Introduction of a product family penalty function using physical programming

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 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 parameters and scaling variables for scaled-based families of products derived from 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 scaling variables and the common parameters that constitute the product platform.