Here is an interesting example. Look at the following OEE data for two sequentia

Question

Here is an interesting example. Look at the following OEE data for two sequential weeks.

Question - OEE is improving. Great job! Or is it? Dig a little deeper and the picture is less clear. Most companies would not want to increase Availability by 5.0% at the expense of decreasing Quality by 4.5%.Provide your answer to this question with an explanation as to why i.e a rationale.

Here is additional data you will need to answer this question, as recorded for the first shift:

Calculate the OEE. (4 points)

Hint: OEE is calculated by multiplying the three OEE factors.

Formula: Availability × Performance × Quality

First Calculate the Availability, then Performance and then Quality

Availability

Availability is the first of the three OEE factors to be calculated. It accounts for when the process is not running (both Unplanned Stops and Planned Stops).
Formula: Run Time / Planned Production Time

Planned Production Time

The OEE calculation begins with Planned Production Time. So first, exclude any Shift time, where there is no intention of running production (typically breaks)

Formula: Shift Length - Breaks

Run Time

The next step is to calculate the amount of time that production was actually running (was not stopped). Remember that Stop Time should include both Unplanned Stops (e.g., Breakdowns) or Planned Stops (e.g., Changeovers). Both provide opportunities for improvement.

Formula: Planned Production Time Stop Time

Performance

Performance is the second of the three OEE factors to be calculated. It accounts for when the process is running slower than its theoretical top speed (both Small Stops and Slow Cycles).

Formula: (Ideal Cycle Time × Total Count) / Run Time

Performance can also be calculated based on Ideal Run Rate. The equivalent Ideal Run Rate in our example is 60 parts per minute.
Formula: (Total Count / Run Time) / Ideal Run Rate

Quality

Quality is the third of the three OEE factors to be calculated. It accounts for manufactured parts that do not meet quality standards.
Formula: Good Count / Total Count

Good Count

If you do not directly track Good Count, it also needs to be calculated.

Formula: Total Count Reject Count

Include your answer and calculations for each of the above parts of the equation.

OEE Factor Week 1 Week 2 OEE 85.1% 85.7% Availability 90.0% 95.0% Performance 95.0% 95.0% Quality 99.5% 95.0%

Explanation / Answer

From the provided data, we see that the OEE for week 1 = 85.1% and OEE for week 2 = 85.7%.

In both the cases, the performance of the equipment has remained constant.

With an increase of 5% in availability (90% to 95%), the quality has decreased by 4.5% (99.5% to 95%)

Essentially, there is a trade-off between losses due to quality vis-a-vis the gains due to increase in availability from one case to other.

Let us try to understand this a little more.

If we analyze the parameters a little more in detail, we see that availability is related to the shop floor machine and internal production parameters while the end customer customer will be more interested in quality of the product. A machine thus available for lesser amount of time in week 1 with lower Availability %, ends up producing less number of widgets compared to that in week 2, but a high quality of the product is maintained at 99.5% in week 1 which is what a customer values in most of the cases. Most of the companies do not want to risk losing customers on quality ground for the sake of better equipment performance and production costs.

So, while the OEE has improved from 85.1% in week 1 to 85.7% in week 2, in first case, the rejects will be very little thus providing a superior customer experience and better retention. Thus the future potential revenue from the customer is intact in case of better quality products. We also see how majority of customers would also be willing to pay a marginally higher price for better quality, thus indicating why most of the companies have a greater focus on quality.

As described, let us try to compute each of the individual components of Availability, Performance and Quality

Availability = Run Time / Planned Production Time

Run Time = Planned Production Time Stop Time

Planned Production Time = Shift Length - Breaks

From the data, Shift Length = 8 hours (480 minutes)

We have break time = Two 15 minute breaks and one 30 minute break = 2 x 15 + 1 x 30 = 60 minutes

Thus Planned Production Time = 480 - 60 = 420

Also, given downtime = 47 minutes. So stop time = 47 mins

Run Time = Planned Production Time Stop Time = 420 - 47 = 373 mins

So, calculating Availability = Run Time / Planned Production Time = 373 / 420 = 0.888095 = 88.81%

Let us now compute the second parameter, Performance:

Performance = (Ideal Cycle Time × Total Count) / Run Time

From data, ideal cycle time = 1 seconds = 1/60 minutes (We converted into same units i.e. minutes as other data)

Total Count as provided in the question = 19,271

We had computed Run time for calculating the Availability.

So, Run time = 373 minutes

Substituting these values in the Performance formula

Performance = (Ideal Cycle Time × Total Count) / Run Time

Performance = (1/60 x 19271) / 373 = 0.86108 = 86.11 %

Let us now compute the third parameter, Quality

Quality = Good Count / Total Count

where Good Count = Total Count Reject Count

From the data, Total Count = 19,271 widgets and Reject Count = 423 widgets

Thus Good Count = 19271 - 423 = 18,848 widgets

Quality = Good Count / Total Count = 18848 / 19271 = 0.97805 = 97.81%

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