According to Aberdeen Research, 82 percent of manufacturers have experienced unplanned downtime over the past three years. This unplanned downtime can cost an enterprise as much as $260,000 an hour! No wonder, Equipment downtime & production downtime is one of the biggest pet peeves of any manufacturer.
Referring to the amount of time that a machine is not operating, whether a result of unplanned machine failure (like a fault or broken part) or planned maintenance effort (preventive maintenance), downtime gives a fair idea of the plant’s maintenance and production efforts. Acting on machine downtime tracking insights can thus lead to higher machine uptime, efficiency, and reliability and a better priority set for your maintenance teams. An Optimum maintenance benchmark by world standards is to have an unscheduled downtime to be 10% or less.
So it’s pretty clear, why analyzing downtime would figure in every plant manager’s to-do list. Questions that would need to be analyzed in a downtime report are:
- What is the total downtime experienced by the plant?
- How much of it was planned vs unplanned?
- What is the total cost incurred?
- What areas/machines are responsible for most downtime?
- What are the largest areas of improvement?
- Who was running the machine? Who brought it down? Maintenance/ Production Support?
- Did it cause other machines to stop? What was the loss?
- Were there any warning signs for the downtime?
Analyzing all of these questions can make a difference in understanding not just if current maintenance practices are adequate, but also understand which assets and processes are working below their capacity.
How can you track machine downtime?
Previously, machine downtime was monitored by tracking machine data and downtime events manually recorded by manufacturers with pen and paper, whiteboard, journals, or an Excel sheet. Later, it would be imported together into an MES or ERP system.
But this comes up with many issues of its own.
Not only is the data prone to human error(missing out some critical data to numerical errors), it is also delayed by the time it reaches the plant manager/maintenance team, causing reactive decision-making. The process is time and effort-intensive, requiring a lot of data compiling on a daily basis, using up a lot of time for plant operators that can be utilized elsewhere more productively.
Tracking with automated data collection
With systems like CMMS coming into the picture, the data collection on the shop floor became more ordered and centralized, by consolidating all maintenance team activities in one place. But while the data recording part was taken care of, the analysis was still completely the forte of the plant engineers, maintenance teams, and subject matter experts. And considering the magnitude of the machine data produced every day, the chances of a small blip being missed out are significantly high, which can result in a downtime later.
The gap here is to find a solution that can analyze terabytes of manufacturing data generated continuously on the shop floor to glean out insights that can detect anomalies that can lead to a weakness or a system failure way before it happens and prescribe reasonable solutions proactively.
The AI difference
The difference that AI can bring to this is huge- making maintenance evolve from being reactive to predictive, eliminating downtimes to a minimum. With AI at the heart of thousands of connected sensors and machines, millions of variables and data points are analyzed simultaneously and continuously in real-time, so any faults in machinery can be diagnosed even before they actually happen.
An AI application like UptimeAI that’s custom-built to study and understand equipment monitoring & maintenance subtleties can not just detect an anomaly, but also get to its root cause- independent of environmental conditions, auxiliary machine systems, etc. – all factors that might cloud the judgment of your immediate team or your systems and give prescriptive solutions, based on its experiences- just like a real subject matter expert would.
For Example, A leading global power generating company ran into a generator stator failure, despite using preventive maintenance systems, DCS alarms, and condition monitoring for Plant Maintenance. The generator failure resulted in nearly 45 days of unplanned downtime, amounting to a loss of USD 750,000. With UptimeAI’s predictive maintenance solution, they could detect the issue 10 months ahead and even received recommendations to help engineers understand and take immediate actions to mitigate the problem, ensuring the issue would not be repeated for any of their facilities. You can check out the complete case study here.
Conclusion
Machine downtime Analysis with AI can move from post-mortem analysis to proactively predict & mitigating failure before it occurs.
While initially, AI can help with machine downtime analysis by identifying how to proceed with repairs in time. The next step gives businesses insight around boosting efficiency and eventually moving to zero downtime altogether, by judging the true age & potential of equipment and planning operations to minimize abuse.
Downtime is one of the largest enemies of productivity & production. With the right partner, manufacturers can pursue continuous improvement initiatives fearlessly knowing they have their own in-house subject matter expert 24*7 churning out insights that lead to better decision-making.
Want to make your Machine Downtime Analysis real-time, seamless & proactive with the power of AI? Book a demo with us at info@uptimeai.com.