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In our most recent blog, we explored the four core stages of maintenance (shown below), and how some stages can be more beneficial than others.
Although reactive maintenance (fixing a part of a machine after it fails) can be costly, it does provide a sound basis to begin tracking and analyzing your machines’ overall performance and the parts’ performance of those machines so that you can effectively progress along the maintenance spectrum.
Advancing from reactive to preventative and then on to predictive maintenance schedules allows maintenance managers and operators to maximize machine output and minimize machine downtime, with the critical measurement at hand being charged downtime. Time can be your best friend or worst enemy depending on the amount of pre-planning you have done on your machines’ maintenance schedules.
In order to truly reap the benefits of a preventative maintenance schedule and set the foundation for more predictive maintenance, you need to first identify when the cost of replacement is greater than the cost of repair.
To arrive at the answer, our trainers at NTT recommend gathering these initial data points:
NTT recommends documenting this information for every piece of equipment owned and operated by your company, unless the lifecycle of that machine is towards the end when the cost of fixing the machine is greater than the cost of replacing it.
Now let’s investigate why these data points are so critical and the benefits to each.
|Quantity of Machines Made||
|Importance||The lower the quantity of machines made, the higher the cost to replace the machine.||When a critical part is not functional and you do not have a spare, it halts your operation, and the downtime can be significant.||The longer it takes to replace a part, the higher the cost of downtime.||Ultimately, the size of your storage area to keep and maintain spare parts will dictate how many you keep on hand.|
|Example||If there are only 3 of these machines made in the U.S., you need to increase your stock of replacement parts.||If you have a low supply (or no supply) of your critical spare parts, and a critical spare causes a machine shut-down, the cost of downtime is higher.||If the run-time on a machine produces $500,000/hour in output, and the lead time for a part is 5 hours, it will cost you $2.5 million in downtime.||
When you have access to a critical spare part and the normal lead time is 5 hours to obtain the part at $500K, the cost is $2.5M, when the original cost of the part may have only been $8K.
When you have that part on hand and replace it in 1 hour, your cost is $508K.
As a result, there is nearly $2M in cost savings, that you do not have to make up chasing in overtime and losing profit margins on your product.
If you have a sizable stock of replacement parts, it cuts down on the lead time to replace that part.
Also, it allows you to understand how many people are competing with you for the same parts, so you understand the market for having certain parts that are not easily accessible.
|If you know which parts directly affect output, you can plan to stock those critical spare parts to prevent extended machine downtime.||If you track the amount of lead time on parts accurately, you can plan accordingly and have the parts right before the machine breaks down and minimize the downtime spent waiting for the part to arrive; which in turn saves money in downtime and waiting for a part to arrive before you can start the process of changing out the bad part.||If you understand your lead times and stock the right parts, specifically those critical spares, you can save yourself from extended downtimes on your machinery.|
Once you have started documenting these data points for your machines and machines’ parts, the next step is to analyze the data and use critical thinking skills to develop a more sophisticated maintenance routine, incorporating preventative tasks into a predictive maintenance schedule.
Preventative maintenance tasks involve routinely inspecting your machines and machines’ parts, first engaging the senses to see, hear and touch the parts for any errors or damages. The next step is to do the analysis, using the critical factors we identified above, along with data from the manufacturer about how often that certain part is used and the projected lifecycle of the equipment. NTT also recommends considering the environment and atmosphere that the equipment “lives” in. For instance, if the manufacturer states a projected 10-year lifecycle for a machine in a moisture-rich environment with levels of 80-85% humidity, the machine’s lifecycle will decrease if the machine is being used in Colorado where humidity levels only average around 52%.
NTT also recommends that maintenance managers make use of data from a computerized maintenance management system (CMMS) for the most reliable reporting of a machine’s lifecycle. We do caution that the “garbage in-garbage out” rule does apply here, such that if you are allowing bad or vague maintenance action reporting, you will have inaccurate information to review and not be able to schedule precise machine repairs to keep ahead of the repair cycle.
How are you currently monitoring and tracking your machines and their parts? What type of system works best for you?
Check back soon for our next blog to learn more about what components should be in your preventative/predictive maintenance schedule in terms of understanding what you have planned for and what you haven’t planned.
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