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Research Papers: Design Theory and Methodology

Technology Evolution Prediction Using Lotka–Volterra Equations

[+] Author and Article Information
Guanglu Zhang

Mem. ASME
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77840
e-mail: glzhang@tamu.edu

Daniel A. McAdams

Fellow ASME
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77840
e-mail: dmcadams@tamu.edu

Venkatesh Shankar

Center for Retailing Studies Mays
Business School,
Texas A&M University,
College Station, TX 77840
e-mail: vshankar@mays.tamu.edu

Milad Mohammadi Darani

Center for Retailing Studies Mays
Business School,
Texas A&M University,
College Station, TX 77840
e-mail: mmohammadi@mays.tamu.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 14, 2017; final manuscript received February 12, 2018; published online March 23, 2018. Assoc. Editor: Christopher Mattson.

J. Mech. Des 140(6), 061101 (Mar 23, 2018) (9 pages) Paper No: MD-17-1275; doi: 10.1115/1.4039448 History: Received April 14, 2017; Revised February 12, 2018

During the development planning of a new product, designers and entrepreneurs rely on the prediction of product performance to make business investment and design strategy decisions. Moore's law and the logistic S-curve model help make such predictions but suffer several drawbacks. In this paper, Lotka–Volterra equations 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 maturation evaluation in a two-step process. The performance data of the system and its components over time are modeled by simplified Lotka–Volterra equations. The methods developed here allow designers, entrepreneurs, and policy makers to predict the performances of a product and its components quantitatively using the simplified Lotka–Volterra equations. The methods also shed light on the extent of performance impact from a specific module (component technology) on a product (system technology), which is valuable for identifying the key features of a product and for making outsourcing decisions. Smartphones are used as an example to demonstrate the two-step simplification process. The Lotka–Volterra model of technology evolution is validated by a case study of passenger airplanes and turbofan aero-engines. The case study shows that the data fitting and predictive performances of Lotka–Volterra equations exceed those of extant models.

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Figures

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Fig. 1

Two-step simplification of Lotka–Volterra equations for smart phone speed to open an app

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Fig. 2

Data fitting result for system technology performance (Passenger capacity*speed*ranger of passenger airplane) [30]

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Fig. 3

Data fitting result for component technology performance (take-off thrust of turbofan aero-engine) [31,32]

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Fig. 4

Predicted system technology performance on historical data (1960–1998)

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Fig. 5

Six steps to apply Lotka–Volterra equations in technology evolution prediction

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