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Lean Six Sigma is an approach that is gaining momentum both in manufacturing and service industries. Design for Lean Six Sigma (DFLSS) is an outgrowth of the DFSS and Lean Six Sigma approaches. The essence of DFLSS is to ensure design quality and predictability during the early design phases and the approach employs a structured integrated product development methodology and a comprehensive set of robust tools to drive product quality, innovation, faster time to market, and lower product costs. When it comes to automotive Product Development, applying lean principles and DFSS together becomes more of a challenge within the existing PD system. While the benefits of DFLSS present an attractive proposition in a fiercely competitive market it brings its own challenges as to how to deploy it for maximum benefits. This paper examines the challenges, potential and opportunities for DFLSS in the automotive industry and presents a vision for integrating it in to the Product Development System.

Metamodeling has been widely used in place of complex numerically intensive simulations to perform design reliability assessment and optimization. Due to cost and time constraints, most complex simulations can only afford a limited number of runs with a relatively large number of factors. The accuracy of a metamodel is affected by the degree of the underlying non-linearity, the sample size, the sampling strategy, and the type of the metamodel. In this study, the effect of the DOE optimality criteria on the accuracy of the Kriging metamodel is investigated under scarce sampling plans. Uniformity optimization is performed using some of the most popular uniformity measures, such as Centered Discrepancy (CL2), Maximin, and Entropy criteria. Case studies consist of eight analytical closed-form functions drawn mostly from real engineering applications with five to seven factors each and various degrees of non-linearity.