In many industries efficiency is severely constrained by quality assurance issues arising from complex
manufacturing process, where faults commonly do not arise from a single point of failure, but are a
consequence of a number of contributing factors.
Assessing the effect of these factors and applying remedial actions may require expertise from a range of
different disciplines and experiences, which for operational and logistical reasons may not be available at every
stage in the manufacturing process.
The cost of non-conformance to quality standards is more than just the simple issue of a devalued product
being produced. Lack of predictability and unexpected variation in quality can severely influence the delivery
reliability. For exampe, during the planning process an estimate is included of the proportion of the goods
manufactured which will conform to required standards (Product Through Yield). If the estimate is too low
then there is wastage in the form of rework which adds cost and affects speed of working, reapplication to
lower margin outlets or scrappage, if the estimate is too high then this means that replacement-manufacture
has to take place right back at the beginning of the process, which could entail significant delays, or the whole
reschedule of the plant.
This project directly addresses the challenge for organisations to realise actionable knowledge from an
ever increasing flood of potentially valuable data. Currently, the need for skilled ‘data scientists’ is a major
bottleneck in this regard. The project will apply existing and develop new technologies to create an ICT tool set
which alleviates this bottleneck through provision of a collaborative platform with tools for data integration
and analytics deployment which are accessible to non-ICT specialists. The project includes innovative elements
especially concerning the integration of text-based knowledge with numerical data and inclusion of ‘plug-in’
algorithms for model-based novelty detection in data streams. In this way the envisaged toolset provides a
vehicle for projects to move from problem definition, to knowledge discovery and on to real-time deployment,
and with an embedded continuous improvement cycle. Each instance of the toolset will also become a
knowledge system in its own right which can be interrogated by users, either directly in relation to the process
route and quality issue served, or as part of the wider organisational knowledge base. A means is therefore
provided for non-specialists to organise and progress data analytics projects independently, but where skilled
data scientists are available, for them to use the framework to structure and steer project work in multiple
areas simultaneously, thus increasing effectiveness.
The project is funded by Innovate UK, start date 1.10.2014 and has a length of 18 months.