Crowdsourcing of Complex Problems in Industrial Firms : A Case Study Within the Packaging Industry

Abstract: This study takes root in the emergence of new crowdsourcing techniques that have made it possible to solve business problems of complex nature by reaching outside of traditional organizational boundaries. While crowdsourcing is not a new concept, emerging technological trends such as Industry 4.0 and a growing interest by organizations to leverage the collective intelligence of online communities have made it an intriguing subject to study. The case study was conducted in USA, California at an established Swedish firm within the packaging industry. As opposed to traditional forms of crowdsourcing of repetitive and simple tasks, this study has an emphasis on complex problems with open-ended goals that often require iteration and expert skills to solve. The case project called Megatron relates to a packaging product developed to transport data servers for major technology companies in Silicon Valley. The product significantly differs in material and design compared with earlier packaging versions that have served the same purpose. Not only does it fold within its footprint when empty, to save space and gain logistical benefits, but it has a lightweight and robust material. The project development of this prototype of seemingly new features constituted a complex problem for the industrial firm that could be studied. Using innovation, problem types, speed and agility of the project as metrics and benchmarks to evaluate the potential of crowdsourcing, interviews and project documentation was gathered as empirical data. The empirics were then analyzed with the help of literature, theory and models that offered scope of crowdsourcing models for different purposes, and a relationship between problem types and innovation. The study indicated a strong willingness among company employees to integrate digital platforms and tools for experimentation and prototyping. Furthermore, the study identified paradoxes and drawbacks such as the malleability of complex problems. However, recommendations to deal with the uncertainty are provided as well, such as online reputation systems and peerreviewing tools to validate the quality of work.