The aims of the Brokering Additive Manufacturing project are to develop an agent-based distributed manufacturing system to meet big demand. This will be achieved through modelling of a system/network based on the best available data. Some of that data is already available, such as data on manufacturing in the UK, import and exports and regional distribution of manufacturing activities by sector. However, much data does not already exist, creating challenges for the research team. In the absence of some data, alternative methodologies are being developed.
Why is there a paucity of data on additive manufacturing? The AM sector is growing in size, year on year, based on many industry observations. The Wohlers Report provides some information based on data collected from a selection of AM firms. The 2021 report indicates that AM products and services grew to $12.8 billion in 2020. We know that the sector is growing in size and industry news informs us of corporate restructuring, mergers and acquisitions, new market entrants, technological innovations and existing and new applications. What is less clear are precise (or sometimes even ball-park) figures that are needed to fully measure and support the AM sector. These include the hardware installation base – do we even know how many machines are in the UK? And how many of them are actively used? In which manufacturing sectors is AM in more demand? What are the business models underpinning the use of AM? This information is integral to developing an understanding of how and why a firm may choose to connect to a distributed network and what the business rationale may be. Let’s face it, the network needs to be sustainable otherwise interest in participating may wane after an initial flurry of excitement. There is also a need to understand the network and its parts to maximise the value they bring for profit-making purposes.
How are we overcoming these difficulties?
- Trust in collating data – For some understandable reasons, there is a culture of secrecy around much AM activity. The sector – as it is typical in this stage of development – is very good at publicising new and potential applications but is less able to share concrete data on production volumes, revenue generation by types of activity and other business model aspects. As academics, our objective and independent status may encourage more data sharing from within the sector. The more information the sector shares with us, the more sophisticated the model will be. We also appreciate the need for AM firms to have a customised model in helping them fulfil demands in the post-pandemic world.
- Characterisations of AM business models – Using existing information and the data we collect, we will be able to sufficiently characterise trends in the adoption, use and application of AM and to develop exemplar business models. The volume of data supporting the modelling means that cross-checked industry expert-informed estimates of data (for example installation base and usage levels) will be more than sufficient to model the network.
- Transparency across our project – For this project, our research paradigm is to maintain interdisciplinarity not only across disciplines, but also through ensuring interaction with industry that will enable us to ‘test’ our assumptions. Real world ‘reality checks’ will be provided by our project partners (list) and through our demonstrations of the model at trade shows and conferences.
From early 2022 we will be collecting data through a UK-wide questionnaire that will questions around the use of AM, changes in demands and scenarios, use of AM equipment and what is needed from a distributed network. This will take around 10 minutes to complete online. The team will also be contacting a range of different companies from across the AM value chain to take part in discursive interviews to drill down into these issues in more depth.
If you or your company, or indeed companies you work with, are interested in participating in the research, please get in touch.
Jennifer Johns and Kautsar Ramli