How IT teams enable Predictive Maintenance data enrichment from maintenance records

31 January 2020

Manufacturing today is a world away from where it was ten years ago. With the advent of Industry 4.0, organizations have been hit with an influx of data – so much so that it has its own term: big data. Not very imaginative, but it gives a clear picture of the enormity of the data revolution.

It is hardly surprising then that IT is strongly represented at board level, enabling innovation and creating digitization strategies to support and enhance businesses.

Predictive maintenance data

Predictive Maintenance looks to keep machinery running through identifying issues before they become a manufacturing problem. Proactively maintaining machinery has a number of benefits, including cost savings through reduced equipment failure and resulting damage, better quality, and extended machinery life. But to get there an organization needs to shift its culture to focus on continuity and constant improvement – not by relying on big data alone.

To collect, manipulate and act upon masses of data requires technology. There’s too much data, too many variables and too many algorithms for people to feasibly (or profitably) process it all. And so we come back to the role of IT teams.

Laying the foundations

Predictive Maintenance solutions bring together data from sensors, machinery manufacturers and environment reports. That’s a lot of data. Bringing maintenance data into the system provides a foundation layer of knowledge, for example a scheduled maintenance task could be the reason for a period of unusual sensor readings that breached a threshold. It brings context to the data and allows Predictive Maintenance systems to get a better idea of how machinery functions, reacts and recovers during various events. And the best part is that the maintenance data already exists, this is just a value-add.

Bringing it together

Back to IT: sourcing the data is down to maintenance teams but getting it into a Predictive Maintenance system in an efficient way is where technology expertise is required. Raw data formats, company-specific security requirements, data regulations) and the Predictive Maintenance solution itself all bring challenges – and ensuring smooth, timely and secure data transfers is a job for an expert.

The whole is greater than the sum of its parts

Enriching Predictive Maintenance data with maintenance data brings maintenance teams a new layer of insight from making use of existing data. Having the guidance and support from an innovative IT team means that this and any further data sources can be exploited for secondary value with minimal ongoing input. This is just one example of the result of an organization growing and learning together through a Predictive Maintenance mindset.

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