Methodology for Dynamic Dimensioning of Re-Assembly Factories

The transformation toward a circular economy requires the industrial scaling of re-assembly processes. However, re-assembly factories face significant uncertainties regarding product condition, variant diversity, and volatile demand, which push existing factory planning approaches to their limits. This dissertation addresses the central challenge of dynamic dimensioning of re-assembly factories - the continuous adaptation of capacities and production structures to changing boundary conditions. In contrast to conventional concepts of flexibility and changeability, which exhibit either insufficient scopes of change or inadequate adaptation speeds, this work develops a methodology for proactive, data-driven dimensioning. The overall solution consists of three essential modules: First, the digital product passport that contains various data classes with different levels of completeness, detail, and quality. Second, a model of cause-effect relationships that links product data to capacity planning requirements. Third, capacity planning methods ranging from rough estimation to second-precise operational details. The core of the solution is the systematic utilization of digital product passports to derive ReX strategies that serve as a link between product requirements (demand) and production system configuration (supply). The methodology encompasses the development of a descriptive model of strategic premises, an explanatory model of cause-effect relationships, and a decision model for adaptive capacity planning and production segmentation. This work contributes to enabling industrial re-assembly factories to operate in an economically scalable manner, thereby significantly supporting the transformation toward a circular economy.