Manufacturing intelligence to support innovative services Vast amounts of information are available today on how products and services are used, from which companies can draw valuable insights to improve design. Karl Hribernik tells us about the FALCON project’s work in developing a framework which will help to both improve product-service systems and shorten the development cycle Many consumers today express their opinions about specific products and services via social media and other channels, while large volumes of information about how products are used are available from other sources as well. This data can offer valuable insights to the commercial sector, helping companies to improve the products and services they offer, a topic central to the work of the FALCON project. “We wanted to look at these sources of product-usage information. So this is information that is generated when a product is being used – from sensor systems for example. We also wanted to take into account social media and non-structured information that is generated by users on Facebook, internet forums and so on,” outlines Karl Hribernik, the project’s Technical Coordinator. “We investigated which of those product usage information channels could be used in product-service design, and for which type of product. How could we technically integrate that information into design and improvement processes?”
Product usage information This is not just about improving the design of a product, but also the associated services around it. The value of a product is no longer determined purely at the manufacturing stage, but also through the provision of additional services that supplement the initial offering; the project’s work holds clear importance in these terms. “We try to provide tools to visualise, analyse and apply product usage information from different sources. We aim to help companies leverage that information in the services that they offer, beyond the actual products,” explains Hribernik. This research holds relevance to several different areas of industry; Hribernik says four business scenarios have been explored in the project, two of which are business-to-consumer sectors. “We have white and brown goods, such as washing machines, televisions and refrigerators, that are directly marketed to consumers. The second scenario in the business-to-consumer sector is clothing textiles,” he outlines.
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FALCON Virtual Open Platform for product-service design and product lifecycle management
The remaining two scenarios are businessto-business cases, where researchers looked at healthcare products and high-tech products. Different channels of product usage information are more important to specific sectors than others, an issue that Hribernik and his colleagues in the project investigated. “For example, there’s a lot of information about clothes on websites like Youtube, information which is valuable to the fashion industry. Social media is less valuable in the healthcare sector, as there is no valuable information out there on healthcare products. There are very good reasons for that,
misrepresented for example, while people in certain areas of the world may prefer a looser fit to those in other locations, something which researchers in the project took into account. “We effectively pre-process all the information, then make it available to the product designers on the platform, so that they can verify it. So they can ask; ‘ok, where did this information come from?’ ‘Who actually expressed this opinion about this product?’ ‘How old are they?’ And so on,” outlines Hribernik. In the other use cases, product usage information may be more structured. “For example, with sensors
We try to provide tools to visualise, analyse and apply product usage information from different sources. We aim to help companies leverage that information in the services that they offer, beyond the actual products in particular patient privacy,” he points out. A semantic model of the information relevant to each of the use cases has been developed, which lies at the core of the project’s platform. “For example, in the clothing use case there are product characteristics – you’ll find information on popular colours, sizes, and types of garments. Synonyms for these terms are also included in the model,” explains Hribernik. A lot of information in this particular case is by nature unstructured and subjective, with individuals expressing their own views and opinions on clothing, while there may also be differences in interpretation. Colours may be
on a product you might get a temperature value, which is very structured information – you know exactly what it means, and you know exactly what component of a product it relates to,” says Hribernik. This kind of information is relatively easy to interpret, while by contrast an Amazon review of a particular product would be significantly more complex. A user could use many different terms to express their opinion, and it may be difficult to understand whether the review relates to any specific component or function, so Hribernik says it’s necessary to look a little deeper. “We take that text apart, interpret it
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