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Improving the inventive design process by using agile methods and machine learning algorithms

Abstract : In recent decades, companies have continually sought approaches that help them reduce the innovation cycle time due to its importance to their success. Among these approaches, it is possible to mention the TRIZ-based systematic inventive design processes, such as Inventive Design Methodology (IDM). However, one of the criticisms often leveled is that this approach does not have the essential agility. Hence, it is required to combine IDM with other methodologies to increase its agility. In this thesis, a Lean-based based method has been developed to add the agile characteristics to IDM. Besides, Analytical Hierarchy Process (AHP) and Failure Mode and Effect Analysis (FMEA) methods have been integrated into the process to select the most important initial problem. Furthermore, the machine learning algorithms and neural network doc2vec have been applied to extract the essential data in the process. These research works aim to facilitate and accelerate inventive design in companies.
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Submitted on : Thursday, December 16, 2021 - 12:02:33 PM
Last modification on : Tuesday, January 4, 2022 - 6:50:58 AM


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  • HAL Id : tel-03483107, version 1


Masih Hanifi. Improving the inventive design process by using agile methods and machine learning algorithms. Artificial Intelligence [cs.AI]. Université de Strasbourg, 2021. English. ⟨NNT : 2021STRAD002⟩. ⟨tel-03483107⟩



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