Abstract
The main focus of this paper is how the concept design phase of the product development process can be improved by using an objective data-driven approach in selecting a final concept design to progress further. A quantitative new test-bed ‘Product Optimisation Value Engineering’ (PROVEN) is presented to critically assess new and evolving powertrain technologies at the concept design phase. The new test-bed has the ability to define a technology value map to assess multiple technical options as a function of its attributes, whose precise values can be determined at a given cost. A mathematical model that incorporates a highly adaptable, data-driven and multi-attribute value approach to product specification and conceptual design is developed, novel to the concept design process. This creates a substantially optimised product offering to the market, reducing overall development costs while achieving customer satisfaction.
Highlights
The early phase of product development is referred to as the concept development phase which ends with a final concept decision
Given a scenario where a new vehicle design requires a 10% fuel economy improvement, a business equation can be defined to determine how much the business is prepared to pay for new technology to make economic sense
The major under-lying issue observed in using the Pugh approach for the air induction case study was the translation of converting performance values related to a concept design into a rated score between (1-10)
Summary
The early phase of product development is referred to as the concept development phase which ends with a final concept decision. Given a scenario where a new vehicle design requires a 10% fuel economy improvement, a business equation can be defined to determine how much the business is prepared to pay for new technology to make economic sense This can be achieved by taking into account the following factors to estimate a cost the company is willing to pay to deliver attribute improvements for a new product: 1) Projected sales volume of new vehicles to be sold determining economies of scale; 2) Customer willingness to pay for improved attributes linked to overall cost of vehicle ownership and initial purchase price of vehicle; 3) Calculated increased value to overall brand strength for future products. The end result confirms the performance relationship between a set of independent variables and a dependant variable Further analysis including both linear and non-linear regression can be performed for all other critical ‘x’ inputs to determine the required performance for each powertrain sub-system to achieve the required target attributes for a new vehicle programme. As Cook’s value equation and the multi-attribute approach are essentially the centre piece of this research in evaluating concept designs, it is important to test the theory by means of a case study[14]
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