Implementing Delayed Product Differentiation in manufacturing systems: Heuristic development and industrial validation

In mass customization environments, managing product variety while maintaining efficiency and responsiveness is a critical challenge. Delayed Product Differentiation (DPD) addresses this by introducing product platforms-intermediate assemblies produced under Make-to-Stock (MTS) and later customized via Make-toOrder (MTO). However, existing optimization-based models for platform design often face scalability issues in industrial practice. This paper proposes a two-step heuristic methodology for product family grouping and platform design, guided by two novel indicators: the Platform Personalization Index (PPI) and the Marginal Customization Impact (MCI). These metrics evaluate and reduce customization effort by iteratively refining platform structures. The methodology is applied to two real-world case studies in the piping and automotive sectors. Results demonstrate up to 38 % reduction in customization time and a significant simplification of platform management. When benchmarked against a state-of-the-art optimization model, the proposed heuristic achieves competitive results with negligible computation time, confirming its scalability and industrial applicability. Beyond its empirical validation, the study contributes a generalizable framework that explicitly integrates technological precedence constraints into platform design, bridging the gap between theoretical optimization models and practical decision-support tools for high-variety manufacturing.