Preparing product teams for industry 4.0: development of a process & supporting toolkit for early-stage decision-making  in mass customisation

Advances in manufacturing and supply chain technologies have led to a resurgence in the popularity of mass customisation (MC), the concept of producing custom products at a scale and efficiency near that of mass production. However, product teams attempting to leverage MC are experiencing high failure rates as they struggle to understand the level of customisation – if any – that their product should offer. This could be solved by finding the point of intersection between the customer’s perceived monetary value of being able to customise a given product, and the cost of implementing it. While the need for such a mass customisation decision process (MCDP) was identified over two decades ago, it still appears to be unaddressed today. This research proposes such a process, leveraging tools like the Kano Model, Quality Function Deployment and the ‘serious game,’ Buy a Feature. It was developed through an action research approach with 77 new product development student projects, before being tested in a business setting by two start-up companies. In testing and developing this decision process, this research also provides insights on the types of products that appear to be most and least suited to mass customisation. The findings highlight how easy it is to assume that customisation might be a valuable addition to a product, when in most cases it is not something for which customers are willing to pay a sufficient amount. In cases where it is worth offering, the process helps to identify a suitable level of customisation early enough to allow for appropriate design and manufacturing methods to be exploited. Finally, previously undocumented user experience (UX) issues relating to the tools used in the MCDP were uncovered during this testing and a toolkit was iteratively developed to overcome them. This toolkit makes the MCDP easy to learn and apply for both students and companies, and these two research outputs together form the starting point of a proposed end-to-end framework and supporting software for the successful implementation of MC.