From our blog on Understanding Throughput, we established how it's a critical manufacturing KPI for to measure for improving efficiency and profitability. As we noted, we can calculate throughput for individual machines in a manufacturing line or for the entire line as a whole. For example, within a brewery line we have may have a filter and a packaging station, each one with their own throughput. At the same time, the line as a whole has its own final throughput. Moreover, for product to get to the customer’s hands takes more time, so we may evaluate our distribution throughput. It is also important to note that not all machines will work at a 100% efficiency, and some products may not hold up to standards after production so throughput net of quality may be important to understand.
There are several ways to calculate throughput taking these realities into consideration.
Different Ways to Calculate Throughput
We understand throughput as being the average number of units being produced on a machine, line, unit or plant over a specified period of time. However, there are several aspects of a manufacturing plant that come into play when measuring throughput. We can think about the throughput of an individual machine as we can think about the throughput of an entire manufacturing line. Both play a crucial role between each other and both are important to understand and analyze. Before we do so, it is important to know that there will always be common and special causes that affect our business.
Manufacturing lines do not run at 100% efficiency non-stop; there's going to be downtime caused by either machinery malfunction and/or unexpected situations such as a winter storm that causes production plant to shut down momentarily. We have to consider these cases when calculating our throughput, as it takes time for machines to be repaired or start up again after a production halt. For this consideration, we calculate a machine’s uptime percentage, which is a fraction of the mean time a machine runs between failures and halts over the mean total time. The mean total time is not only the amount of time a machine is running, but also the time it takes to repair and start it up again after a halt.
So then, uptime percentage is:
Uptime% = Running time / Total time
Uptime percentage also tells us how efficient a machine is. For example: If the brewery line noted above shuts down for 5 minutes every hour due to a defective component, its uptime percentage would be:
60 minutes / 60 minutes + 5 minutes
60 minutes / 65 minutes
= 0.923 → 92.3%
So if we measured our Brewery throughput to be 6,000 cans per hour at full capacity, with our calculated uptime percentage, we can reflect a more realistic throughput.
6,000 cans/hour * 0.923 = 5,538 cans/hour
Our brewery line’s throughput is closer to 5,538 cans/hour instead. That is almost 500 cans less than previously calculated. This is an example on how throughput, along with downtime consideration, can give us valuable and eye-opening insight into our operations. This also reflects the importance of efficiency and the drastic effects of just a couple minutes of downtime.
We just calculated throughput for an individual machine. However, our brewery line not only consists of this machinery. Suppose that there are 2 other machines in our brewery. One that labels all cans and another one that packages them. We know that our labeling machine is old and capable of labeling only 5,300 cans per hour but has an efficiency of 98% due to its superior quality. We also have a packaging machine, that handles one can at a time into their respective containers. This machine has the capacity to package 6,100 cans per hour and it is 93% efficient. We can calculate the actual throughput of each machine.
Labeler: 5,300 cans/hour * 0.98 = 5,194 cans/hour
Packager: 6,100 cans/hour * 0.93 = 5,673 cans/hour
Throughput Can Get Complicated - Make Sure You're Measuring the Right Thing!
It looks like all of our 3 machines handle around the same amount of cans per hour. It looks like a solid production plant. That is what we tend to think, but we may forget that machines’s downtimes do not occur at the same time, nor do they all last the same. One machine being shut off for 5 minutes, like our initial canning machine, can affect and halt the labeler and packager machines too. To calculate throughput for an entire production line we first calculate the entire line’s efficiency by multiplying each machine’s efficiency. In our example:
0.923 * 0.98 * 0.95 = 0.859 → 85.9%
Our entire production line is only 85.9% efficient! And its overall throughput? In order to calculate an entire line’s throughput we need to consider only the lowest throughput between all of its machines. Why? Well, because a production line can only produce what this slowest machine produces in the long run. This slowest machine is known as the “bottleneck”, and it will keep posterior machines waiting for its output while it processes accumulated output from previous machines that get to it at a faster rate that it can process them. Our bottleneck in this example is our labeler, with a throughput of 5,194 cans/hour.
Multiplying this throughput by the entire line’s efficiency we get:
5,194 cans/hour * 0.859 = 4,462 cans/hour
Our brewery is only able to produce 4,462 cans per hour, over a thousand less cans from the initial believe. It is extremely important to calculate the throughput of our machinery, and keep an eye to them.
If you'd like to learn how Smart Factory Analytics can help you to de-bottleneck your facility, take a few minutes and watch our demo:
Another way of thinking about throughput, although not exclusive, is depending on the amount of output that is good for sell. There can be some deviations in production, and defective items may be a result from it. These items cannot be count towards throughput, because the company will not be able to gain revenue from them. This defective items can be a result of machinery malfunction and sudden downtime, adding more to this earlier problem. We can account for this in a simple throughput calculation.
Suppose that 250 cans come out as defective and therefore not up for profit out of the 4,462 cans our brewery produces in an hour. We can calculate a ‘process yield’, which is the fraction of good units produced. Our process yield is
Process yield = 4,462 cans / (4,462 cans + 250 cans) = 0.9469 → 94.69%
94.69% of our cans are good to sell. We multiply this fraction by our throughput of 4,462 cans/hour to get the accurate production rate that the brewery will profit from.
Throughput = Throughput rate * Process Yield
= 4,462 cans/hour * 0.9469
= 4,225 cans/hour
Around 240 cans come out defective from production. This, in the long run, can be a big loss for our company. As we can see, throughput is key to tell how production is taking place in any company. It is the most important manufacturing metric and must be kept an eye on at all times. Constant monitoring will result in lower downtimes and a better throughput. Effective throughput at each machine results in an overall effective throughput at a plant level. However, knowing the throughput of each machinery can be difficult. Counting the amount of output by eye, or the total down time from all machines, can be a difficult task to do.
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