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Product Performance Evolution Prediction by Lotka-Volterra Equations

[+] Author Affiliations
Guanglu Zhang, Daniel A. McAdams, Milad Mohammadi Darani, Venkatesh Shankar

Texas A&M University, College Station, TX

Paper No. DETC2017-67369, pp. V007T06A014; 8 pages
doi:10.1115/DETC2017-67369
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 29th International Conference on Design Theory and Methodology
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5821-9
  • Copyright © 2017 by ASME

abstract

During the development planning of a new product, designers rely on the prediction of the product performance to make business investments and frame design strategy. The S-curve model is commonly used for this purpose, but it has several drawbacks. A significant volume of product performance data doesn’t fit well with an S-curve. An S-curve model is also not capable of showing what factors shape the future performance of a product and how designers can change it. In this paper, Lotka-Volterra equations, which subsume both the logistic S-curve model and Moore’s Law, are used to describe the interaction between a product (system technology) and the components and elements (component technologies) that are combined to form the product. The equations are simplified by a relationship table and a maturation evaluation process as a two-step simplification. The historical performance data of the system and its components are fitted by the simplified Lotka-Volterra equations. The methods developed here allow designers to predict the performances of a product and its components quantitatively by the simplified Lotka-Volterra equations. The methods also shed light on the extent of performance impact from a specific module on a product, which is valuable for identifying the key features of a product and thus making outsourcing decisions. Smart phones are used as an example to demonstrate the two-step simplification. The data fitting method is validated by the time history performance data of airliners and turbofan aero-engines.

Copyright © 2017 by ASME

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