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Research Papers

Observations From Radical Innovation Projects Considering the Company Context

[+] Author and Article Information
Bernard Yannou

Professor
e-mail: bernard.yannou@ecp.fr

Marija Jankovic

Assistant Professor
e-mail: marija.jankovic@ecp.fr

Yann Leroy

Assistant Professor
e-mail: yann.leroy@ecp.fr
Laboratoire Genie Industriel, Ecole Centrale Paris,
Chatenay-Malabry 92290, France

Gül E. Okudan Kremer

Associate Professor
Harold and Inge Marcus Department of
Industrial and Manufacturing Engineering,
Penn State University,
361 Leonhard Building,
University Park, PA 16802
e-mail: gkremer@psu.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received February 5, 2012; final manuscript received October 27, 2012; published online January 7, 2013. Assoc. Editor: Jonathan Cagan.

J. Mech. Des 135(2), 021005 (Jan 07, 2013) (17 pages) Paper No: MD-12-1093; doi: 10.1115/1.4023150 History: Received February 05, 2012; Revised October 27, 2012

The development of product-service innovation projects within the context of a company is not yet supported by clear theories and methodologies. Our objective is to analyze innovation and idea generation for such projects from the fuzzy front end to the selected design concept, assessing their potential to be successfully developed and launched on the market. We present a protocol study, using which data derived from 19 innovation projects of five types and conducted by 86 students are analyzed. Sixty-one variables are observed, thus generating 700 data vectors. Bayesian network learning is used to explore conditional inferences among these variables. We examine conditional probabilities between the innovation process means and the significant results produced for the company, modulated by the influence of contextual variables. A number of surprising findings are drawn about the link between problem setting and problem solving processes, the importance of certain contextual variables, and the potential discrepancies between the apparent and produced results of innovative projects. Conducted analyses imply the need for novel innovation evaluation frameworks.

Copyright © 2013 by ASME
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Figures

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Fig. 1

The conceptual design process defined by Cross [7]

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Fig. 2

Pugh's conceptual design process [9]

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Fig. 3

Liu et al. proposition of an “ideal” design process and design support tool [8]

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Fig. 4

Influencing factors on creativity [34]

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Fig. 5

Our observation protocol of innovative projects

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Fig. 6

The RID innovation wheel: From initial idea to feasibility and innovation dossier through ideal need, perimeter of ambition, brief(s), concepts

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Fig. 7

The value machine of an innovative project using RID methodology

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Fig. 8

Tripod concept for weighting machine for African children

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Fig. 9

Percentage of product versus service embedded in the initial design statement of the 5 projects: variable P/S

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Fig. 10

Evaluation consensus reached considering the knowledge required for the realization of the project according to type of project

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Fig. 11

UML model representing the data collection and RID experimental protocol

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Fig. 12

Supervised network on D20 (refer to tables 1 to 4 for the meaning of variables). (Green—design team features, red—results, blue—project context, yellow—deliverables, orange—quality.)

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Fig. 13

Semisupervised network on overall quality of problem solving D21

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Fig. 14

Semisupervised network concerning the Organization of creativity: brainstorming workshops based upon identified innovation leads D9

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Fig. 15

A major finding of study: the overall quality of problem setting process dramatically influences the overall quality of problem solving process

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Fig. 16

Supervised learning on R15 “The chosen design concept is innovative, mature, and sufficiently validated”

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