As manufacturers continue to automate their factories and connect them with intelligent sensors, the data collected is arming them with critical information on the health and remaining life of their machinery, enabling scalable predictive maintenance.
Condition monitoring, Industry 4.0, analytics, predictive maintenance, diagnostics, prognostics…
With an increasing use of technology to drive businesses forward in an agile, efficient, compliant and - crucially - competitive manner, does the skilled manufacturing sector worker need to go back to school to study computer science? What do these terms mean? Does it even matter? Despite claims that “every business is a technology company”, every business has its own core skillset and can utilise appropriate specialist outsourcing solutions for additional expertise, utilising software for advanced data analytics to better understand machinery health.
For a long time, the Internet of Things (IoT) was associated with bringing sci fi into our homes; the fridge that reorders its own food, selecting music or tv choices using speech rather than a remote control, controlling lighting and heating from anywhere in the world using a mobile phone. With devices like the Amazon Echo being responsible for huge sales, we’re actually quite close to that vision of the future (still, nobody cares for smart fridges!). But more significantly, IoT has a bigger job at hand; it is reinventing manufacturing businesses.
An industry disruptor
Industry 4.0 is changing the way manufacturers – and many other organizations – think about their businesses. It challenges traditional staff roles, machine management, investment, supplier and customer relationships, streamlining processes and creating an agile structure to support this new, more efficient and innovative way of working. It’s living up to its name as the fourth industrial revolution.
As technologists at heart, we are always looking ahead, developing solutions and updates to improve customer experience and create opportunities for businesses to transform their operations – and indeed their internal culture to one that can take full advantage of predictive maintenance. 2017 saw steady progress in industry awareness of condition monitoring (CM) and prognostics and whilst we are seeing some external challenges encroaching in the form of the new GDPR regulations which will no doubt throw up some added complications early in the year. Overall, we look to 2018 with great enthusiasm for Industry 4.0.
Southampton, UK, 11 December 2017 – Senseye Limited, the leader in predictive maintenance software, today announced it has raised £3.5 million at the close of a Series A funding round led by MMC Ventures, a venture capital fund investing in early stage, high growth companies. The round was also supported by existing investors Breed Reply, IQ Capital and Momenta Partners.
Senseye, the scalable predictive maintenance leader, today announced that it has joined the PTC ThingWorx Marketplace™, partnering with PTC to offer predictive maintenance to users of PTC’s leading ThingWorx® industrial innovation platform without the pain of expensive consultants or extensive customization.
Industry 4.0 offers huge opportunities for production efficiency and visibility through predictive maintenance and other connected technologies, but it also brings a new operational risk – security. Cyber-attacks are increasing and the manufacturing sector is being hit with data theft, ransoms for access to locked data, machinery downtime, site safety, and build quality threats.
It’s a fact that no-matter how advanced the machine, there will come a time when it unexpectedly fails and your factory experiences the financial and reputational losses caused by unplanned machine downtime. The impact of this can vary dramatically; from direct costs of over £1.5M per hour in automotive manufacturing to thousands per hour in the Fast-Moving Consumer Goods (FMCG) industry, not to mention damage to reputation. Even machines cared for under the best preventative maintenance regime will experience unplanned failure at some point in their lifetime.
How do you know when you’re ready to take advantage of software like Senseye to automate the analysis of condition monitoring data from your machinery?
Change is uncomfortable, implementing new technology, and the associated process change and cultural changes bring significant challenges as well as benefits, even if all goes smoothly. Whilst manually analysing Condition Monitoring is useful, moving to fully automate analysis allows factory and organisation-level scalability. It’s important to be sure that the correct systems and assets are in place to support this new way of operating.