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The Hotelling multivariate control chart and the sample generalized variance |S| are used to monitor the mean and dispersion of vehicle build vision data including the pallet information to identify the non-conforming pallets that are used in body shops of FCA US LLC assembly plants. An iterative procedure and the Gaussian mixture model (GMM) are used to rank the non-conforming or bad pallets in the order of severity. The Hotelling multivariate T2 test statistic along with Mason-Tracy-Young (MYT) signal decomposition method is used to identify the features that are affected by the bad pallets. These algorithms were implemented in the Advanced Pallet Analysis module of the FCA US software Body Shop Analysis Toolbox (BSAT). The identified bad pallets are visualized in a scatter plot with a different color for each of the top bad pallets. The run chart of an affected feature confirms the bad pallet by highlighting data points from the bad pallet.

Dr. Edwards Deming spirited organizations “If you can't define what you do as a process, you don't know what your job is” (Weinstein, 1999). Significant effort has been conducted to engineer, deploy and control a process to product development. This coverage reflects impact of product development process on developing and producing consumer products effectively and successfully for the future. Reflecting on the past and observing mistakes and lessons learned would be key to help our companies to engineer future or modify existing product development processes. This paper examines views, types and needs of product development process from a six sigma perspective to enable deliver of competitive products with cost and time in mind. Learning from the past enlightens us to identify opportunities that would drive evolution and trend of product development process into the future. A recommended view is presented that changes the way product development process is designed and implemented.

Transfer functions, one of core components in Design for Six Sigma (DFSS), provide the needed relationships between design, process and materials parameters and the CTQs (Critical-to-Quality characteristics) in the product and process development cycle. Transfer function provides direct method for understanding and representing an over all product and process function. Transfer function also provides a strategy for customer voice cascade, function decomposition, physical modeling and concept generation. The concept of transfer function is not new. However, the development of transfer function is not trivial and is a creative and challenging task. In part I of this paper, we will discuss how to develop a transfer function in the DFSS framework. In part II of this paper, we devote our efforts in the discussion of selecting the best transfer function for design evaluation and optimization.