1. Run at Rate Key Concepts Rolled Throughput Yield and Overall Equipment Effectiveness and the 5 Day Work Week / Standard Production Year
Mark Oesterling |

2. Two Important Concepts
The concepts of Rolled Throughput Yield (RTY) and Overall Equipment Effectiveness (OEE) are key to having a successful, accurate run at rate. But more than this, they are key to properly loading your factory and not overselling your capacity.
Most do not understand how to get these metrics accurately and the result is that the costing groups believe there is more manufacturing time on the floor than there actually is. The result of this the factory begins to play catch up. Unplanned overtime, excessive air freight shipments are the two earmarks of a facility that does a poor job of RTY and OEE.
This leads to the “death spiral.” You can’t catch up because you don’t have excess time. But you can’t make excess time because you are always behind. Bad things begin to happen. Employee turnover increases because your people are overworked. You have to offload sometimes choice jobs at a higher cost and winning jobs turn into losers.

3. Rolled Throughput Yield

4. Rolled Throughput Yield
Simplest first, rolled throughput yield. Think of this as a net scrap rate.
You are the plant manager of a factory that applies confectionary treatments to baked goods. A syrup and sprinkles guy. Here’s your business model:
You send back well decorated baked goods for profit !!!
Your customer ships you bare baked goods
But in reality, you will make bad ones. RTY helps you determine how many bare donuts you need to ensure you meet your requirements.
You unleash your secret process handed down from your grandma for generations

5. Rolled Throughput Yield is your overall scrap rate.
Let’s start your process and track how you do fulfilling an order of 1,000 donuts.
Process 1
Drizzle on Chocolate
Process 2
Add Sprinkles
1) You purchase 1,000 blanks.
1000
920 80
2) You run your first process, but it messes up 80 of the blanks – no chocolate !!
1,000
819
101
920
3) You run your second process, but it messes up 101 of the blanks – not enough sprinkles !!
You know this is correct, because you know you bought 1,000 blanks and only shipped 819.
4) You are now going to short your customer because you only have 819 donuts to a target of 1,000 so you calculate your first time through and your scrap like this:
𝐹𝑇𝑇= =81.9%
𝑆𝑅= 1000− =18.1%
Process 1
Process 2
Good
Bad
Total
Scrap Rate

6. Rolled Throughput Yield is your overall scrap rate.
You know: FTT = 81.9 % and SR = 18.1%
Process 1
Drizzle on Chocolate
Process 2
Add Sprinkles
1000
920
819
You tell your drizzle engineer and your sprinkle engineer to investigate the last run.
They know that:
𝑠𝑐𝑟𝑎𝑝= 𝑏𝑎𝑑 𝑝𝑖𝑒𝑐𝑒𝑠 𝑡𝑜𝑡𝑎𝑙 𝑝𝑖𝑒𝑐𝑒𝑠
And fill out the scrap report.
11% 8% 80
101
These are the right scrap rates. But notice how they compare to the overall scrap rate:
They don’t SUM to it. ( ) = 0.19, or 19%. That’s close …but not right.
They don’t AVERAGE to it. The average is:
( ) 2 = .095=9.5%
How do you determine the plant scrap rate from the process scrap rates? This is the rolled throughput yield …..
Process 1
Process 2
Good
Bad
Total
Scrap Rate
920
819 80
101
1,000
920

7. Rolled Throughput Yield is your overall scrap rate.
You know: FTT = 81.9 % and SR = 18.1%
Process 1
Drizzle on Chocolate
Process 2
Add Sprinkles
1000
920
819 80
101
Again, first time through (percent good) is found from:
𝐹𝑇𝑇= 𝑔𝑜𝑜𝑑 𝑝𝑖𝑒𝑐𝑒𝑠 𝑡𝑜𝑡𝑎𝑙 𝑝𝑖𝑒𝑐𝑒𝑠
Rolled throughput yield is found by multiplying all the FTTs together.
Process 1
Process 2
Good
Bad
Total
Scrap Rate
FTT
𝑅𝑇𝑌=0.92 ∗0.89= .819=81.9 %
920
819
This is known as a geometric mean, as opposed to an arithmetic mean.
This is the overall plant first time through, and of course, the overall scrap rate is simply this subtracted from 100% 80
101
1,000
920 8%
11%
𝑆𝑅=1.00 −𝑅𝑇𝑌=1.00 −.819=0.181=18.1%
92%
89%

8. Where this really is important, is determining how many blanks
The needed blanks can be easily found from:
𝑅𝑇𝑌= 𝑑𝑒𝑠𝑖𝑟𝑒𝑑 𝑝𝑖𝑒𝑐𝑒𝑠 𝑛𝑒𝑒𝑑𝑒𝑑 𝑏𝑙𝑎𝑛𝑘𝑠
The customer wanted 1,000 good decorated donuts, so we should have ordered:
Or:
𝑛𝑒𝑒𝑑𝑒𝑑 𝑏𝑙𝑎𝑛𝑘𝑠= 1, =1,221
𝑛𝑒𝑒𝑑𝑒𝑑 𝑏𝑙𝑎𝑛𝑘𝑠= 𝑑𝑒𝑠𝑖𝑟𝑒𝑑 𝑝𝑖𝑒𝑐𝑒𝑠 𝑅𝑇𝑌
We should have ordered 1,221 blanks.
Our process would then have looked like this:

9. Rolled Throughput Yield is your overall scrap rate.
You know: FTT = 81.9 % and SR = 18.1%
Process 1
Drizzle on Chocolate
Process 2
Add Sprinkles
1221
1123
999 98
124
Knowing your RTY means you can properly order parts.
You may be thinking …. I can just get this from my purchase order for blanks and my shipper for finished donuts. You can and that’s an excellent verification. But we have a bit more complexity in our worlds.
But excess parts must be ordered to net out your target number due to intermediate scrap losses.
Process 1
Process 2
Good
Bad
Total
Scrap Rate
FTT
Process 1
Process 2
Good
Bad
Total
Scrap Rate
FTT
920
819
1,123
999 80
101 98
124
1,000
920
1,221
1,123 8%
11% 8%
11%
SAME!
92%
89%
92%
89%

10. RTY in the Real World
Here’s an excerpt of the GKN worksheet with some rows hidden to show the important concepts.
You enter in the estimated scrap rate for each process (pink row) and the RTY is calculated for you (purple row).
Notice as you go from last to first process, the RTY gets lower and lower, the result of it’s cumulative nature.
At the intermediate location, the RTY means the percent of parts that enter this process that will make it off the last process.
The last two rows are the process entrance and exit quantities. These account for the RTY.
For example, here, 99.2% of the parts that enter process 6 will pass process 6 (the process first time through). BUT only 91.58% of the parts that enter process 6 will make it off the LAST process, or the rest of the way - the process rolled throughput yield.
This works back up the chain, increasing the needed amount of input parts until you work back to process 1.
Key Point: Assume you intend to outsource heat treating. And you have a target APW of 4,875. The target you need to give the heat treater would be 5,366 because of the RTY.
For any process, the process decrease is the input quantity times the FTT (red arrow). The total decrease is the input quantity times the RTY (green arrow).

11. Overall Equipment Effectiveness

12. Overall Equipment Effectiveness (OEE) Textbook Definition
Textbook Definition of OEE
Load Time is the time the job occupies space in the machine, the machine is loaded.
Operating Time is the time that the job actually runs.
Down Time is the time it did not run. This is the accumulation of the setup time, any scheduled stops for maintenance, any scheduled stops for employee breaks, and unplanned downtime. The setup time is easy to account for as are the breaks. The unplanned downtime is usually tracked via work orders or run cards.
Net Operating Time is the time it runs at nominal cycle time. This piece can be confusing. It is determined by counting parts in the operating time block (good and bad) and multiplying by the cycle time. It should be less or equal to the operating time.
Special Losses are the unaccounted stops. The operator gets a drink of water. Some guard gets loose and the operator stops to tighten it. A supervisor comes up and has a conversation. These are not reported stoppages, but they are stoppages.
Valuable Time is the time you spent making good parts out of your Net Operating Time
Defects is the time you spent making bad parts. Calculated from scrap rates.
Load Time
Operating Time
(Availability)
Net Operating Time
(Performance Efficiency)
Valuable Time
(Quality)
Down Time
Special Losses
Defects
Each of these values is calculated as a percentage. The OEE is then the three percentages multiplied together.

13. OEE – A Practical Example
Here is an hourly calendar that spans 3 days.

14. OEE – A Practical Example
Lets analyze a hypothetical 2 shift operation:
First shift is from 0300 to 1300 (a ten hour shift)
Second is from 1300 to 2300
Overlaid on this graph, your plant is open for the orange time (the load time in OEE definition)
Notice the grey time … nothing is running during this time
Some people want this included in OEE (using the strict definition of any time not making a saleable part is a loss of effectiveness). This is not correct by the standard definition.
The standard way is to exclude this gray time and only consider time scheduled. You do not exclude lunch breaks, however. You only exclude time where you do not have operators scheduled.
The two graphs at the right will track how much time we are occupying. The upper one will be out of the elapsed time (3 ea 24 hour days) The bottom will be out of the OEE definition of the load time.
Here is an hourly calendar that spans 3 days.

15. OEE – A Practical Example
Your scheduler then says you only need to run the job for about 40 hours to make your numbers
You decide the job will start at 0600 on Monday (you have a run to finish)
And you figure you can stop running about 1000 on Wednesday
Notice the change to your OEE load time. There’s two partial shifts involved now
You are using much less of the elapsed time (you ARE theoretically being less effective)
But the OEE load time is still the same – it is 100% of the time, even though it is now 16 hours less than before.
This is because the denominator of OEE is total scheduled time.
Let’s take care of setup time ….

16. OEE – A Practical Example
With a bit better visuals ….
The time the factory doors are closed have been shaded black.
The dark grey time is time this machine is running other work.
Let’s assume it takes 3 hours to do a swap of the tool. One hour to remove it, and two hours to set the next tool. Again, 3 hours total from last good part to first good part.
So Monday morning, you stop the run at 5 am and we start the clock on the new job at 6 am (we need to account for two hours setup here)
Wednesday at 10 am you need the machine empty so you can begin to set the next job.
There is no change to the load time, but we are going to mark it according to the OEE colors to the left, replacing Load Time with Operating Time and Down time. The colors are going to change …

17. OEE – A Practical Example
This is the teardown of the previous job – removing tooling until the machine is empty.
At the end of the job, you will have more downtime. You have to remove the tooling for the next job. This hour takes you from the last good part to the empty machine, ready for setup on the next job.
Setup of the next job, (light gray) then the next job starts running (dark grey).
The clock starts on the current job at 600 hrs. But the first two hours are setup time. Billable to this job. And it’s downtime.
In both graphs to the right, the orange Load Time has been replaced with the green Operating Time and Downtime.
Key point – this is 3 days. What if the job ran two weeks? Here is where including elapsed time can get you in trouble. Relatively, the green areas would get larger. Tuesday is a full run day. If I copied Tuesday a dozen more times, the elapsed time would shrink.
You can easily get all these durations simply from writing down the times.

18. OEE – A Practical ExampleB A C A A
Unfortunately, that’s not the end of the downtime story
You have your 30 minute lunch breaks on each shift where you aren’t running the job.
You have 2 hours and 20 minutes unplanned downtime where the machine broke
Every Tuesday night at 9, the machine needs all its filter replaced, that takes an hour.
All these events count as down time.
The Operating Time is 80% of the Load Time
The Down Time is 20% of the Load Time
Up until this point, we have been using the clock on the wall to figure the Down Time we know:
The time for the setup by the clock
The time for the breaks by the schedule
And the expected and unexpected equipment failures from the work orders.
Now we will turn the dark green blocks blue and begin to account for “Special Losses”
0.80
0.20

19. OEE – A Practical Example
The blue blocks now represent the dark green operating time and we have to now group out the Net Operating Time from the Special Losses. But what are the Special Losses?
All the Down Time we have seen to this point has been recordable by using a clock. Special Losses are not like that, they are smaller segments of time that do NOT typically trigger a work order, or a recordable event. But they are still losses. And they are calculated from the cycle time.
If you were now to count up the elapsed time of the blue boxes – the operating time, the time you run – you would find that it is in total 35 hours and 10 minutes. This is because we know it was scheduled from 6:00 am Monday until 10:00 am Tuesday which is a total time of 44 hrs scheduled. Remove from this a total downtime (green blocks) of 8 hours and 50 minutes and you have the blue run time of 35 hrs 10 minutes, or 2,110 minutes.
Our part takes exactly 10 minutes to make. And we know this from averaging some clean cycles. 10 minutes to make one when nothing goes wrong. That means for the blue time we should have:
0.80
0.20
2,110 min × 1 𝑝𝑎𝑟𝑡 10 𝑚𝑖𝑛 = 211 parts
But what if we have only 203 parts?

20. OEE – A Practical Example
If we only have 203 parts from an expected 211 parts, it does not mean that 8 parts are missing, it means the time to make the 8 parts was lost. These are your special losses. How much time was lost?
8 parts × 10 𝑚𝑖𝑛 1 𝑝𝑎𝑟𝑡 = 80 minutes
Now these 80 minutes are not all run together, they are sprinkled in throughout the run.
The operator gets a drink of water
A supervisor has a conversation with an operator distracting him
He waits an extra minute or two for the next part to get to him
The list goes on ….
0.80
0.20
The Net Operating Time is then:
𝑁𝑂𝑇= 𝑡𝑜𝑡𝑎𝑙 𝑝𝑎𝑟𝑡 ∗𝑐𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 = 203 ∗10 2,110 =0.962
0.96
0.04
Note that you use TOTAL parts, not scrap parts.

21. OEE – A Practical Example
On to scrap – the net operating time has now been turned purple. Consider our 203 parts …
If 26 of them are bad, then our FTT is found from:
𝐹𝑇𝑇= 𝑔𝑜𝑜𝑑 𝑝𝑎𝑟𝑡𝑠 𝑡𝑜𝑡𝑎𝑙 𝑝𝑎𝑟𝑡𝑠 = (203−26) 203 =0.872
At this point, you have counts which are easier to deal with than times. These scrap parts have been pictorially represented above (pink), but you don’t have to know where they came from. This is your “valuable time.” Your OEE is then the geometric mean of these three ratios:
𝑂𝐸𝐸=𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 ∗𝑃𝑒𝑟𝑓 𝐸𝑓𝑓 ∗𝑄𝑢𝑎𝑙𝑖𝑡𝑦
=0.80∗0.96 ∗0.87=0.67
0.80
0.20
Out of Load Time !
In words, it is the percentage of time that you spent actually making good parts out of the time you attempted to make good parts. It excludes time you did not schedule your line, or were running something else, and because it includes scrap and cycles, it may be different job to job on the same machine.
0.96
0.04
Out of OperatingTime !
0.87
0.13
Out of Net OperatingTime !

22. Here is how you do it, step by step, on one page …
Load Time (LT)
𝐿𝑇= 𝑐𝑙𝑜𝑐𝑘 𝑡𝑖𝑚𝑒 𝑗𝑜𝑏 𝑒𝑛𝑑𝑒𝑑 −𝑐𝑙𝑜𝑐𝑘 𝑡𝑖𝑚𝑒 𝑗𝑜𝑏 𝑠𝑡𝑎𝑟𝑡𝑒𝑑 −𝑡𝑖𝑚𝑒 𝑛𝑜𝑡 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑
In words, it is the time the line or process was set up on the job less the time you don’t run. If you run 24/7, then time not scheduled would be zero. Time not scheduled does not include lunches/breaks. If the job is always in the machine, you can calculate a subset of it. You could do it on the day, week, whatever makes sense. LT is the time you were set up on the job including the setup time and breaks but excluding time off.
Operating Time (OT) = Availability = Uptime
𝑂𝑇= 𝐿𝑇 −𝐷𝑜𝑤𝑛𝑡𝑖𝑚𝑒 𝐿𝑇
This is the percent of time that you tried to run that you actually ran. The LT is the time you tried to run. You reduce this by the sum of: breaks/lunches, planned downtime (for maintenance), unplanned downtime/unexpected breakages and setup time. All the times you record where you could not run, but wanted to.
Net Operating Time (NOT) = Performance Efficiency = Uptime
𝑁𝑂𝑇= 𝑃𝑎𝑟𝑡 𝐶𝑦𝑐𝑙𝑒 𝑇𝑖𝑚𝑒 ∗𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑟𝑡𝑠 𝑂𝑇
Here is where you factor in your “unexplained losses”. These are the mini stoppages that aren’t long enough to trigger a recordable downtime event. Basically, if you have a good cycle time, and you know how many cycles you ran (good AND bad) you can divide this product by your OT and get a percentage of time you actually ran. This is a key concept – many people do not account for these special losses and it can be a large number.
Valuable Time (VT) = Quality = First time through
Overall Equipment Effectiveness (OEE)
𝑉𝑇= 𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑟𝑡𝑠−𝐷𝑒𝑓𝑒𝑐𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑟𝑡𝑠
Straightforward scrap calculation.
𝑂𝐸𝐸=𝑂𝑇 ∗𝑁𝑂𝑇 ∗𝑉𝑇
The geometric mean of the 3 components. In the graphic above, OEE is the percent of the dark purple bar out of the orange bar.

23. But is it a good metric? Answer: It’s OK It is a good thing to trend
It makes a lot of sense on a weekly basis or longer interval, less so on a daily basis.
It is good to identify on a macro level that something went wrong. But ONLY that something went wrong, it doesn’t tell you where to look – an alarm, rather than a gage. (The subcomponents of it give you clues).
It is not bad comparing different lines – it levels the field.
It can be used collectively – it still has meaning if several lines/process in parallel are combined into one.
Remember that it includes setup time and scheduled downtime
If you are comparing jobs, and one of them has a longer setup time because it is complex, it will have a worse OEE. This may hide the fact that the simpler job is “running” worse. The unexpected downtime and scrap of the simpler job may get overshadowed.
The same applies for scheduled downtime. In TPM, scheduled downtime isn’t a “bad” thing. It is a necessary thing to prevent more and longer unexpected downtime. It is arguable that scheduled down time is like scheduled off time and should be excluded.
Typically, it is better suited for monthly reports. You will have quicker diagnostics for day to day operations if you track downtime, special losses, and defects separately.
It is more important that your metrics are SMART (specific, measurable, attainable, relevant, timely). If you modify OEE to better suit how your factory runs and collects data, then by all means, do so. But you should be trending some sort of utilization metric and it should be used in your quoting/planning. Understating this metric results in lots of overtime and air freight. The GKN Run at Rate uses a slightly modified OEE from this method.

24. Where do people fail at OEE?
Answer: Everyone fails at Net Operating Time
Valuable Time (First Time Through) and Defects (Scrap Rate) are easy, everyone knows how many parts they made, and how many are good and bad. (Keep in mind, if your customer rejects parts you THOUGHT were good, you really should back them in to any long term utilization calculations. Consider a short term (your data) and a long term (including your customer rejects) utilization trend.
Operating Time is also typically called Uptime. Most people get this from maintenance work orders. This is important because maintenance work orders typically have categories, and this allows you to Pareto your reasons for downtime. Many good metrics come these methods, but they have limitations in figuring OEE – they typically miss the Special Losses.
In a typical maintenance work order system, people are keying in the times. People round to the nearest hour. People don’t bother filling out a sheet for a 5 minute job. This results in ALL Downtime not being fully captured. When sales and scheduling figure up jobs, they think the factory is more efficient than it is. This results in overestimating available time which forces production into running weekends and air freighting to catch up. If you run a lot of weekends and do a lot of air freighting, a good thing to check is your utilization figures in your estimating and scheduling. (You think it’s 85% when it is really 65%).
Because work orders and downtime results rely on user inputs, it is often better to reverse the method to calculate Operating Time and Net Operating Time. See next page….

25. Alternate Calculation Method
𝐿𝑇= 𝑐𝑙𝑜𝑐𝑘 𝑡𝑖𝑚𝑒 𝑗𝑜𝑏 𝑒𝑛𝑑𝑒𝑑 −𝑐𝑙𝑜𝑐𝑘 𝑡𝑖𝑚𝑒 𝑗𝑜𝑏 𝑠𝑡𝑎𝑟𝑡𝑒𝑑 −𝑡𝑖𝑚𝑒 𝑛𝑜𝑡 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑
Calculate LT the same way
𝑁𝑂𝑇𝑛𝑒𝑤= 𝑃𝑎𝑟𝑡 𝐶𝑦𝑐𝑙𝑒 𝑇𝑖𝑚𝑒 ∗𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑟𝑡𝑠 𝐿𝑇
But calculate NOT differently, divide it by the ENTIRE LT.
𝑁𝑂𝑇𝑛𝑒𝑤=𝑂𝑇 ∗𝑁𝑂𝑇
For reference, NOTnew in this way will be equal to OT times NOT in the old way – you lump them together in one easier calc that is more accurate because you avoid work order accumulation errors, etc, entirely.
becomes
Load Time
Net Operating Time (NEW)
(Performance Efficiency)
Valuable Time
(Quality)
Downtime AND Special Losses
Defects
𝑉𝑇= 𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑟𝑡𝑠−𝐷𝑒𝑓𝑒𝑐𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑟𝑡𝑠
First time through is calculated the same.
𝑂𝐸𝐸=𝑁𝑂𝑇𝑛𝑒𝑤 ∗𝑉𝑇
And OEE is simplified to this.
You gain simplification and to an extent accuracy.
You lose discrimination between “downtime” and “special losses” – but do you care?
You want OEE to be accurate for your estimating and scheduling, not for fixing your downtime.
Keep your maintenance work orders and pareto them, use this to categorize your reasons for downtime so you can go after the significant causes. Just don’t use them as part of total utilization – they miss too much.

26. Collecting Up OEE 𝑂𝐸𝐸= (0.827+0.809+0.91+0.96) 4 𝑂𝐸𝐸=0.876
This presentation has been using the term “geometric mean” quite a bit. It is important to know the difference between this and an arithmetic mean. Here’s an example: you have a factory where you have an internal roughing line feeding two finishing lines. You have two more finishing lines, but for these, you purchase the roughed components. How do you determine your plant OEE?
1) For the lines in series, you find the geometric mean – multiplying the OEEs all together as decimals.
Roughing Line
OEE: 0.87
Finishing Line 1
OEE: 0.95
Finishing Line 2
OEE: 0.93
Finishing Line 3
OEE: 0.91
Finishing Line 4
OEE: 0.96
Purchased Blanks
0.87 * 0.95 = 0.827
2) For the lines in parallel, you find the arithmetic mean – adding all the OEEs all together and divide by the number.
0.87 * 0.93 = 0.809
0.91
0.96
𝑂𝐸𝐸= ( ) 4
𝑂𝐸𝐸=0.876
For the whole plant
Key point – if you have to aggregate Rolled Throughput Yield (RTY), you use the same method:
Process in series use a geometric mean (multiply them all together)
Processes in parallel use an arithmetic mean (add them and divide by the total count)
Key point – in Excel, you can multiply a list of numbers all together (geometric mean) using the PRODUCT function !!

27. 5 Day Work Week / Std Prod Year

28. Annual Volumes and the Standard Production Year
Definitions:
A standard production year is 47 ea 5 day work weeks
This is 47 * 5 = 235 days
It is also 235 * 24 = 5,640 hours
APW and APY are the average part volumes per week and per year
MPW and MPY are the maximum part volumes per week and per year
MPY is between 10% and 20% more than APY, it depends on OUR customer.
When you are given an APW, you must be able to hit this number in 5 days = 120 hours. Why? You will need your Saturdays and or Sundays to hit MPW.
When you are given an APY, you must be able to hit this number in a standard production year for the same reasons.

29. A Standard Production Year - Graphically
The circle represents an entire year of time.
Here is the 5,640 limit – you must achieve APY in this time. It is almost 2/3ds of a year.
This “BAL” area is the remaining time. Roughly the 5 weeks of time we haven’t accounted for. This is for your vacations, outages, etc.
At first glance, the requirement that volumes must be met using only 64% of the available time seems wasteful. Let’s look at an example …..
These are the Saturdays and Sundays that go with the 47 week limit. This is your wiggle room. You use this to recover from a spill OR hit MPW when MPW is called for.

30. Example of a Supply Bubble
1 weeks production
2500 pcs
But the supplier can only put 1000 pcs on the water because he lost the air freight shipment. This week, he runs another weekend, another 1000 pcs in addition to normal production.
Week 04
2 days
1000 pcs
Bubble!
1 weeks production
2500 pcs
The supplier runs one more day. He has now rebuilt one weeks worth of parts running Saturdays and Sundays. Things are steady again with balanced suppler except for the bubble.
Week 05
2 days
1000 pcs
1 dys 500 pcs
Bubble!
1 weeks production
2500 pcs
Finally back to normal. The counter is at 9 weeks. Lots can go wrong. In the BEST case, two air shipments were required.
Week 09
2 days
1000 pcs
1 dys 500 pcs
1 weeks production
2500 pcs
The bubble moves, the supplier is back to normal schedule and he is sitting on the excess parts.
Week 06
2 days
1000 pcs
1 dys 500 pcs
Bubble!
1 weeks production
2500 pcs
The air freight shipment arrives …. Just in time.
Week 03.3
2 days
1000 pcs
1 weeks production
2500 pcs
The current order goes on a plane and the supplier puts his factory on weekend shifts, running two more days … he can make another 1000 pcs, but he has 1500 to go.
Week 03.2
2 days
1000 pcs
1 weeks production
2500 pcs
Skip two weeks, the bubble is now due to arrive.
Week 08
2 days
1000 pcs
1 dys 500 pcs
Bubble!
1 weeks production
2500 pcs
Shipping …. Shipping …..
Week 02
1 weeks production
2500 pcs
Shipping ….
Week 01
1 weeks production
2500 pcs
The customer quarantines the shipment, but is one week shy. The supplier completes his current order.
Week 03.1
Week 00
1 weeks production
2500 pcs
The supplier has to put the balance of the bubble on a plane to catch the bubble. You may think he could have loaded these on the following boats, but they would not have arrived when needed!
Week 08.1
2 days
1000 pcs
1 dys 500 pcs
Bubble!
1 weeks production
2500 pcs
The parts arrive at the customer and he discovers they are bad. Things happen fast now ….
Week 03
The customer is exactly balanced consuming:
500 per day or
2500 per week
The supplier can make:
500 per day or
2500 per week
Supplier
Customer
1 weeks production
2500 pcs
But now suppose this shipment is completely bad. The supplier missed something.
1 weeks production
2500 pcs
1 weeks production
2500 pcs
1 weeks production
2500 pcs
1 weeks production
2500 pcs
1 weeks production
2500 pcs
1 weeks production
2500 pcs
1 weeks production
2500 pcs
Arrives n +5
Arrives n +4
Arrives n +3
Arrives n +2
Arrives n +1
Week n+6 (being produced)
Week n (being consumed)
Here is the nominal supply situation, well established. The supplier runs a 5 day week, the customer consumes a 5 day week.

31. Now we see the concept …
The cartoon was fun. Lets look at a sample with two suppliers with real numbers…..
Here we have two suppliers. Supplier B runs a bit slower making 458 / day as opposed to A making 520 / day.
The customer weekly need is 2,500.
We can divide into this each suppliers daily production and we find that because A is faster, he needs 4.81 days to make 2,500. Supplier B needs 5.46 days. This is almost 5.5 days. What this basically means is supplier B will have to run every other Saturday to make 2,500 per week on average. That doesn’t seem so bad, right? In fact, supplier B is only 13.5% slower (5.46/4.81 = 1.135)
If we assume we will max out at 6 days (Sunday needed for maintenace) we can easily calculate the excess days each supplier has by subtracting “Days Needed” from 6 days. We see supplier A has 1.19 “free” days each week and B has only half a day.
We can convert this “Excess Days” to “Excess Parts” easily. We know how many parts each supplier makes each day. Multiply that by Excess Days and we see Supplier A could make 620 extra parts if he had to. B can only make 459 extra parts.
But now the customer scraps a week of parts (one shipment). They may not all even be bad. But this is a key point – if the customer quarantines them, even if only 2% are bad, the entire lot may be quarantined for checking. This could take time. They are effectively out of play, bad or not, and the customer will go down.
We know the Excess Parts per week (supplier still has to make normal shipments, he can only air his excess parts). We can divide these Excess Parts into the quantity removed to determine how many weeks it will take to replace the quarantined parts.
But take a look at the percentage... It takes 35% more time for supplier B to catch up even though he runs only 13.5% slower (5.45/4.03 = 1.352). This is because the slow speed is a double hit – it takes longer to cover production, so the available time to make catch up parts is shorter. If you could reduce your air freight bill by 35%, would you?

32. 5 Day Work Week – Key Points
Obviously, the number of parts quarantined affect how costly air freighting the replacements is.
In both examples presented, only 1 week of parts was quarantined, but consider …
There are 5 weeks of parts in transit, typically.
What if the failure mode has affected them all and is discovered only when the first shipment arrives?
Even if they are partially bad, what if the sort is difficult and time consuming?
None of the examples have included safety stock. That could exacerbate the problem, because then there are effectively up to 10 weeks between the supplier and customer.
In truth, the air freight/excess part problem doesn’t have to be quality related.
What if the supplier needs to replace or move a piece of equipment and the customer demands an increased safety stock bank to cover the time to move and recertify the piece of equipment? How will this bank be built if capacity is tight?
What if there is an engineering change requiring a bank?
What if the equipment is going to be used for another project and you need to do a PPAP run?
This is why, regardless of how you arrange the days, you are limited to 120 production hours to make APW and 5,640 hours to make APY.