which is becoming increasingly scarce due to skilled labor shortages. Expert systems offer a solution by consolidating and analyzing data to automatically identify energy-saving opportunities. These systems leverage stored expertise, applying it to measurement data to generate actionable insights, while their explicit knowledge representation and transparent reasoning facilitate knowledge transfer. Despite their potential, most expert systems are developed intuitively and tailored to specific applications, limiting their broader adoption. To address this, we propose a holistic framework for systematic expert system development, supported by defined personas and an expert system shell serving as a software template. The framework is demonstrated and evaluated through its application in a metalworking process chain.

Deep Reinforcement Learning (DRL) can optimize the operating strategies of industrial energy supply systems (IESS), enhancing energy efficiency and flexibility while reducing costs. This work conducts and evaluates a real-world application of Proximal Policy Optimization (PPO), a widely used DRL algorithm, on the complex ETA Research Factory’s supply system. Our approach involves five steps: system boundary definition and data accumulation, modelling and validation, DRL implementation, simulation-based evaluation and finally, application of the DRL-based controller on the real system. In simulation experiments, the controller demonstrates a 43% reduction in operational costs with respect to the conventional controller while maintaining operational stability within safety limits, thus proving to be a cost-effective and reliable solution. During real-world application, the DRL-based controller upheld safety and robustness. Future research should aim at establishing comparability under various environmental conditions and closing the gap between simulation and reality.