Improving Maintenance Planning And Scheduling With Data Automation
Routine maintenance of production equipment is a must for every manufacturer who wants to improve equipment reliability, keep costs under control, reduce equipment downtime, and make sure the quality of their products is not compromised.
Thanks to advancements in technology and cloud-based platforms that utilize the benefits of data automation, maintenance planning and scheduling can not only be automated, but the accuracy can also be considerably improved.
In this article, we’ll look more closely into the very simple and practical benefits that data automation brings in the context of preventative maintenance.
The impact of maintenance on the bottom line
Maintenance matters. There are no two ways about this. Allocating required resources like parts and labor and ensuring they are available when needed is the essence of keeping equipment running and production flowing.
However, we should also understand how asset management impacts a company’s bottom line to grasp the complete picture of its importance and why we need to have an improvement mindset.
Some of the cost-driven benefits of improving maintenance are:
Investments can be postponed because the existing equipment is better utilized.
Costs due to breakdowns decrease because machines run as planned.
Production costs decrease as each operator produces more per hour.
Cost per product goes down as production quality improves.
Regardless of the current state of your department’s maintenance maturity, your focus on improving how you do maintenance is a necessity. Optimizing maintenance planning and scheduling means doing maintenance when it is needed and avoiding doing it when it’s not. In other words, improving the accuracy of your preventive maintenance program.
Preventive maintenance as the most popular strategy
There are different maintenance strategies that you can apply in your factory to improve maintenance, and choosing the right one depends on where you are in your journey.
Preventive maintenance is performed at regular intervals. We can differentiate between two types of preventive maintenance based on how we define those intervals:
Calendar-based maintenance: A form of planned maintenance scheduled ahead of time to replace parts before they break down. For example, utilizing a set interval such as 10, 30, or 90 days.
Usage-based maintenance. If calendar-based maintenance uses a set time interval to replace parts, usage-based maintenance utilizes a usage interval. For example, replacing a part after 10 000 machine cycles.
If we compare the two, then the latter aims to be more precise. But there is a catch.
It’s hard for companies who don’t have access to their machine data to track the usage of their assets accurately. Thus, it’s also hard to improve maintenance because an accurate utilization rate is missing.
As we will see in the next chapter, that can be solved with data automation.
What does data automation bring to the table?
In the true sense, data automation is the use of intelligent processes and systems to collect, process, or store large portions of data. In addition, data automation helps maintain consistency in results, which is a significant issue faced by many businesses where data execution is carried out manually. There’s a big difference between manual and automated data collection.
6 dimensions of data quality
In a factory, that can mean using different sensors to collect production information and then processing this data using a system or a suite of integrated systems.
An example of what information you can collect automatically includes:
production speed and quantities (cycles)
machine uptime and downtime
Since we are talking about maintenance, we must look at how this information can help us in our quest to improve the accuracy of maintenance planning and scheduling.
For example, to accurately know how many cycles our machines have gone through, we need to know which products were produced at any given time. Once we know that, we can assess the actual usage of our machines.
Another example is machine uptime and downtime. Again, with data automation, we’ll know exactly when our machines were working and when they were stopped or idling.
Using data automation to improve maintenance planning and scheduling
Following are three very simple and practical ways to utilize data automation and make your regular maintenance more accurate.
#1) Calendar and usage-based checks made smarter
Once you have the data regarding when and how much your machines are working, maintenance planning and scheduling become much simpler.
For example, using sensors and a system that makes sense of the information captured, we’ll know if our scheduled shift had any actual production. If it did not, then we don’t need to count that time towards our interval. So instead of 30 calendar days, you can schedule a check after 30 days of actual work. And this is validated with the data from your machines.
Similarly, with data automation, you will know exactly how many cycles your production machines have gone through.
A well-implemented system will be able to tell you what product is currently being produced, what was produced before, and in what quantities. In other words, it will keep track of the actual usage of your production assets. It will also be able to tell you if you have been running slower than you planned.
This means you can leverage data automation to improve the accuracy and relevancy of your usage-based maintenance activities.
#2) Event-related maintenance
Having an in-depth understanding of your production process and the different events happening day-in-day-out can make maintenance scheduling much more precise. Following are three examples that you can utilize:
Maintenance based on downtime reasons. The first thing data automation provides is a correct overview of actual downtime. Once you have that information, you can start collecting the reasons causing your downtime. Once you identify the root failure cause, you can plan future maintenance tasks to address or prevent those specific reasons.
Maintenance reminder during a setup. If you have a system that tracks changeovers to new products, you can remind your crew to complete routine checks of activities before setup is complete. This will help ensure that your machines are running in peak condition. This sort of improvement would be challenging to implement without data automation.
Quality events. Say your team discovers that they just had to scrap 100 products due to a problem with the labeling machine. This information is captured automatically using sensors, and once it’s logged, your maintenance crew is automatically notified of the issue. Yet another quick win in making maintenance more effective.
This is not an exhaustive list, but it outlines the idea that once you have accurate information about your production process thanks to data automation, you can significantly improve the accuracy of your planned maintenance work and minimize the chances of doing excessive maintenance.
How to implement data automation?
The final question that remains is how to automate your data collection for maintenance purposes. Different systems can do this, the most obvious one being a computerized maintenance management system. However, there is another option – combining a CMMS system with an OEE system.
A powerful preventive maintenance system allows you to manage maintenance schedules, improve control of your spare part inventory, automate the administrative part of your maintenance, and more.
An OEE system provides you with information about what is actually going on in your production process as it looks at the utilization of machines through the following three components: availability, performance, and quality.
Why do these components matter? Because three out of the four bottom-line benefits that we outlined at the beginning of this article are also related to OEE.
Costs due to breakdowns decrease. The fewer breakdowns you have, the higher your availability.
Production costs decrease as each operator produces more per hour. If your production speed is optimal, then performance improves.
Production quality improves because machines run as they should. The more quality products you produce, the higher your quality reading.
This means that if you optimize OEE, you also improve maintenance and vice versa. It’s no wonder then that Total Productive Maintenance (TPM) has, since its inception, included OEE monitoring and measurement as the primary cornerstone of its philosophy.
Integrating a modern CMMS with an OEE system provides manufacturers maximizes the utilization of data automation.
As manufacturing companies consider the technological advancements made available to them, data automation is undoubtedly one aspect to consider investing in.
It will help improve the planning & scheduling of maintenance activities and it also paves the way for advanced maintenance strategies like CBM and predictive maintenance that heavily rely on automated data collection.
Author: Martin Lääts is the co-founder and Head of Product and Design at Evocon. Evocon is a visual and user-friendly OEE software that automates the data collection from machines and provides real-time information about production performance.
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