- January 18, 2017
- Posted by: David Marshall
- Category: Business, Measurement
In previous articles, I’ve discussed the importance of measuring and monitoring your plant workflow, in order to find any problem areas, whether it’s an associate, a machine, or a process. When you’re measuring plant workflow, you’re not only looking for productivity numbers to make sure you’re meeting your projections, you’re also looking for problem areas, like breakdowns and other issues that lead to serious downtime.
The accumulation of downtime data in terms of causes and remedies becomes important because there’s a point you have to ask yourself whether you have an equipment, system, or people problem. The only way you can do that with any level assurance is with accurate, timely data.
The Ripples of Equipment Breakdowns
Typically, when a piece of equipment goes down, the ripples can be felt throughout the operation. Your active time goes down as your associate is left without a working machine. Your scrap goes up as the machine starts producing damaged or less-than-perfect pieces, which drives up costs of raw materials. And your productivity goes down as your parts per hour and associate productivity drop.
You can always reassign the associate to a different task or a different piece of equipment, or you can release them for the day. None of those are ideal, but that’s a way to greatly reduce the loss and reduction. But you may have a bigger problem on your hand.
Over time, as you track the reasons why the equipment went down, you’ll see a pattern. For example, it could be the same motor that keeps breaking down. It could be the wrong size or shape, or is overheating, or isn’t made for its current workload. Once you identify that pattern, you can troubleshoot the cause and find a working solution that will eliminate the problem.
On the other hand, if this happens regularly, but for a lot of different reasons, that piece of equipment might have reached the end of its useful life, so you need to decide whether to replace it.
Tracking your downtime and the equipment breakdowns gives you the ability to measure a return on investment for repairing or replacing that equipment. Then, this becomes a discussion of fact and data, not an estimation based on the vagaries of human memory.
I would even suggest we use the term “return on innovation.” Because if you have a 20 year old piece of equipment on its last legs, this could be the time to make a change in your manufacturing process.
Think about what you can do if you innovate within your operation and create a new way of producing your goods in your plant. You can innovate on your machine and come up with a new process of creating your parts, or you can even create a new part altogether.
A new machine can usher in a brand new way of doing things. Because if an old machine is expensive enough, it might be worth changing the way you make your product entirely. You limit yourself if you only think about how to replace an expensive machine with another expensive machine.
Ultimately, the important thing is to be able to calculate a return on the investment of replacing your existing machine or creating a new machine. You can only do that with real data and measurement, not the faulty memory of the human mind with half-remembered stories and anecdotes.
Because you could actually accidentally replace your existing machine with one that has so much capacity you’d never use it. Or you could create a new machine and a new part that doesn’t actually suit what you need. In the end, you would never see that positive ROI, and would have to fight to fill in the hole you’ve dug for yourself.
I’ve been in that situation several times, and it’s only because we were able to measure downtime and productivity that we were able to make the most informed choice.
I’ve been a manufacturing executive, as well as a sales and marketing professional, for a few decades. Now I help companies turn around their own business. If you would like more information, please visit my website and connect with me on Twitter or LinkedIn.