In my last posting, I told you that over the next series of posts I would show you how to generate more capacity than you have now. When I’ve told other companies this same thing, for the most part these companies have responded by saying they are running at full capacity already. This is simply not true for the overwhelming majority of companies!
Most companies have what is referred to as hidden capacity that is just waiting to be discovered and used, and it’s usually to the tune of at least 20 percent. Hard to believe?
A typical process
In the first several posts I have explained the concept of the system constraint, or bottleneck, and how it controls the rate at which a process can produce products. To refresh your memory, let’s look at a very simple four-step process used to produce something. Raw material is introduced into Step 1 and is processed for 5 minutes. The semi-finished product is then passed to Step 2 where processing takes 10 minutes. At the end of the 10 minutes the semi-finished product is passed on to Step 3 which takes 20 minutes to complete. It’s passed on to Step 4 which takes 10 minutes to complete and is then considered finished product.
True capacity of a process
So let’s look at this from a couple of different perspectives. If this was a new process, just starting up for the first time, the first part would take a total of 5 + 10 + 20 + 10 minutes for a total of 45 minutes to complete. However, after the first part is completed through Step 1 and passed to Step 2, typically Step 1 would immediately start producing a second part and immediately parts (WIP) would begin staking up in front of Step 2. Why? Because Step 2 takes twice as long as Step 1.
Similarly, Step 3 takes twice as long as Step 2, so WIP also builds up in front of Step 3. If you’re like most companies who use efficiency as a performance metric, you are looking for each step to run at maximum capacity. But is this a good thing to do? The answer is no, and it isn’t simply because excessive WIP lengthens the total cycle time of the process. Think about how many parts each step in the process would have produced after having run “full-out” for a single day. If we assume that this process runs one shift a day and we further assume that there are two 15-minute breaks and a lunch that lasts for 30 minutes, then the actual run time available (assuming no downtime) would be 420 minutes.
So even though Step 1 produced 84 parts in the 7 hours of run time, Step 2 could only process half of them. So at the end of the day, Step 2 has 42 parts sitting in front of it. Similarly, Step 3 could only process half of Step 2’s output (i.e. 21 parts), so it has 21 parts waiting to be processed. Because Step 4 is twice as fast as Step 3, it has the capacity to process 42 parts, but unfortunately it only received 21 parts from Step 3 to turn into finished product. So I ask you, does it make any sense at all to run all steps to their maximum capacity? The answer is, no it does not. So what’s the answer?
The better way to run your processes
It should be clear at this point that Step 3 controls the output of this process, so it is referred to as the constraint or bottleneck. The only way to generate more output for this process is to reduce Step 3’s processing time or to free it up to process more parts. One simple way to do this is to keep Step 3 running during breaks and lunches. If we did this, Step 3 could now produce at a rate of 24 parts per day. The operator at Step 1 would be the most logical candidate to run Step 3 during breaks and lunches and then have his/her breaks and lunch at a different time. This would cost the company zero dollars but would increase the effective capacity by nearly 15 percent.
In my next posting we’ll continue to look for ways to increase the capacity of this process even more.
Thanks for reading. See you next time.
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