IoT enabling the mass customisation of products in manufacturing


This Pitch-In-funded project aimed to harness the potential of IoT to transform processes around product customisation, to produce, for the first time, an assessment of the viability of this technology for manufacturers interested in the business model called mass customisation or MC.

Mass customisation is based on the idea that products can be produced and sold at scale to fit the customers’ specific needs and desires. The MC business model challenges traditional manufacturing logic when you mass produce a product, which sees product volume and variety as variables that need trading off i.e., the higher the volume, the smaller the number of product variants you should have to make this economically viable.

MC is currently advocated to allow manufacturers to gain competitive advantage and offer their customers additional value. The trend is linked to the increasing availability of reconfigurable manufacturing technologies such as 3D printers and digital infrastructure such as IoT. IoT infrastructure can potentially harness data from consumers’ product use and preferences, which could be automatically transformed into personalised designs for users.

Before investing in a new business model, manufacturers need to be sure of the technical and commercial viability of building an infrastructure to support it. Pitch-In support enabled researchers to carry out this vital research. The project sought the views of companies and consumers, through a combination of workshops and online surveys, to model the theoretical value of IoT-driven MC. Pitch-In brought together partners from the STIM consortium, which comprises 20 well-known industrial partners in diverse sectors, and researchers from the Institute for Manufacturing (IfM) at the University of Cambridge, in particular the appliance manufacturer, Beko.

The objective was to model whether IoT-enabled MC was a good proposition for manufacturers compared to any alternative i.e., whether to continue mass production of that product, to by adopting MC driven by direct input of customers preferences and characteristics, or to adopt an IoT-led infrastructure which would derive designs completely automatically.

The models have been derived with a combination of a theoretical approach looking at the characteristics of the value of MC, and the analysis of data captured from a survey regarding the value consumers attribute to currently mass-produced goods.

The context to the project

While MC is not a new concept, for example you can design your own sports shoe for a premium price, IoT-driven MC is new. Here the user’s role changes from being an active participant, with human-driven product customisation, to a passive beneficiary. You might think that the latter would be universally preferred, however evidence on customisation and marketing studies show that the act of participating in the customisation process adds value to products. This is more obvious if we think, for instance, about buying a wedding dress, and the effort the industry is making to demonstrate that this is a unique experience which engages the customer throughout and accounts for their specific preferences and requirements.

So, the question is not just technical i.e., what infrastructure would be needed to support the relevant IoT data capture, but also commercial i.e., would a user pay more for a product that they do not spend time personally co-designing? Are there different types of product categories for which one approach is better than the other?

Some researchers believe that it is possible to use sensor-based data to understand what users need and feed this into the design process to make changes on the assembly line easier. For example, for washing machines, you could take data on how the end user uses them, to inform the factory about usage. Other benefits include the capability to customise products for the specific user. For instance, IoT could be used to harvest data from users regarding their posture when sitting, to automatically create the best design for their chairs, or from their tennis racquet to provide a customised racquet with optimised string tension, weight balance and frame stiffness.

What were the problems or barriers?

  • A lack of understanding by manufacturers about the potential of IoT to offer a cost-effective way to customise their products and produce them at volume.

  • A lack of interest by end users and consumers that could help providing valuable insights into their behaviour with a product and inform design decisions.

  • An understanding gap about different situations which propose suitability of customer intelligence or application of IoT infrastructure to obtain customised goods.

  • The challenge of incorporating IoT-based systems into existing business processes. Manufacturers do not know how to use the insights that are gained from data into the design process for a product.

What did you do?

  • Mass-produced goods were categorised according to their suitability for different customisation strategies. This was done by gathering survey data from over 500 people about how they perceive different aspects of the goods and clustering them. In this way we obtained the ‘fingerprints’ of 200 products.

  • Products that consumers consider upgrading frequently (FU) and which are purchased mainly considering their performance (PR) were more suitable for an IoT-driven customisation.

  • When the value is instead primarily given by the style (ST) and consumers are happy or would be motivated to put extra effort in buying goods (EE), human-driven customisation is likely to be the dominating choice.

  • Once divided into groups, we modelled different scenarios of how customers would perceive value during the act of customisation, creating predictive patterns of customer’s perceived value considering the difference between perceived benefits and perceived sacrifices (see fig 1).

  • A series of workshops were carried out to discuss the model and test the concepts, to understand the perception of implementing the MC business model and IoT-driven manufacturing with a number of industry experts through the STIM consortium.

What were the results?

A theoretical tool was developed to help manufacturers determine what types of goods could be more suitable for IoT-driven customisation. This was done by explaining, when, for equally appreciated customised products, users would favour an IoT-driven MC process over a human-driven one. The product archetypes demonstrate the applicability of this model to existing products, so that manufacturers can see what type of infrastructure the model would suggest for their products.

Fig. 1 shows an example, whereby products such as Video Game Consoles or VR Headsets came out as highly suitable for being customised automatically, with the support of IoT infrastructure. Furthermore, the value model predicts that the customers will appreciate the items being customised, but would not appreciate spending time to customise them.

On the contrary, products such as shoes and watches would be more suitable for human-driven customisation. Our value model predicts that customers value would increase with the time spent on the customisation of these items.

Of course, not all products ended up in either category, straight away. For example, products whose most important characteristic in the eye of current customers is price, would score pretty low on their suitability for customisation, since the current expectation is that customers pay extra for receiving customised items. However, as the MC paradigm will progress in feasibility, the expectation is that customisation could become equally convenient and hence also the producers of such goods would need to choose which type of MC infrastructure would be suitable (IoT driven or Human Driven). Our model has looked also into this opportunity.

While the model is mostly theoretical, it helps manufacturers identify in which group their products are likely to fall and presents a navigation guide which can be useful in the consideration of these difficult technology-driven decisions. We are planning to conduct further work to both validate the theoretical model and provide empirical evidence of customer-perceived value for different product and customisation options.

Lessons learnt

  • The Pitch-in network and funding support was pivotal in setting the basis for an experimental tool with the potential to help managers decide which investments to make in IoT for MC.

  • The project proved that it could provide a detailed perspective about the implications of IoT for manufacturing through the eyes of consumers. IoT implementation cannot be uniquely technology-push, but it needs to be considered as part of a broader evaluation of the value it provides.

  • Quantifying value is challenging – it is not as simple as measuring sales versus cost or selling more products. Unpacking the perception of customer value for each product provides the means for anticipating how to best setup the future manufacturing infrastructure. Different types of value should be considered linked to both the experience of using a product and that of spending time in obtaining desirable variations of a product.

  • Connecting capabilities: Pitch-In and STIM were invaluable to this project. They provided the opportunity to conduct workshops for the project to obtain valuable insights and feedback for pilot tests.

The industrially-oriented research environment of the Pitch-In and IfM networks helped contextualise the relevance and perception of MC in manufacturing. The industry members in the STIM consortium gave industry insights and feedback on the business model which will help shape the experiments that will form future pilot implementations.

What next?

The value model developed in this work will be used as a basis for future experiments aimed to interpret the reaction of customers during the customisation of different products providing a measurement for the customer-perceived value and the empirical basis for taking a decision on suitable customisation strategy.

Quote: “Pitch-In provided the basis for developing a tool aimed at reducing the uncertainty in investing in promising, yet still emerging, IoT technology.

Exploiting such technologies can potentially transform a traditional manufacturing business, yet they demand significant investment. It’s a big decision to make when certain technologies are still maturing and there is a level of uncertainty about the outcomes and value. By delaying such decisions, companies risk being overcome by the fact that technology is developing too fast for them to catch up.

The approach used in this project via Pitch-in’s network and funding allowed us to balance the risk against the opportunities. This now offers the potential to demonstrate the value of other technologies to support the manufacturing systems of the future.”

Dr Letizia Mortara, University of Cambridge


Industrial partners and researchers who are interested in understanding how IoT might support mass customisation are welcome to participate in our ongoing activities and research in this area. For any engagement opportunities, please email Dr Letizia Mortara at