Abstract
The rate of adoption of digital solutions in manufacturing environments remains low despite the benefits these can bring. This is particularly acute among industrial small and medium enterprises (SMEs), who typically do not have the confidence to adopt new technologies and for which cost and a lack of skills remain key barriers. Most digital solutions require some type of visualisation component, being a vital way to the interpret and use effectively the data. Data visualisation on its own provides an opportunity to bridge the gap of digitalisation in SMEs by providing them invaluable process insights in an efficient manner without requiring high levels of training or expertise. However, as with other digital technologies, software components such as data analytics and visualisation are commonly developed, deployed, and maintained by a third party, and SMEs lack the expertise to understand how to implement or change visualisations and how they can be applied in the manufacturing domain. The Digital Manufacturing on a Shoestring approach proposes using off-the-shelf components, both hardware and software, to develop low-cost digital solutions with minimal expert knowledge. The underlying Shoestring architecture enables the incremental connectivity of different solution components using a service-oriented approach. This paper introduces the implementation of visualisation-as-a-service, where the visual components of a digital solution is dynamically created by a set of reusable, configurable and modular elements. We also introduce the use of templates for the no-code creation of visual solutions, taking advantage of the re-usability of visual components across different digital solutions.
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Martínez-Arellano, G., McNally, M.J., Chaplin, J.C., Ling, Z., McFarlane, D., Ratchev, S. (2022). Visualisation on a Shoestring: A Low-Cost Approach for Building Visualisation Components of Industrial Digital Solutions. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_20
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