Friederike Stefanowski

Manufacturing facilities are becoming increasingly vulnerable to erratic shocks and uncertainties that have the potential to exert disruptive effects on operations. Global crises, such as the COVID-19 pandemic, the energy crisis, and the Suez Canal blockade, highlight the vulnerability of value networks that extend beyond company boundaries. In the absence of effective strategies to address these disruptions, manufacturing companies may face the risk of losing their competitive edge. While flexibility may suffice for minor risks, more substantial impacts necessitate a significant degree of adaptability and robustness. The promotion of organizational resilience, defined as the capacity to adapt to evolving circumstances swiftly, constitutes an ongoing strategic process that is pivotal to the effective creation of operational value. This publication delineates the methodological approach of the PARMa research project. Within the research project, the vulnerability of manufacturing companies to internal and external uncertainties is being addressed. Moreover, PARMa aims to strengthen the adaptability of production companies through proactive resilience management. In the event of sudden shocks, this should ensure rapid and targeted action, prevent operational disruptions and maintain competitiveness.

Sequence‐dependent setups in job‐shop manufacturing create a trade‐off: raising productivity through setup‐optimized sequencing tends to reduce schedule reliability. This study presents a modeling approach that quantifies this trade‐off at the workstation level. Building on the production operating curve and the boundary case with many setup families, the approach links sequencing effects to mean work‐in‐process (WIP) and utilization. The model yields expressions for the performance‐based and the effective productivity gains, which are bounded by the relative setup share and can only be realized during active production time. It also provides an expression for the sequence‐induced standard deviation of relative lateness as a function of WIP and utilization. Sensitivity results show proportional dependence on the setup share and diminishing gains with increasing numbers of setup families, while a two‐point comparison demonstrates the trade‐off. The model provides consistent metrics for positioning workstations within the productivity–schedule‐reliability trade‐off and for supporting sequencing and WIP‐related operating decisions.