In our previous post (Calculating OEE – A Simple Example, Part 1) we saw how to calculate OEE using only the Good Parts count and the Planned Operating Time. Now we are going to calculate OEE for the same example using the Availability, Performance and Quality Losses. This is a more complex way of calculating OEE but it provides us with the data to identify the main reasons for loss.
Remember our example:
Over a 12hour shift, our Filling machine fills 11,000 bottles. The manufacturer has specified that the Standard Time for this Filler to fill one bottle is .05mins/bottle. Over the course of the shift there are some Planned Downtimes: 2 x 15minute tea breaks and 1 x 30minute lunch break.
Planned Operating Time
15mins | Morning Tea Break
30mins | Lunch Break
15mins | Afternoon Tea Break
Planned Downtime: 60mins
Total Time: 720mins
Planned Operating Time = 720mins – (15mins + 30mins + 15mins)
Now let's look at Availability. In our previous post we defined Availability as ((Planned Operating Time) – (All Availability Losses)) / (Planned Operating Time)
We know our Planned Operating Time is 11hrs. So if we know our Availability Losses we can calculate Availability. Availability Loss are all downtimes related to Breakdowns and ChangeOvers.
Over the course of our Shift we logged downtimes as follows:
25mins | ChangeOver
10mins | No Caps in Hopper
15mins | No Air
Availability Loss: 50mins
Planned Operating Time: 660mins
Actual Operating Time = 660mins – (25mins + 10mins + 15mins)
Availability = (Actual Operating Time) / (Planned Operating Time)
The Performance Loss is a combination of Short Stops and Speed Loss. Short Stops are momentary downtimes which aren't Breakdowns or ChangeOvers which stop the machine and interrupt production but do not generally require technical support. In general for Manual Systems Short Stops are ignored as it can be onerous to record each stop. This results in the loss associated with Short Stops being built in to the Cycle Time Speed Loss. In Automatic Data Capture Systems it is more realistic to log Short Stops.
Rather than take a Cycle Time measurement and assume that this Cycle Time was constant over the whole shift it is often more realistic to calculate the Net Operating Time by working back from the more easily measured Throughput (ie sum of Good Parts and Defect Parts).
We know that the number of Bottles filled was 11,000
250 | Underfilled
100 | No Cap
Defect Parts: 350
Good Parts: 11,000
Thus we can calculate our Good Time (ie Standard Time to produce Good Parts) and our Defect Time (ie. Standard Time to produce Defect Parts).
Good Time = 11,000 x 0.05
Defect Time = 350 x 0.05
Where 0.05mins/bottle is our Standard Time to produce one bottle.
Net Operating Time = (Good Time) + (Defect Time)
and working back we see that:
Perfomance Loss = (Actual Operating Time) – (Net Operating Time)
We now know the Net Operating Time therefore we can calculate the Performance.
Performance = (Net Operating Time) / (Actual Operating Time)
Just for the sake of completeness let’s say that the machine stopped 10 times due to Falling Caps. Each stop was 6 seconds in duration and didn't require any technical intervention. We can say that the machine suffered 1min of downtime due to Short Stops.
Subtracting the Short Stops from the Performance Loss gives us the Speed Loss (Slow Running). ie the Loss due to the fact that the machine was running at a slower rate than the optimum rate specified by the manufacturer.
Speed Loss = (Performance Loss) – (Short Stops)
As a matter of interest our average Cycle Time over the Shift was ((Net Operating Time) + (Speed Loss)) / ((Good Parts) + (Defect Parts)) = (609) / (11,350) = 0.0537mins/bottle. Thus the Filler took on average 0.0037mins more then the Standard Time to fill each Bottle.
Finally we consider Quality Losses, ie the time taken to produce Defect Parts. In the above section we have already calculated the Good Time and the Defect Time. Defect Time is another name for Quality Loss and Good Time or OEE Time is the same as Fully Productive Time.
Thus we have every thing to calculate Quality.
Quality = (Fully Productive Time) / (Net Operating Time)
Note: Quality is only equal to (Good Parts count) / ((Good Parts count) +(Defect Parts count)) when the Standard Time is the same for all parts run on the machine over the shift.
Now the final calculation;
OEE = Availability x Performance x Quality
= 92.4% x 93.0% x 96.9%
By taking the long way round we have generated 3 additional KPIs and we have a lot more data which we can use to focus in on the causes of loss.
Let's briefly take a look at the Level 1 Losses in a table ordered by size of loss
This tells us that in this example downtimes are the most significant type of loss.
And now look at the individual losses in a similar table
|Speed Loss (Slow Running)||41.5|
|No Caps in Hopper||15.0|
|No Cap (Defect Time)||12.5|
Here we see that Speed Loss is in fact the biggest individual loss. In the absence of a downtime monitoring system Speed Losses are often missed as it can appear that the machine is running perfectly well when in fact it is producing much less then it should.
When generated on a shift by shift basis these tables are helpful in the day to day operational management of the machine. However when calculated over longer periods of time you can build up a very insightful picture as to the real causes of loss – as opposed to your presumptions – which may or may not in fact be correct.
In our next post we will show how Provideam can help to organise the data you have collected manually into a database which can be analysed in many different ways. The beauty of a database over a spreadsheet, like Excel, is that the data can easily be grouped and filtered by all sorts of interesting criteria in a rapid and flexible manner.