Feasibility investigation of crowdsourcing-based product design and development for manufacturing

Abstract

In the era of Industry 4.0, to help manufacturers make quick response to rapidly changing market and customer needs, this research explores the feasibility of realizing benefits of crowdsourcing in product design and development from a lifecycle point of view through investigations on product design quality control and crowdsourcing technology theories, product design lifecycle information modelling, and simulation platform prototyping. It intends to help manufacturers create a product-service ecosystem to deliver values to all involved stakeholders of a PDD process. This study started with building up the theoretical foundation of product design quality control in crowdsourcing design environment. Then, key crowdsourcing technologies for realizing a lifecycle PDD process on a crowdsourcing platform while enabling the design quality were explored. Thirdly, a multi-layer product design lifecycle information model was developed to accommodate all design related information in a PDD process and the identified information at each design phase and the relationships and interactions among information entities were evaluated by case studies and ORM modelling method, respectively. Finally, two crowdsourcing platform prototypes based on the PDLIM were developed to test their effectiveness in communicating design information among stakeholders and delivering value to them. The proposed research made contributions to knowledge through the following improvements/advancements: (1) understanding of key factors affecting product design quality in crowdsourcing design environments, (2) a technical foundation of crowdsourcing technologies for PDD process, (3) a novel product design lifecycle information model accommodating design information in crowdsourcing environments, and (4) guidelines on developing intermediary and integrated crowdsourcing platforms for PDD.

Date of Award	4 Feb 2021
Original language	English
Awarding Institution	Northumbria University
Supervisor	Sheng-feng Qin (Supervisor) & John Vines (Supervisor)