The manufacturing sector is already able to predict when machinery is going to break down – but the biggest gains are yet to come.
By keeping equipment maintained rather than reactively fixing it, manufacturers can avoid damaging their output levels and storing up problems further down the production chain.
However without truly predictive maintenance there is a potential problem that they will over-maintain, leading to unnecessary downtime for the machinery and costs.
“Predictive maintenance strikes a balance between reactive and excessive maintenance, identifying and resolving potential issues before equipment breaks down without incurring excessive costs from emergency or even over-maintenance,” Columbus UK product strategy director Kevin Bull said.
IIoT deployments monitor temperatures, vibrations or humidity from sensors embedded within equipment on the plant floor, generating large volumes of data streams in real-time.
Once this data is gathered in a cloud-based system, it can be analysed to identify equipment status, monitor efficiency and detect if components are failing.
“AI and machine learning represent another step towards truly predictive maintenance,” Bull said.
“When unleashed on the vast volumes of data captured from the plant floor, data analytics can be enhanced to filter out anomalous information, detect hidden or underlying patterns and more accurately project equipment reliability – and adjust maintenance schedules accordingly.”
Advanced data analytics will also be key to identifying any teething problems when deploying new, fully digitised equipment and systems.
Analysing the effectiveness of emerging technologies and how they affect existing business processes can help futureproof businesses against further digital disruption and manage the impact of newly-deployed technologies.
“When harnessed correctly, these disruptive technologies will deliver wider business benefits for manufacturers, from shop floor to the customer experience to service delivery models,” added Bull.