According to a report by market research company Vanson Bourne, unplanned machine downtime costs manufacturers $260,000 for every hour of lost production, and 82% of manufacturers experience machine downtime at least once each year. It’s an extremely costly and common problem, but there is a viable solution that begins with advancing beyond outdated maintenance methods.
Traditional approaches to machine maintenance don’t do much to limit or remove unplanned downtime. With a reactive approach, for instance, technicians only step in once the minutes of downtime start adding up.
A preventive approach might do a better job of catching issues early, but only if the maintenance happens to be scheduled when a machine begins showing signs of breaking down. Because most factories rely on one or both of these methods for machine maintenance, the vast majority still consistently suffer from unplanned downtime.
Predictive maintenance, on the other hand, offers technicians enough advanced warning to address issues before they cause unplanned downtime. For the first time, factories are able to take a fully proactive approach to machine maintenance thanks to the advent of internet-connected sensors.
These sensors can attach to equipment and monitor key machine health data in real time, then feed that data into a predictive maintenance platform that applies data analytics to identify red flags as soon as they appear. The platform can then send technicians automatic, real-time alerts.
This new generation of technology promises to turn machine health monitoring — historically an overlooked area for maintenance teams — into an asset that manufacturers can leverage to reduce unplanned downtime. Here’s how:
The Vanson Bourne report also shows that the average unplanned downtime event lasts about four hours, and lost productivity during this time can cost manufacturers more than $1 million.
Downtime has huge costs because an unplanned outage brings production to a halt for an unknown reason, which technicians must then scramble to diagnose and fix as quickly as possible. The work is reactive, so there’s no way to know how long diagnostics and repairs will take.
Planned machine downtime is much more palatable. Manufacturers can prepare for these events in advance and schedule exactly what they plan to do. However, production still suffers because equipment might shut down for maintenance it doesn’t need.
Predictive maintenance tools use machine health monitoring to differentiate when a machine does and doesn’t need maintenance. That way, factories can plan for downtime events and incorporate only the equipment that currently needs attention. Technicians can respond early and with keen insight to minimize any negative impacts on production. Once these proactive interventions become the norm, unplanned downtime becomes a rarity.
Say you have 200 machines. Two of them are on the brink of failure, 25 are in very poor condition, 50 show early signs of wear, and the rest are healthy. A crew of five technicians has six planned maintenance windows in the next six months, five of which will be one hour long, and the sixth will be an eight-hour chance to get some serious work done. How does the crew maximize each opportunity?
Predictive maintenance tools answer that question. Machine health data reveals which equipment requires immediate attention and which can be put off until later. Furthermore, machine health data helps identify where and how a machine requires repairs so that technicians can make the biggest impact in the shortest time window. Each opportunity counts.
With a clear indication of where, when, why, and how technicians need to respond, maintenance teams can use limited resources to make even a large industrial environment (or multiple sites) immune to the issues that cause unplanned downtime.
When factories can optimize each maintenance opportunity, they can begin to prepare for maintenance in the long term. To put it differently, instead of waiting for the next disaster, maintenance can lay the groundwork for even greater consistency and stability in terms of machine performance. They can prepare individualized plans for each machine, start ordering spare parts, and organize staff based on their skill sets.
With enough fine-tuning, everyone knows exactly what to do so that planned downtime goes systematically. Everyone can use the same preparation and experience to minimize unplanned downtime should it ever occur.
Unplanned downtime used to feel inevitable — a costly disaster waiting to happen. But that was before the era of predictive maintenance driven by machine health monitoring. Downtime will never be the same again.
Read more about eliminating unplanned downtime.
Augury is building a world where people can always rely on the machines that matter. Augury supports its partners by enabling Digital Transformation through superior insights into the health and performance of the machines they use to make products, deliver services and improve lives.