Balancing storage cost and customization time in product platform design: a bi-objective optimization model

Abstract In the modern market scenario governed by the Mass Customization paradigm, the so-called delayed product differentiation (DPD) rose as a production strategy best balancing traditional Make-to-Stock (MTS) and Make-to-Order (MTO), potentially reducing storage cost and customization time. In industry, DPD uses product platforms, defined as a set of components forming a common structure, from which a stream of derivative variants is produced. Early-stage platforms, made of few components, limit their storage cost, increasing the time to customize and turn them into final variants. The literature widely discusses the product platform design problem, asking to explore quantitatively the trade-off between platform storage cost and customization time. This paper contributes to applied research in mass customization, proposing and applying a bi-objective optimization model able to assign the most suitable production strategy to each product variant among MTS, MTO, and DPD. In the case of DPD selection, the model designs the product platforms best balancing storage cost and customization time as the target metrics to optimize, subject to industrial constraints to produce and store them, matching each variant to the most suitable platform. A case study adapted from the electronic components sector exemplifies the use of the bi-objective model, supporting companies in managing high-variety mixes.