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InnoCrowd, a Product Classification System for Design Decision in a Crowdsourced Product Innovation

  • System engineering focuses on how to design and manage complex systems. Meanwhile, in the era of Industry 4.0 and Internet of Things (IoT), systems are getting more complex. Contributors to higher complexity include the usage of modern components (e.g. mechatronics), new manufacturing technologies (e.g. 3D Print) and new engineering product development processes, e.g. open innovation. Open innovation is enabled by IoT, where people and devices are easily connected, and it supports development of more innovative products through ideas gained from predecessors and collaborators world wide. Some researchers suggest this approach is up to three times faster and five times cheaper than conventional approaches [Gassmann, 2012], [Howe, 2008], [Kusumah, 2018]. Because open innovation is relatively new, many managers do not know how to employ it effectively in some phases of product development [Schenk, 2009], [Afuah, 2017], including requirements definition, design and engineering processes (task assignment) through quality assurance. Also, they have trouble estimating and controlling development time and cost [Nevo, 2020], [Thanh, 2015]. As a consequence, the acceptance of this new approach in the industry is limited. Research activities addressing this new approach mainly address high-level and qualitive issues. Few effective methods are available to estimate project risk and to decide whether to initiate a project. We propose InnoCrowd, a decision support system that uses an improved method to support these tasks and make decisions about crowdsourced engineering product development. InnoCrowd uses natural language processing and machine learning to build a knowledgebase of crowdsourced product developments. InnoCrowd presents a manager with results of similar projects to show which practices led to good results. A manager of a new project can use this guidance to employ best practices for product requirements definition, project schedule, and other aspects, thereby reducing risk and increasing chances for success.

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Metadaten
Author:Indra Kusumah
URN:urn:nbn:de:bsz:kon4-opus4-29406
Referee:Camille Salinesi, Hervé Panetto, Jivka Ovtcharova
Advisor:Camille Salinesi, Clotilde Rohleder, Josef Wieland
Document Type:Doctoral Thesis
Language:English
Year of Publication:2021
Granting Institution:University of Paris 1 Panthéon-Sorbonne
Date of final exam:2021/09/10
Release Date:2021/12/20
Tag:Innovation; Product Development; NLP; Machine Learning; Crowdsourcing; Requirements Engineering
Page Number:151
Open Access?:Ja
Relevance:Abgeschlossene Dissertation
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International