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What is an MES?
Data and metrics that drive the factory (Part 4/5)

Key MES data and metrics…and how manufacturers use them.

Part 4 – Key Data and Metrics

While process definitions are important in coordinating the work being done in the factory, the data created during processing is equally important (but often overlooked). In part 4 of this five-part series, we look at some of the data generated by the MES and the factory metrics that can be derived from it. We also discuss how manufacturers use this information to improve operations and keep the factory running smoothly.


Welcome to part four in this series, “What is an MES?” In this part, we’ll continue our deep dive into lot tracking, looking closer at lots and equipment, the key data they generate within the MES, and the metrics that can be derived from this data. A lot’s journey through manufacturing creates a lot of data, and much of that is related to cycle time (that is, the total amount of time a lot spends in manufacturing). It includes considerations like whether a lot is on hold, slowing the lot’s progress in manufacturing, or has been designated as a high-priority lot, effectively speeding the lot up.

I’d like to point out a couple of the more interesting items. The due date is the date the lot is required to be finished and ready to ship. It’s often based on contractual terms between the manufacturer and their customer.

The estimated time remaining is the expected amount of time a lot will take to complete manufacturing. It serves as a countdown timer for the manufacturing staff for how much time they have left to finish the lot. And the estimated completion date uses the estimated time remaining to determine the expected date and time the lot will finish in manufacturing.

When comparing the estimated completion date against the due date, it’s possible to determine the amount of time the lot is ahead or behind schedule in manufacturing. This in turn, is used to prioritize lot order within equipment lot queues and keep lots moving in manufacturing with the goal of shipping on time. Some interesting metrics can be derived from lot information.

One of the most important is cycle time, which can be thought of as how fast a lot makes it through manufacturing. If we can decrease a lot’s cycle time, then that would make room for another lot to process during the time that was freed up. If we can decrease the cycle time for all lots, then potentially a lot of room can be freed up for many other lots to process, effectively increasing the overall output of the factory.

And, of course, that means increasing the potential for additional revenue, and that is a worthwhile goal. So that’s why manufacturers pay close attention to cycle time and work constantly to manage it and decrease it.

Yield is another important factory metric. It’s simply the ratio of the number of units that finish in manufacturing to the number of units that were originally started. If you need 100 units to fill a customer’s order and you start 100 units in the factory, but a few get messed up and scrapped along the way, you don’t wind up with 100 units the customer needs.

So you have to start more than 100 units in order to get the 100 units the customer needs. If you start 105 units and you finish with 100 units, your yield is, let’s see, that was number completed divided by the number started, so 100/105 or, 95% yield.

Yield is an important contributor to the cost of manufacturing. If the raw materials for the widget you’re building are expensive and you scrap a lot of them during the manufacturing process, the cost basis of the widgets that you complete are higher than they otherwise would be. If you start 100 and end up with 95, the cost of the 5 that were scrapped during manufacturing needs to be spread among the 95 you’re able to sell.

Higher cost means lower margin. Yep, that’s right. This is a business problem, not just a manufacturing problem.

And that’s why manufacturers work very hard to increase yield, because increasing yield decreases costs and lower costs mean higher margins. Each one of the lot’s key metrics has a story like this about how it affects the business of manufacturing. And the underlying data for each of these metrics is generated by the MES.

The data the equipment generates within the MES is largely related to utilization and factory throughput or how much of the time tools are available for productive processing to keep lots moving through the factory. This includes information like how long it takes to process a lot or the amount of time the equipment is unable to process lots because of, for example, scheduled maintenance. Again, I want to point out a few of the more interesting items.

The state of the equipment is typically expressed in terms of up, down, or idle. Up means it’s currently processing. Idle means it’s not processing, but is ready to process.

And down means the equipment is unable to process. The more the equipment is logged to a down state, the less it can be productive to contribute to overall factory output. So the equipment state is something that manufacturers watch very carefully.

As we’ve discussed before, an equipment’s lot queue represents the lots that have been dispatched to the equipment and are waiting to be processed. Long lot queues are often a factor in cycle time problems. The longer the queue, the longer each lot has to wait before processing.

And an equipment with a consistently long lot queue may indicate the equipment is a bottleneck in the factory. That is, the throughput of the entire factory is limited by that equipment, just like the slowest car on a windy two-lane road sets the pace for all the drivers behind. It’s important to keep bottleneck equipment up and running as much as possible, else the output of the entire factory suffers.

Processing time is the amount of time it takes an equipment to process a lot. We’d expect that time to be pretty much consistent from one lot to the next, running the same process recipe. But sometimes the processing time changes.

It could be an abrupt change because an internal component failed, or it could be a gradual change as the equipment drifts out of calibration over time. Either way, manufacturers pay close attention to processing time because changes in processing time likely point to a problem that needs to be fixed. Dividing a unit of time, say an hour, by the processing time, we get a key equipment metric called throughput.

Throughput is the number of lots that can be processed per unit of time. So if we wanted to know the number of lots that a piece of equipment can process in an hour, we’d take 60 minutes divided by the number of minutes it takes to process a lot. If it takes 10 minutes to process a lot, the throughput for that equipment would be 60 minutes divided by 10 minutes per lot, or 6 lots per hour.

For bottleneck equipment, multiply that number by the number of tools you have of the bottleneck equipment type, and you get the maximum factory throughput. So if the throughput of the bottleneck process tool is 6 lots per hour, and you have 5 of those tools, the maximum factory throughput will be 30 lots per hour. That’s a pretty important thing to understand.

Another key metric derived from equipment information is utilization. Utilization is a productivity metric. It’s the amount of time a piece of equipment is used for productive processing, that is, processing lots that can potentially be sold.

To better understand utilization, I find it helpful to look at what is not included in utilization and simply deduct that from total time. For example, scheduled maintenance can be deducted because when the equipment is being maintained, it’s not being used to process lots. And of course, so can unscheduled maintenance, when the equipment is unexpectedly broken and undergoing repairs to get it back up and running.

And when the equipment isn’t doing anything at all, it’s just idle. This can be for any reason. Maybe because there are no lots for the equipment to process.

Perhaps the equipment operator is at lunch. Maybe the operator is simply busy with something else and can’t start a new lot and idle equipment isn’t being utilized.

Manufacturers want utilization to be as high as possible, particularly on their bottleneck tools, because greater productive use increases factory output, which increases potential revenue. Yep, again, this is about the business of manufacturing. And again, the underlying data for each of the key equipment metrics is generated by the MES.

We’ll finish off this series in Part 5 by looking at how the data generated by the MES can be used for reporting and analytics.

About the Author

Picture of Dan Meier, Director of MES Strategy
Dan Meier, Director of MES Strategy
Dan is a seasoned manufacturing professional with nearly 30 years of operational and management experience. He has an extensive background in manufacturing optimization, quality systems, analytics, financial modeling, factory automation, and manufacturing software systems. Dan earned bachelor’s and master’s degrees in music from The Juilliard School, as well as a Master of Science in Electrical Engineering and a Master of Business Administration from the University of New Mexico.