Selecting Technology Alternatives for Product Families Through Technological Coverage and Functional Verification

Abstract

In product platform development the technology selection issue has seldom been studied. This article tries to fill this gap by introducing an original Technology Selection Method (TSM) to help designers to manage technology selection within the design of a product family. This method is based on technological coverage and functional verification, including the behaviour of the future product family. Practically, the proposed approach uses a multi-objective analysis based on dimensional analysis theory to choose the best technology available. The developed approach has three major points of interest. First, dimensional analysis theory is considered as a specific type of multi-objective optimisation approach which aggregates attributes using a weighting method based on the laws of physics. This manner of viewing dimensional analysis theory provides a scientific coherence to the weighting process which is not obtainable with other multi-objective methods. Second, the principle of similarity included in dimensional analysis theory is extended and allows a range of technologies and range of functions to be compared in a unique design space. Third, the dimensional analysis provides a powerful simulating tool for studying different kinds of behaviours and interactions between attributes. Consequently, dynamic aspects can be analysed and robustness analyses can be performed. The interest of such a method is highlighted through a case study involving a family of small excavators. We suggest that the potential scope of the approach is broad and our aim is to demonstrate the entire scope of the approach in future research.

Usage Coverage Model for Choice Modeling: Principles

“…Additionally, evidence supported that contexts could be differentiated based upon functional attributes, indicating a link between engineering parameters and perceived usefulness, which occurs under the influence of different usage contexts. This underscores the benefits of going far beyond using rough functional attributes, as done in Coatanea et al [8].…”