Data-Driven Function Network Analysis for Product Platform Planning: A Case Study of Spherical Rolling Robots

Abstract

A properly designed product-system platform can reduce the cost and lead-time to design and develop a product family and thus achieve the tradeoff between economy of scope from product variety and economy of scale from platform sharing. Traditionally, product platform planning uses heuristic and manual approaches and relies on expertise and intuition. In this paper, we propose a data-driven method to draw the boundary of a platform, complementing other platform design approaches and assisting designers in the architecting process. The method generates a network of functions through relationships of their co-occurrences in prior designs of a product domain, and uses a network analysis algorithm to identify an optimal core-periphery structure. Functions identified in the network core co-occur cohesively and frequently with one another in prior designs, and thus are suggested for inclusion in the potential platform to be shared across a variety of product-systems with peripheral functions. We apply the method to identifying the platform functions for spherical rolling robots, based on patent data.