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The Cost of Learning from Failures and Mistakes in Product Design: Reviewing the Literature

Published online by Cambridge University Press:  26 July 2019

Ali Shafqat*
Affiliation:
Norwegian University of Science and Technology;
Josef Oehmen
Affiliation:
Technical University of Denmark
Torgeir Welo
Affiliation:
Norwegian University of Science and Technology;
Pelle Willumsen
Affiliation:
Technical University of Denmark
*
Contact: Shafqat, Ali, Norwegian University of Science and Technology, Mechanical and Industrial Engineering, Norway, [email protected]

Abstract

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In the design phase of product development (PD) process, most new products face significant uncertainties and risks. Uncertainty is typically associated with a lack of information, while learning is a process that acquires information. Therefore, learning fast and at low cost decreases the uncertainty and increases the efficiency of the product design phase. This paper investigates the concept of the cost of learning in PD's design phase. Reviewing the literature, we conceptualize the cost of learning and review the learning methods considering three aspects in the design phase of the PD process: (1) costs associated with learning from mistakes and failures, (2) learning methods and (3) categories of learners. This paper thus provides the conceptual foundations for future work to increase the efficiency of the PD process by reducing the cost of learning from mistakes and failures.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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