Managing Mass Customization through Delayed Product Differentiation: a bi-objective model for product platforms design

In recent years, the heterogeneity of customer needs caused a wide proliferation of product variants, asking industrial companies to adopt new strategies to remain competitive in the transition from mass production to mass customization. Traditional production strategies such as Make-to-Order (MTO) and Make-to-Stock (MTS) are no longer adequate for the efficient manufacturing of multiple product variants. In such a scenario, the Delayed Product Differentiation (DPD) rose as a hybrid strategy overcoming the main limitations of traditional production strategies, best balancing high product variety and quick response time with low storage cost through the so-called product platforms. These platforms are subsystems of components, forming a common base structure from which a stream of product variants can be efficiently derived. Platforms are produced and stocked in advance, following an MTS strategy, and customized into different variants after the order arrival, according to an MTO strategy. Platforms dimension affects in an opposite manner their storage cost and their customization time, as platforms made of few components require long time for customization activities, reducing the storage cost. The best platform design and association to product variants are open topics in current literature, having major impact on the trade-off between platform storage cost and customization time. This paper contributes to applied research in mass customization, proposing a bi-objective optimization model for platform design and association to product variants, determining at the same time the best production strategy among MTO, MTS and DPD for each product variant to best balance platforms storage cost and customization time. The model is applied to a reference case study providing a multi-scenario analysis about the main effects of the limitation in the number of possible platform types on platform configurations, customization tasks and their correspondent customization time and storage cost.