5 Ways to Use Maintenance Data for Operational Efficiency
The age of big data is here and, more than ever before, manufacturers are using what they know to influence how they’re running their operations. In smart factories everywhere, the industrial internet of things (IIoT) is providing insights that are truly revolutionizing the way we work.
Maintenance is one segment of manufacturing that’s getting a huge boost from the plethora of data now available. Big data generated through the IIoT is helping factory maintenance professionals in myriad ways, including:
- Providing real-time performance data about equipment
- Improving machinery integration and interconnectivity
- Alerting or providing warnings about current or impending breakdowns
- Gathering and aggregating operational and maintenance data
When coupled with new-age applications and software-as-a-service (SaaS) platforms designed to assist maintenance professionals, big data is having a profound effect on how we make informed decisions about machine maintenance.
Putting big data to work
In factories equipped to collect and aggregate important data about their machinery, the benefits of deeper insights are taking shape in big ways. Take a look at five examples of how smart manufacturers are using advanced analytics to improve their maintenance data and, in turn, their operational efficiency:
- Predictive maintenance — Being able to better predict when equipment needs maintenance is only possible by assessing trends and understanding performance intervals. Take an example like ABB, a robotics firm investing heavily in the IIoT for predictive maintenance. The company has manufacturing plants on five continents, and they’re all set up to provide preemptive maintenance alerts well before breakdowns occur.
- Anticipatory downtime — With the ability to predict maintenance and plan for it comes another possibility: the potential for anticipatory downtime. Knowing when machinery is offline helps manufacturers plan around it. It can help them better manage production flows, plan for bottlenecks, or alter workflows all in an effort to achieve near-zero downtime.
- Identify catalysts for maintenance — Maintenance personnel can’t always easily discern machine breakdown causes. Having robust data available at their fingertips for review is helping change that. Being able to review maintenance data alongside sensor logs can show maintenance professionals what catalysts caused breakdowns and how proactive maintenance could prevent similar situations from arising in the future.
- Inform purchasing decisions — Maintenance is a process, not just a single act. The more data manufacturers have about their processes, the more they can work to improve them. Maintenance data could inform everything from how to purchase and stock spare parts to what types of investments manufacturers make in the caliber of their replacement parts.
- Maintenance automation — Already a reality for many manufacturers, automated maintenance looks to be the way of the future, according to the IoT World Today article. Smart factories will be able to order parts automatically before machines need them replaced and catalog maintenance data in real time, gaining intelligence on recognizing trends and patterns. Someday, this might mean one robot repairing another automatically. For now, however, it represents a symbiosis between human maintenance professionals and automated insights.
No shortage of innovations resulting from big data exist in the manufacturing sector. Better, smarter maintenance is one driving concept at the center of it all.
Data drives excellence
Big data and the maintenance innovations that stem from it point toward expounding benefits for factories. For example, in a study by McKinsey, implementing IIoT technologies for predictive maintenance could end up saving the manufacturing industry $240 to $630 billion annually. This couples with advantages like reducing machine downtime by 50% and increasing associated revenues from more reliable production lines.
The more you know, the more you can do. And, when your data pertains to something as pivotal as machine maintenance, the ways in which you can improve your operations are numerous. It all comes down to data.