smart manufacturing

Digital twins are frequently referred to as a promising approach for reducing greenhouse gas (GHG) emissions in industrial production; however, robust empirical evidence of their benefits under real-world conditions is largely lacking. In this case study, the emission reduction potential of a digital twin—as a conceptually described target system—is quantified ex-ante via the example of a test bench for hydraulic pumps. To this end, the GHG emissions of the original test plan for the year 2025 are determined based on actual measured energy consumption of the tested pumps and time-resolved grid electricity emission intensities. This is followed by a rule-based rescheduling, in which energy-intensive test processes are shifted to time intervals with lower emissions. The rescheduling takes operational constraints into account so that processes and equipment remain unchanged. The savings potential is determined by comparing the GHG emissions of the reference and the optimized case.

Manual picking remains common due to the high initial cost of support systems. This paper reviews existing technologies, presents an exploratory vision-based prototype, and examines existing literature that explores how combining object detection with language systems could enhance manual workflows. The findings suggest a promising, low-cost direction for worker support in logistics.

In today’s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime.

Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which ...