Richard Hill - Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things

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Industrialisation is an essential part of global economic development. Central to this development is technology; creating of new ways of doing things to enhance quality, repeatability and to discover new frontiers of value creation.

The automotive and aerospace industries have been prime movers in the advancement of the application of technology to improve lives and generate wealth. Both the outputs of these industries, and the environments in which they are produced, require a continual application and adaptation of technology to make our activities safer, more affordable and ultimately more sustainable, as the global community becomes aware of our collective need to reduce the consumption of increasingly scarce natural resources.

Thus, innovation is one of our primary tools to address current and future challenges. Innovation gives the ability to respond rapidly to emergent situations and to make reasoned sense of historical experience, so that we can learn from the past to inform the future.

Data has always been central to industry; without measurements, quantities, reporting and accounting, we would not have been able to make the advancements that have been witnessed through industrialisation.

However, it is more recent developments in computing technologies that are creating new ways to use data to create even more value and more advanced products.

Through widespread application of wireless sensor networks, embedded systems, cloud computing and ubiquitous high speed network infrastructure, we can identify hidden patterns in operational data, store and process vast quantities of data and constantly refine computational algorithms to search, categorise and predict new behaviours in a complex, inter-connected world.

This use of technology to collect, organise, process and consume data provides industry with the ability to monitor performance, automate decision making through condition monitoring and predictive maintenance, create seamless supply chain linkages through the close integration of industrial processes and logistics, leads us to discover and release value streams that were not seen prior to the adoption of analytics technologies. These technologies are key as we enter into a more model-based engineering (MBSE) approach to industrial innovation.

The 2017 UK Government Industrial Strategy has been a catalyst of technology awareness. While the Tier 1 leaders of industry have understood that technology is central to their competitive advantage, other suppliers further down the supply chain have not always been able to keep pace with the early adopters.

Financial constraints such as limited investment have restricted the extent by which small and medium sized enterprises (SME) can explore the benefits of technological innovation until the technology itself becomes more affordable.

We are now at the beginning of an exciting era where technology is relatively inexpensive and the key differentiator between a business that is agile and fit for the future, and one that may struggle to remain sustainable, is the knowledge of how to use dataspecifically the techniques of analyticsto maintain their competitive edge.