An Empirical Study of Product Functional Families: Analyzing Key Performance Metric Trends to Derive Actionable Design Insights
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To satisfy customers and get market success, an engineered product must be designed to match the intended application. It is asserted here that victorious products in mature markets have followed a set of implicit rules which represent essential (but not sufficient) criteria for success. This research seeks to discover and document an understanding of some of these implicit rules, and convert them into actionable design insights. The study will start with reverse engineering the product definition information (product requirements and customer needs) of several carefully selected sets of inter-related products.
This product definition information may then be linked to information about the usage context for which each product was designed. Insights gleaned from the analysis of this data will then be made accessible to engineers in the form of: (1) knowledge encapsulation modules providing familiarity with several product design contexts, (2) Ashby-style plots graphically depicting products successful in a provided design context in terms of normalized key performance metrics, and (3) a Design by Analogy method showing trends of how certain functions are often solved based on characteristics of the design context. It is hypothesized that when designing for product requires in a design context outside their experience and expertise, engineers may create more effective designs when equipped with these design insights. This hypothesis will be tested formally through experimentation with design teams. The key contributions expected from this work are:
(1) an empirical data set of product definition information linked with usage context,
(2) an insightful comparison of products on the basis of normalized key performance metrics,
(3) documentation of energy function solutions correlated with the resulting key performance metrics, and
(4) documented actionable design insights.