Scalable product family design: case study of axial piston pumps

Abstract

Many companies are trying to generate product variants through platforming to cater to diverse customer needs in a cost-effective fashion. Although product platforming may provide a cost-effective solution, platforming may also render undesirable performance degradation of individual product variants. Creating product variants based on multiple platforms instead of a single platform can be adopted as a strategy to reduce performance degradation. In the current paper, a methodology is proposed to determine the minimum number of scalable platforms needed for creating known product variants by considering the trade off between cost effectiveness and performance degradation. The solution methodology also provides values of common and scalable design variables for each platform. The objective function is defined as minimization of the weighted demand and total cost of providing each variant. The total cost is a function of the cost of each product variant and the cost associated with performance loss owing to platforming. The cost of performance loss is estimated using the Taguchi loss function. The application of the methodology is demonstrated using the case of a family of axial piston pumps. This case of axial piston pumps described in this study is new to the product platforming literature and can be potentially used as a benchmark problem by other researchers in the field to test the efficacy of existing scalable product platforming approaches.

Keywords:

• Product platforms
• Multiple platforms
• Axial piston pump
• Scaling variables

Acknowledgements

The authors would like to thank Mr Shyam Jayakeerthi, Mr Ramesh for sharing their knowledge on axial piston pumps and Mr Madwaraj Rao for his guidance during programming the algorithm using MATLAB. Partial support for this work came from the National Science Foundation under Grant No. IIS-0325415. The authors would also like to thank Mr Abhijit Choudhury for his kind help in editing the paper, and double checking the results of the case study. Any opinions, findings, and conclusions or recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.