Optimal product portfolio formulation: Merging predictive data mining with analytical target cascading

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer survey data is translated into performance design targets. Second is platform architecture design that we model as a dynamic entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a predefined architecture is not present at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through our cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the enterprise level that maximize company profit.