Introduction of a Product Family Penalty Function Using Physical Programming

Abstract

In an effort to increase customization for today’s highly competitive global markets, many companies are looking to product families to increase product variety and shorten product lead-times while reducing costs. The key to a successful product family is the common product platform around which the product family is derived. Building on our previous work in product family design, we introduce a product family penalty function (PFPF) in this paper to aid in the selection of common and scaling parameters for families of products derived from scalable product platforms. The implementation of the PFPF utilizes the powerful physical programming paradigm to formulate the problem in terms of physically meaningful parameters. To demonstrate the proposed approach, a family of electric motors is developed and compared against previous results. We find that the PFPF enables us to properly balance commonality and performance within the product family through the judicious selection of the common parameters that constitute the product platform and the scaling parameters used to instantiate the product family.