Abstract Manufacturing firms increasingly adopt modular product architectures to manage variety, yet the economic consequences of architectural decisions remain difficult to quantify because traditional costing systems obscure how variants drive overhead. This study extends a previously developed data-driven framework by adding monetary valuation, direct material cost, and volume-dependent scale effects to activity-based estimates of engineering, procurement, planning, and data-administration workloads. The extended framework is applied across four industrial cases spanning engineer-to-order (ETO), make-to-order (MTO), and configure-to-order (CTO) environments using heterogeneous enterprise resource planning (ERP), computer-aided design (CAD), and configurator datasets. The results reveal a consistent pattern: architectural complexity is a dominant driver of overhead costs, and reducing internal variety lowers cross-functional workload to an extent that often outweighs increases in material expenditure. Conversely, attempts to minimize material cost in isolation can raise architectural complexity and generate substantial additional workload throughout the value chain, undermining profitability. The findings show that overhead is more sensitive to product variety than direct cost and that a holistic, data-driven evaluation is essential for sound architectural decision-making. Together, the cases demonstrate that overhead costs associated with product variety can outweigh direct-cost effects, while the extended framework provides a practical means of quantifying these effects in industrial settings.
Grønvald et al. (Thu,) studied this question.
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