Abstract
The development of a new product can be accelerated by using an approach called crowdsourcing. The engineers compete and try their best to provide the related solution based on the given product requirement submitted in the online crowdsourcing platform. The one who has submitted the best solution get a financial reward. This approach is proven to be three time faster than the conventional one. However, the crowdsourcing process is usually not transparent to a new user. The risk for the execution of a new project for developing a new product is not easy to be calculated [1, 2]. We developed a method InnoCrowd to handle this problem and the new user could use during the planning of a new product development project. This system uses AI concepts to generate a knowledgebase representing histories of successful product development projects. The system uses the knowledge to determine qualitative and quantitative risks of a new project. This paper describes the new method, the InnoCrowd design, and results of a validation experiment based on data from a current crowdsourcing platform. Finally, we compare InnoCrowd to related methods and systems in terms of design and benefits.
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Kusumah, I., Rohleder, C., Salinesi, C. (2022). InnoCrowd, An AI Based Optimization of a Crowdsourced Product Development. In: Canciglieri Junior, O., Noël, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations. PLM 2021. IFIP Advances in Information and Communication Technology, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-030-94335-6_19
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DOI: https://doi.org/10.1007/978-3-030-94335-6_19
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