Internet of Things platforms for mass customisation: when are they appropriate?

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Introduction

Manufacturers today need to take difficult decisions about investing in developing and adopting emergent technologies such as the Internet of Things (IoT). These managerial challenges exist not only because currently IoT technology is yet to be fully developed to perform safely and economically, but also because their commercial potential is still poorly understood. One of the most interesting applications for IoT technologies in manufacturing is to use IoT to derive data for the development of designs to drive the mass customisation (MC) production of goods. As an example, IoT could be used to harvest data from users regarding their posture to automatically derive the best design for their chairs. If coupled with digital manufacturing infrastructure which could be easily reconfigured, such as 3D Printers or laser cutter, having personalised IoT-designs would mean that manufacturers would be able to produce individualised products at the cost of mass-produced ones.

However, assuming that the technical challenges still to be resolved to build such an infrastructure can be overcome, the commercial viability of such IoT-driven MC infrastructure has not yet been proven. Would customers appreciate MC enough to justify such a technological investment? Which products provide a concrete opportunity for MC? This question is further complicated by the fact that the adoption of an IoT-driven manufacturing infrastructure for MC would reduce the intervention required from customers during the customisation of products, which many studies have revealed to be the primary source of value for consumers. In fact, studies report that consumers enjoy taking part in customising goods and businesses such as Adidas have set up the infrastructure to involve users in customising shoes. As the commercial value for MC is still to be determined, we are still poised with questions which manufacturers need to answer to be able to invest in infrastructure. Would consumers appreciate (and hence would they be happy to purchase) mass-customised goods they do not need to be involved to design.

To help manufacturers understanding for which products an IoT-driven MC approach could represent an interesting commercial proposition, this project developed a theoretical value model as the basis for a tool that could help manufacturers determine what types of goods could be more suitable for Internet of Things (IoT)-driven personalisation.

The model comprises:

  • A generalizable range of product archetypes for measuring consumers’ perception of value for MC. These archetypes regroup current mass-produced products which are consumed (and appreciated) in a similar way. The analysis of these archetypes is helpful for the identification of which customised products are most appealing to users and in particular which seem to be more suitable for the IoT-driven MC approach.
  • A theoretical value model which describes how/when, for equally appreciated customised products, customers would favour an IoT-driven (and hence time/effort saving) MC offer over a human-driven MC one.

This project has benefited from the expert contribution of R&D managers from Arçelik A.Ş, who helped with discussing the market perception of value in consumer goods. Further, we relied on the Strategic Technology Innovation Management consortium (STIM), which comprises over 20 companies from a variety of sectors (https://www.ifm.eng.cam.ac.uk/research/ctm/stim/) who convene regularly, to discuss manufacturing and strategic technology management approaches. Via the STIM platform we ran workshops, presented our research progresses and discussed with technology management managers their views on IoT-driven MC and provided a sounding board for our approach.

Project aims

The main goal of this project is to develop a theoretical value model as the basis of a tool that helps manufacturers determine what types of goods could be more suitable for Internet of Things (IoT)-driven MC.

The success of this project would support the implementation of IoT for MC in consumer goods by providing a tool to guide in the decision of which type of product or service customisation proposition would more successfully rely on customisation parameters gathered from the IoT. This project specifically addressed several recognised barriers regarding the implementation and adoption of IoT technology, in particular:

  • The lack of understanding in how IoT will/can generate value in a given application domain;
  • The low familiarity with IoT which should enable confident decisions regarding IoT adoption or investment; further the lack of knowledge of various stakeholder skills and interests in IoT (e.g., the users/consumers));
  • The challenge of incorporating/streamlining IoT-based applications/decisions with existing business processes.

What was done?

In the first phase, a range of different product archetypes was identified to study suitability of different products for IoT-driven MC, with the help of a questionnaire based on the way products are consumed (one product from any particular archetype would represent all other products in that archetype). Responses to this questionnaire were analysed using a clustering method to categorise various products into different product archetypes sharing similar attributes. In the second phase, constructs evaluating customer-perceived value in products were identified through a literature search. These constructs formed the basis for a theoretical approach to appreciate the different elements of value in customisation. The theoretical model was developed to describe how the time invested by a user in designing customised products might be perceived (i.e. is it considered to be a benefit because users enjoy the experience or a sacrifice as the user needs to spend time and effort to design their goods), presenting different potential patterns. This analysis was based on a systematic review of the understanding of the value in business decisions. The patterns which indicate that the time invested in customisation is perceived as a sacrifice more than a benefit are more suitable for IoT-driven MC.

Ongoing work aims to plan experiments to match products (one per archetype) with the theoretical patterns, to determine which category of products would be most suitable for IoT-driven MC. Different industry experts were involved with these phases through the STIM consortium.

Results

1. A range of product archetypes, which consider the various rationales consumers adopt in the consumption of current products. Individual products in each archetype can be representative of a number of products consumed in the same way. This range of product archetypes is developed to be used to evaluate what aspects consumers favour in current goods and which align best with a MC proposition. Figure 2 shows, as an example, a product archetype A1. The products in this group, such as televisions or mobile phones are predominantly valued according to utilitarian attributes such quality, price and performance.

2. A theoretical value model for the suitability of different products for IoT-driven MC, demonstrating three different situations: 1) where human-driven MC is suitable, 2) where IoT-driven MC is suitable and 3) where we would need more data to take a decision on the MC manufacturing strategy.

Impact

  • The research is being submitted to academic journals and presented at conferences
  • One conference track is planned for the 2021 R&D Management conference where mass customisation enabling technologies and approaches will be discussed.
  • The testing of the model in an empirical setting is being designed.
  • A tool is being conceived which will help managers anticipate whether their products are suitable for mass customisation, and the ones which will favour an IoT-driven infrastructure to support it.

Next steps

The theoretical value model developed in this work will be used as a basis for experiments aimed to validate the theoretical model, and to measure customer-perceived value from different product and customisation options combination. The application of the developed range of product archetypes will be explored for new technological strategy implementation in a variety of settings.

Lessons learned

The Pitch-in network and funding support has been pivotal in setting the basis for an experimental tool that has the potential to help managers moving forward in deciding investments to use IoT for MC.

Both the Pitch in and STIM platforms have been very valuable for this project as they provided the opportunity of conducting workshops to obtain useful insights and feedback for pilot implementations of the experiments. The friendly and industrially- oriented research environment of Pitch-In and IfM helped contextualising the relevance and perception of MC in manufacturing.

The theoretical basis of how scholars assess “value” is very broad and crosses multiple domains. Vetting the useful conceptual constructs underpinning value required much resources, but it provided a detailed perspective about the ultimate implications of IoT for manufacturing in the eyes of consumers.

Access to deliverables and other tangible outputs

The outcomes of this project will be disseminated with different academic journal papers publication, along with a report for Pitch-In website. Based on the research output and insights from this project, a conference track for R&D Management Conference 2021 has been accepted and is open for review https://www.rnd2021.org/Conference-Tracks/id/481.

What has Pitch-In done for you?

Pitch-In provided the basis for scoping and developing the idea behind this project, i.e., the elements to develop a tool aimed to reduce the uncertainty in investing in promising, yet still emerging, technology such as IoT.

Although there is a generic emphasis on potential future value from IoT-driven MC, the diffusion and exploitation of IoT in the domain of MC is still patchy as the technology is emergent. The high investment costs necessary for the exploitation of such technologies require conscious decisions from firms. However, it is well known that the strategic technological decisions which would potentially lead to the radical transformation of companies’ traditional business are tough to take when the technologies are still maturing, due to the high uncertainties involved. This is to the point that, sometimes, by delaying and postponing such decisions, companies end up suffering as a result (e.g., they are overcome by technology-driven disruptions). The strategic technological decisions to be taken relate to investments in innovative capabilities. For these, the technological opportunities and the risks involved have to be balanced. The approach proposed by this project via the Pitch-in network allows to do so and could be potentially replicated for appreciating the value of other technologies supporting manufacturing systems of the future.

Project lead:

Dr Letizia Mortara, Institute for Manufacturing , University of Cambridge
Twitter: @LM367 / @IfMCambridge

Partners:

Arçelik A.Ş. designs produces and sells durable consumer goods. It offers products and services around the world with its 30,000 employees, 18 different production facilities in 7 countries for its 11 brands (including Beko and Grundig). The key connection will be the Beko UK R&D team.

Strategic Technology Innovation Management (STIM) Consortium: The STIM Consortium is a practice-oriented research and networking collaboration between industrial member companies and the Centre for Technology Management. It currently counts 20 large companies in a number of sectors. The STIM consortium is interested in understanding principles and tools useful for managing technologies in the digital manufacturing age.

Engagement opportunities:

During the project we will be seeking to work in collaboration with industrial partners who are interested in understanding how IoT might support mass customisation.

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