Development of a Product Life-Cycle Cost Estimation Model to Support Engineering Decision-Making in a Multi-Generational Product Development Environment

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Development of a Product Life-Cycle Cost Estimation Model to Support Engineering Decision-Making in a Multi-Generational Product Development Environment

Journal of Cost Analysis and Parametrics

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Abstract:

The research under product development domain identifies and implements new concepts in design, planning, manufacturing, testing, and service sectors to keep up with the market demands. Many product developments are multi-generational in nature and may require redesigning the product at each generation because an optimized strategy for a single generation may not be the best option in the multi-generation scenarios. With this regard, this article proposes a novel product life-cycle cost estimation model to support engineering decision-making in a multi-generational product development environment. This cost estimation model develops a mathematical formulation which has three major cost constituents: development cost, service cost, and associated risks cost. The risks cost further includes three components coming from technical risk, market risk, and operation risk. The developed mathematical formulation measures the on-going multi-generational product developments status, addresses its basic needs such as design for future reuse. It also serves as a strategic decision-making tool for successful project management. For the purpose of validating the proposed cost model, an empirical case study of a manufacturing-based product (gas turbine) is illustrated by considering three candidate concepts. This cost estimation is very handy to those design engineers who have a little idea of cost analysis when they design a product. It assists design teams by accurately forecasting and strategically planning a project and by making decisions which are compatible with future generations at a relatively cheaper cost. Thus, this article aims to provide a new direction and approach to maximize the business profit earned from multi-generational products.

Authors:

Xianming Cai received a Ph.D. degree in Industrial and Systems Engineering from Wayne State University, Detroit, MI in 2010. He is currently working with Seimens Engery Inc. as a Project Engineer. His research paper has appeared in Int. J. of Six Sigma and Competitive Advantage. His areas of interest for research include integrated product development (IPD), lean six-sigma implementation, process engineering, systems engineering and statistical analysis.

Satish Tyagi received B.S. and M.S. degrees from NIFFT, Ranchi, India and University of Louisiana at Lafayette, LA in 2008 and 2010, respectively. He is currently pursuing a Ph.D. in Department of Industrial and Systems Engineering at Wayne State University. His papers appeared in journals such as IEEE-SMC Part-C, Engineering Applications of Artificial Intelligence, International Journal of Production Economics, International Journal of Production Research, Virtual and Physical Prototyping, etc. His areas of interest for research include integrated product development, lean six-sigma implementation, information modeling and semantic alignment, and statistical analysis.