Tag: OEE Calculations

OEE for Batch Processes

Coke being pushed into a quenching car, Hanna ...
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We recently received an e-mail regarding OEE calculations for batch processes and more specifically the effect on down stream equipment that is directly dependent (perhaps integrated) on the batch process.  While the inquiry was specifically related to the printing industry, batch processing is found throughout manufacturing. Our more recent experiences pertain to heat treating operations where parts are loaded into a stationary fixed-load oven as opposed to a continuous belt process.

Batch processing will inherently cause directly integrated downstream equipment (such as cooling, quenching, or coating processes) to be idle. In many cases it doesn’t make sense to measure the OEE of each co-dependent piece of equipment that are part of the same line or process. Unless there is a strong case otherwise, it may be better to de-integrate or de-couple subsequent downstream processes.

Batch processing presents a myriad of challenges for line balancing, batch sizes, and capacity management in general.  We presented two articles in April 2009 that addressed the topic of  where OEE should be measured.  Click here for Part I or Click  here for Part II.

Scheduling Concerns – Theory of Constraints

Ideally, we want to measure OEE at the bottleneck operation.  When we apply the Theory of Constraints to our production process, we can assure that the flow of material is optimized through the whole system.  The key of course is to make sure that we have correctly identified the bottleneck operation.  In many cases this is the batch process.

While we are often challenged to balance our production operations, the real goal is to create a schedule that can be driven by demand.  Rather than build excess inventories of parts that aren’t required, we want to be able to synchronize our operations to produce on demand and as required to keep the bottleneck operation running.  Build only what is necessary:  the right part, the right quantity, at the right time.

Through my own experience, I have realized the greatest successes using the Theory of Constraints to establish our material flows and production scheduling strategy for batch processes.  Although an in-depth discussion is beyond the scope of this article, I highly recommend reading the following books that convey the concepts and application through a well written and uniquely entertaining style:

  1. In his book “The Goal“, Dr. Eliyahu A. Goldratt presents a unique story of a troubled plant and the steps they took to turn the operation around.
  2. Another book titled “Velocity“, from the AGI-Goldratt Institute and Jeff Cox also demonstrates how the Theory of Constraints and Lean Six Sigma can work together to bring operations to all new level of performance, efficiency, and effectiveness.

I am fond of the “fable” based story line presented by these books as it is allows you to create an image of the operation in your own mind while maintaining an objective view.  The analogies and references used in these books also serve as excellent instruction aids that can be used when teaching your own teams how the Theory of Constraints work.  We can quickly realize that the companies presented in either of the above books are not much different from our own.  As such, we are quickly pulled into the story to see what happens and how the journey unfolds as the story unfolds.

Please leave your comments regarding this or other topics.  We appreciate your feedback.  Also, remember to get your free OEE spreadsheets.  See our free downloads page or click on the file you want from the “Orange” box file on the sidebar.

Until Next Time – STAY lean!

Vergence AnalyticsVergence Analytics
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OEE: Frequently Asked Questions

We added a new page to our site to address some of the more frequently asked questions (FAQ’s) we receive regarding OEE.  We trust you will find this information to be of interest as you move forward on your lean journey.  We always appreciate your feedback, so feel free to leave us a comment or send an e-mail directly to LeanExecution@gmail.com or Vergence.Consulting@gmail.com

We have had an incredibly busy summer as more companies are pursuing lean manufacturing practices to improve their performance.  OEE has certainly been one of the core topics of discussion.  We have found that more companies are placing a significant emphasis on Actual versus Planned performance.  It would seem that we are finally starting to realize that we can introduce a system of accountability that leads to improvements rather than reprimands.

Keep Your Data CLEAN

One of the debates we recently encountered was quantity versus time driven performance data when looking at OEE data.  The argument was made that employees can relate more readily to quantities than time.  We would challenge this as a matter of training and the terminology used by operations personnel when discussing performance.  We recommend using and maintaining a time based calculation for all OEE calculations.  Employees are more than aware of the value of their time and will make every effort to make sure that they get paid for their time served.

Why are we so sure of this?  Most direct labour personnel are paid an hourly rate.  Make one error on their pay or forget to pay their overtime and they will be standing in line at your office wondering why they didn’t get paid for the TIME they worked.  They will tell you – to the penny – what their pay should have been.  If you are paying a piece rate per part, you can be sure that the employees have already established how many parts per hour they need to produce to achieve their target hourly earnings.

As another point of interest and to maintain consistency throughout the company, be reminded that finance departments establish hourly Labour and Overhead rates to the job functions and machines respectively.  Quite frankly, the quantity of parts produced versus plan doesn’t really translate into money earned or lost.  However, one hour of lost labour and everyone can do the math – to the penny.

When your discussing performance – remember, time is the key.  We have worked in some shops where a machine is scheduled to run 25,000 parts per day while another runs a low volume product or sits idle 2 of the 5 days of the the week.  When it comes right down to the crunch for operations – how many hours did you earn and how many hours did you actually work.

Even after all this discussion we decided it may be an interesting exercise to demonstrate the differences between a model based on time versus one based (seemingly) only on Quantitative data.  We’ll create the spreadsheet and make it available to you when its done!

Remember to take advantage of our free spreadsheet templates.  Simply click on the free files in the sidebar or visit our free downloads page.

We trust you’re enjoying your summer.

Until Next Time – STAY Lean!

Vergence Business Associates

How OEE can improve your Inventory

Once you have established a robust OEE system, you should also be reaping benefits in other areas of your organization.

We will be offering some insights into the other performance metrics such as inventory over the next few weeks. Improved availability, performance, and quality will all have an impact on your inventory and materials management processes. Inventory turns is one metric that should be improving as your OEE improves. If not, perhaps there is an opportunity to integrate OEE even deeper into your organization.

In a truly lean organization, other vantage point metrics will provide evidence of a well integrated OEE system. Metrics such as delivery, quality (ppm), labour efficiency, lead time, mean time between failures, mean response times, down time, turn over, and financial performance indicators are all directly or indirectly affected by improvements to your operation and OEE.

We will discuss the impact of OEE on these “other” metrics over the next few posts. Remember, we also offer excel templates at no cost to you. Click on the “BOX” files on the sidebar to get your free templates today! Our templates offer more than a simple OEE calculator – they can be used immediately with little or no modifications to suit your processes.

Until next time, STAY lean!

Vergence – Lean Execution Team.

How to Calculate the Quality Factor for OEE

How to correctly calculate the Quality Factor for OEE

Most people assume that the quality factor for Overall Equipment Effectiveness (OEE) is determined by simply calculating the yield of good parts from the total parts produced.  Unfortunately, this logic does not hold true when calculating the quality factor beyond the individual part or process.

We will show you how to correctly calculate the Quality factor and determine a truly weighted result that is consistent with the definition of Overall Equipment Effectiveness.  Although OEE itself does not have a unit of measure, it is based on the effective use of time.

The Quality Factor Defined

Although OEE itself is expressed as a percentage, all of the individual OEE factors are based on time.  Yes, even the quality factor:

The quality factor measures the percentage of time that was used to make or manufacture an acceptable quality product at rate or standard.

We have witnessed too many organizations that attempt to immediately convert the Quality Factor into a Cost of Non-Quality, Parts / Million (PPM), or other type of metric.  This is not the intent of the quality factor from an overall equipment effectiveness perspective.  Again, OEE measures effective use of time.

While it is not our intent to delve into a cost of non-quality discussion, we agree that understanding the cost drivers is in the best interests of the company to minimize losses.  This includes any investment that must be made to improve OEE.

We would also encourage you to download a copy of our Excel spreadsheets (see the BOX file on the sidebar).  There are no charges or fees for downloading these files and we request that these products remain available as such.  Now, let’s move on to the Quality Factor.

Free Download ->>> Click here to download a copy of the example developed in this post! <<<-Free Download

Where did the time go?

By definition, OEE is used to determine how effectively the time for a given machine, process, or resource is used: 

  • Availability:  Planned (Scheduled) versus Unplanned downtime
  • Performance:  Standard versus Actual cycle time
  • Quality:  Value Added versus Non-Value Added time

All of the OEE factors pertain to time.  From our definition above, the factors are independent of people (labour) required, parts produced, defective product, or the value of these items.  However, when we review many OEE templates, and more specifically the quality factor calculation, the time element is lost.

The true Quality Factor formula

The simple yield calculation works for a single process or part number but not for multiple machines or part numbers.  A simple example will demonstrate the correct way to calculate the Quality factor for a single part.  We will expand on this simple example as we go along.  Click here to download your free copy of the spreadsheet used in this post.

Note:  We are using the standard rate for the Quality time calculations as the Availability and Performance factors already account for downtime and cycle time losses respectively.  Quality is based on the pure standard rate or cycle time only.

EXAMPLE:  Machine A – Production Summary

Part Number

Rate / Minute

Total Produced

Defective

Quantity

Yield %
Quantity

1

2

800

10

98.75%

Totals

——-

800

10

98.75%

Averages

2

800

10

98.75%

As we can see from the table above, machine A produces part number 1 at a standard rate of 2 parts / minute.  A total of 800 parts are produced of which 10 are defective and scrapped.  The simple yield formula will correctly calculate the Quality factor as:

Quality Yield = (800 – 10) / 800 = 790 / 800 = 98.75%

From an OEE perspective, however, our interest is not how many parts were scrapped, but rather, how much machine or process time did we lose by making them.  From our example, 10 defective parts results in a loss of 5 minutes: 

Lost Time = 10 parts / (2 parts / minute) = 5 minutes

The quality factor actually tells us how effectively the time was used to make good or acceptable parts.  From our example, the time required to make ALL parts at the standard rate is 400 minutes (800 parts / 2 parts / minute = 400).  Our Quality factor can easily be calculated as follows: 

  • Value Added Time = Total Time – Non-Value Added Time
  • = 400 – 5
  • = 395 minutes

Total Time (All Parts) = 400 minutes

Quality Factor = Value Added Time / Total Time
                               = 395 / 400
                               = 98.75%

Although the results in this case are the same, the method is uniquely different.  Since this is based on a single machine, the cycle times are cancelled in the formula as shown below:

= (800 – 10) / 2 parts per minute / (800 / 2 parts per minute)

The YIELD pitfall revealed:

Our calculation method becomes relevant when we start looking at the production of different parts running through the same machine or process.  The easiest way to demonstrate this is by extending our first example.

Let’s assume we are also using machine A to produce two additional part numbers.  The production data is summarized in the table below as follows:

EXAMPLE:  Machine A – Production Summary

Part Number

Rate / Minute

Total Produced

Defective

Quantity

Yield %
Quantity

1

2

800

10

98.75%

2

8

1600

160

90.00%

3

1

800

20

97.50%

Totals

——-

3200

190

94.06%

Averages

4

1067

63

95.42%

If we calculate the Quality factor for machine A, the simple yield formula will provide a misleading result.  Note that we’ve provided the process yield factor for each line item part number as we have already determined that the ime factors cancel for individual parts.

The average Yield % from the table above is 95.42%.  We will demonstrate that this result is also incorrect.  Remember, we’re interested in the percent of total time used to make a quality product (also known as Value Added Time).

The real question is, “What is the overall Quality factor for machine A?”  The simple yield formula would suggest the following:

Simple Yield Quality Factor = (3200 – 190) / 3200 = 3010/ 3200 = 94.06%

This percentage is misleading and – as we will demonstrate – the WRONG result.

Calculating the True Weighted Quality Factor

Let’s take the table from above and expand on it to reflect our TIME based calculations.  We will calculate the time required to produce all parts (Total Time) and the time lost to produce defective parts (Lost Time).  Remember, these times are calculated at the standard cycle time or rate.  The resulting table appears below:

EXAMPLE:  Machine A – Production Summary

Part Number

Rate / Minute

Total Produced

Total Time

Defective

Quantity

Lost Time

Yield %
Time

1

2

800

400

10

5

98.75%

2

8

1600

200

160

20

90.00%

3

1

800

800

20

20

97.50%

Totals

——-

3200

1400

190

45

96.79%

Averages

4

1067

467

63

15

95.42%

 From this table, we can quickly calculate the true weighted quality factor as follows:

           Quality Factor = Value Added Time / Total Time
                               = (1400 – 45) / 1400
                               = 1355 / 1400
                               = 96.79 %

Putting it ALL together

From the discussion above, we have combined the results into the table below:

EXAMPLE:  Machine A – Production Summary

Part Number

Rate / Minute

Total Produced

Total

Time

Defective

Quantity

Lost Time

Yield %
Quantity

Yield %
Time

Delta

1

2

800

400

10

5

98.75%

98.75%

0.00%

2

8

1600

200

160

20

90.00%

90.00%

0.00%

3

1

800

800

20

20

97.50%

97.50%

0.00%

Totals

——-

3200

1400

190

45

94.06%

96.79%

2.72%

Averages

4

1067

467

63

15

95.42%

95.42%

0.00%

The true weighted quality factor can be found in the Yield % Time column (96.79%).  This result fits the true definition of Overall Equipment Effectiveness. 

The table also shows that the differences between the methods can lead to a significant variance between the results (96.79% – 94.06% = 2.72%): 

  • = 94.06% (Simple)
  • = 95.42% (Average)
  • = 96.79 % (Weighted)

We can quickly prove which answer is correct quite easily.  Referring to the table below, the only factor that resulted in the correct time calculations is the Yield Time % factor (96.79%).  The table shows that the true Value Added Time or Earned Time is 1355 minutes and the total time lost due to defective parts is 45 minutes.  Exactly what we expected to find based on our earlier calculations.

Quality Factor – Validation Table – All Times are in minutes

Method

“Yield %”

Total Time

Earned

Lost Time

Delta Time

Yield Quantity %

94.06%

1400

1316.9

83.1

38.1

Average Yield %

95.42%

1400

1335.8

64.2

19.2

Yield Time %

96.79%

1400

1355.0

45.0

0.0

What does all this mean in terms of time?  The results shown in this table clearly demonstrate that a seemingly small delta of 2.72% between the different methods of calculating the Quality Factor can be significant in terms of time.  The Delta time indicated in the table is the difference between the calculated lost time for Method and the actually calculated lost time of 45 minutes.

If this machine was actually scheduled to run 450 minutes per shift on 2 shifts the results would be even more dramatic over the course of a year.  Assuming the machine is loaded with the same part mix and there are 240 working days per year:

Annual Working Time = 240 * 450 * 2 = 216,000 minutes

The following table summarizes the results on an annualized basis: 

Quality Factor – Annualized Results – All Times are in minutes

Method

“Yield %”

Total Time

Earned

Lost Time

Delta Time

Yield Quantity %

94.06%

216,000

203,169.6

12,830.4

5896.8

Average Yield %

95.42%

216,000

206,107.2

9892.8

2959.2

Yield Time %

96.79%

216,000

209,066.4

6933.6

0.0

The “Yield Quantity %” method indicates the actual lost time that could be incurred annually is 12830.4 minutes (28.51 shifts).  Relative to our “Yield Time %” method, this is overstated by 5896.8 minutes, the equivalent of just over 13 shifts.  Similarly, the “Average Yield %” method indicates a total lost time of 9892.8 minutes (21.98 shifts).  Relative to our “Yield Time %” method, this is overstated by 2959.2 minutes or approximately 6.6 shifts.  This further exemplifies the need to understand the correct way to calculate the Quality Factor.

Let’s continue to re-affirm the validity of our calculation method.

Individually Weighted Quality Factors

We will now show you how to calculate the individually weighted quality factors for each part number or line item.  The weighted “time based” quality factor is calculated using the following formula for each line item part number: 

Weighted Line Item = (Value Added Time)
Total Time for All Parts

Where, Value Added Time = Total Time – Lost Time

 We have simplified the table from our example to show the time related factors only.  The table showing the time weighted quality factors from our example is as follows:

Part Number

Rate / Minute

Total Produced

Total Time

Defective

Quantity

Lost Time

Yield %
Time

Weighted % Yield Time

1

2

800

400

10

5

98.75%

28.21%

2

8

1600

200

160

20

90.00%

12.86%

3

1

800

800

20

20

97.50%

55.71%

Totals

 

3200

1400

190

45

96.79%

96.79%

Averages

4

1067

467

63

15

95.42%

 

As we can see from the table, the sum of the “Weighted % Yield Time” percentages is the same as the “Yield % Time”.  The time based formula is once again validated.  We will now take this table one step further to reveal where the real opportunities are to improve the Quality Factor and Overall Equipment Effectiveness.

Improving the Quality Factor

The Yield % or the Weighted Time % do not provide any real indication of the contribution of each part number to the overall weighted quality factor.  We can see from the table that part numbers 2 and 3 both resulted in 20 minutes of lost time compared to part number 1 where only 5 minutes were lost.

Since part numbers 2 and 3 resulted in an equivalent loss of time, we would expect that they would also result in an equal contribution to improve the Quality Factor.  To demonstrate this and to appreciate the real improvement opportunity, we added two more columns to our table as shown below – “Weighted % Process Time” and “Yield % Opportunity”:

Machine A – Weighted Quality Factor – EXAMPLE  

Part Number

Total Time

Weighted

% Process Time

Lost Time

Value Added Time

Yield %
Time

Weighted % Yield Time

Yield % Opportunity

1

400

28.57%

5

395

98.75%

28.21%

0.36%

2

200

14.29%

20

180

90.00%

12.86%

1.43%

3

800

57.14%

20

780

97.50%

55.71%

1.43%

Totals

1400

100.00%

45

1355

96.79%

96.79%

3.21%

Averages

467

33.33%

15

452

95.42%

32.26%

1.07%

The weighted process time was calculated by dividing the process time for each part number by the Total Time.  Once again, we can validate our weighted Quality Time by multiplying the “Weighted % Process Time” by the “Yield %” for each line item. 

To make sure we understand the calculations involved, let’s work out one of the line items in the table.  For Part Number 1, 

  • Weighted % Process Time = 400 / 1400 = 28.57%
  • (1)  Weighted % Yield Time = 28.57% * 98.75% = 28.21%
  • (2)  Weighted % Yield Time = (400 – 5) / 1400 = 28.21 %

Note that we showed two ways to demonstrate the Weighted % Yield Time to once again validate the quality factor calculation method.

The opportunity to improve the OEE for the three part numbers is the difference between the Weighted Process Time and the Weighted Yield Time.  For Part Number 1,

            Improvement = 28.57% – 28.21% = 0.36%

Similarly, the improvements for part numbers 2 and 3 are as follows: 

  • Improvement Part Number 2 = 14.29% – 12.86% = 1.43%
  • Improvement Part Number 3 = 57.14% – 55.71% = 1.43%

Three Key Observations

  1. First, the results of the calculations are consistent the actual observed down time.
  2. Second, although the yields for part numbers 2 and 3 are significantly different, each has the same NET impact to the final OEE result.
  3. Third, when add the total “Yield % Opportunity” (3.21%) for all three part numbers to the total “Weighted % Yield Time” (96.79%), the result is 100%.

This last calculation once again demonstrates that the Quality Factor calculation presented here is consistent with the true definition of OEE.

The formula for the Quality Factor is:

Total Time to Produce Good Parts @ Rate / Total Time to Produce ALL Parts @ Rate

One Final Proof

Our method will produce a result that is consistent with the formula OEE = A * P * Q.  Using our example, it is clear that if Availability and Performance are both 100% and the Quality Factor is 96.79%, the final OEE for all parts will also be 96.79%.

Consistent with the definition of OEE, using our example, 96.79% of 1400 minutes is 1355 minutes.  This is the time that was used to make good or acceptable quality parts.  Similarly then, the time lost making all defective parts is 45 minutes (1400 – 1355 = 45).

The Impact to Operations

OEE is typically used by the Operations team for capacity planning, labour planning, and to determine how much time to schedule for a given resource to produce parts.  The above examples clearly demonstrate that even a small delta can have significant capacity, labour, and scheduling implications.  From this perspective it also becomes a relatively simple task to determine the direct labour costs associated with the production of defective parts.

Purchasing, Materials, Scheduling (Lead Times), Inventory (Stock), Finance, and Quality are all affected by inaccurate data and, in this case, OEE calculation errors.  Of course these errors are not just limited to the Quality Factor itself.

There are other significant losses and costs related to quality as well.  It is not our intent to pursue a discussion on the cost of non-quality as we recognize there are many other factors (internal and external) that must be considered to truly understand the real cost of non-quality for activities such as sorting, inspection, scrap (material losses), rework, re-order, machine time, and administration.

In the real world, someone may just be preparing a plan to improve the Quality of parts running on Machine A to reduce excessive labour and material costs.  We can only wonder what method they used to calculate the “savings”.  Inevitably, many companies approve the project and the funding only to realize the savings fell well short of expectations or will never materialize at all.

In Closing

We would contend that the differences in the calculation method presented here and those found elsewhere are significant.  In our example case, the difference is 2.72%.  We demonstrated that this can be significant when annualized over time.  Similarly, the opportunity for improvements using our method is clear and concise.

Now when someone asks you how to calculate the Quality Factor, you can confidently show them how and tell them why.

The example used in this post can also be downloaded from our BOX File on the sidebar or CLICK HERE.  This is offered at no charge and of course will make it easier for you to use for your own applications.

Thank you for visiting – Until Next Time – STAY lean!

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OEE and the Quality Factor

Many articles written on OEE (ours being the exception), indicate or suggest that the quality factor for OEE is calculated as a simple percentage of good parts from the total of all parts produced.  While this calculation may work for a single line part number, it certainly doesn’t hold true when attempting to calculate OEE for multiple parts or machines.

OEE is a measure of how effectively the scheduled equipment time  is used to produce a quality product.  Over the next few days we will introduce a method that will correctly calculate the quality factor that satisfies the true definition of OEE.  The examples we have prepared are developed in detail so you will be able to perform the calculations correctly and with confidence.

Every time a part is produced, machine time is consumed.  This time is the same for both good and defective parts.  To correctly calculate the quality factor requires us to start thinking of parts in terms of time – not quantity.

If the cycle time to produce a part is 60 seconds, then one defective part results in a loss of 60 seconds.  If 10 out of 100 parts produced are defective then 600 seconds are lost of the total 6000 seconds required to produce all parts.  Stated in terms of the quality factor, 5400 seconds were “earned” to make quality parts of the total 6000 seconds required to produce all parts (5400/6000 = 90%).  Earned time is also referred to as Value Added Time.

As we stated earlier, for a single line item or product, the simple yield formula would give us the same result from a percentage perspective (90 good / 100 total = 90%).  But what is the affect when the cycle times of a group or family of parts are varied?  The yield formula simply doesn’t work.

The quality factor for OEE is only concerned with the time earned through the production of quality parts.  Watch for our post over the next few days and we’ll clear up the seemingly overlooked “how to” of calculating the quality factor.

Until Next Time – STAY lean!

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SPC for OEE

Some of our readers have expressed an interest in the application of Statistical Analysis Tools for OEE.  We have also reviewed various texts and articles that have expressed opposing views on the application of Statistical Tools with OEE data.

Our simple answer is this:

  •  At best, statistical tools should only be used on unique or individual processes.   Comparisons may be made (with caution) between “like” processes, however, even these types of comparisons require a thorough, in-depth, understanding of product mix, customer demand, and potentially unique process considerations.
  • At worst, it may be worthwhile to use statistical analysis tools for the individual OEE factors:  Availability, Performance, and Quality.

Negative trends may have Positive returns

While certain top level improvements can be implemented across the company, such as quick die change or other tool change strategies, their very integration may (and should) result in changes to the existing or current operating strategy.  A quick tool change strategy may provide for substantially reduced set up times and, as a result, operations may schedule shorter and more frequent runs.  In turn, the net change to OEE may be negligible or even lower than before the new change strategy was implemented.

A drop or decline in OEE does not necessarily translate to negative financial performance.  From a cash flow perspective, the savings may be realized through reduced raw material purchases, reduced inventories, and subsequently lower carrying costs that may more than offset any potential decrease in OEE.  As we have discussed in previous posts, OEE should not be regarded as a stand alone metric.  It is important to understand the financial impact of each of the OEE factors to your bottom line.  We’re in business to make money and Cash is King.

Scope of Analysis – Keep it Simple

As the scope of the OEE analysis increases from shift, to daily, to weekly, to monthly summaries, the variables that affect the end result are compounded accordingly.  Extending statistical techniques to OEE data across multiple departments or even company wide introduces even more sources of variation that make statistical modeling unrealistic.

While the application of statistics may sound appealing and “neat”, it is even more important to be able to understand the underlying factors that affect or influence the final result in order to implement effective countermeasures or action plans to make improvements or simply to eliminate the source of concern.

Regardless of the final OEE index reported, someone will ask the question, “So, what happened to our OEE?”  Whether an increase or decrease, both will require an answer to explain the variance.  If there was a significant increase, someone will want to know why and where.  This improvement, of course, will have to be replicated on other like machines or processes.  If there was a significant decrease, someone will want to know why and where so the cause of poor or reduced performance can be identified and corrected.

Ironically, no matter what the result, you will have to be prepared to supply individual process OEE data.  So why not just review the primitive data and respond to the results in real time?  While we recommend this practice, there may be certain trends relative to the individual factors that can be statistically evaluated in the broader sense (Quality – PPM, Labour – Efficiencies).

Statistics on a larger scale

We would suggest and recommend using statistical techniques on the individual Availability, Performance, and Quality factors of OEE.  For example, many companies track labour efficiencies relative to performance while others measure defects per million pieces relative to Quality.

While most people readily associate statistics with quality processes, many operations managers are applying statistical analysis techniques to a variety of metrics such as run time performance, performance to schedule, downtime, and setup times.  Maintenance managers are also analysing equipment availability for Mean Time to Repair, Meantime Time Between Failures, and equipment life cycle performance criteria.

As one example, we have conducted and encouraged our clients to consider statistical analysis of production data.  Tracking the standard deviation of daily production over time can reveal some very interesting trends.  These results will also correlate with the OEE factors.  Where the standard deviation is low, the increase in production is reflected in other metrics as well including financial performance.

A final note

Lastly, OEE is a tool that should be used to drive improvements.  As such, the goal or target is forever changing whether in small or large increments.  Another SPC solution for OEE that may be a little easier to understand and execute is as follows:

  1. System – Define and establish an effective system for collecting, analyzing, and reporting OEE data – preferably in real time at the source.
  2. Process – Understand and establish where and how OEE data will actually be collected in your processes and how it will be used to make improvements.
  3. Control– Establish effective methods to control both systems and processes to assure OEE is and remains a truly integrated metric for your operations.

We strongly recommend and support “at the source” thinking strategy.  Quite simply, we prefer points of control that are as close to their source as possible whether it be data, measurement, or product related.  Quality “at the source” (at the machine in real time) is much easier to manage than final inspection on the dock (hours or even days later).  Similarly, OEE managed in real time, at the process or machine, will serve the people and the company with greater control.

We welcome your feedback.  Please leave a comment or send us an e-mail (leanexecution@gmail.com) with your questions, suggestions, or comments.

Until Next Time – STAY lean!

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OEE Calculation Errors

Database Errors

We agree that collecting and tracking OEE data is a task best suited for a database, however, all the bells and whistles of an OEE system don’t serve much purpose if the calculations are wrong.  Before you make a significant investment in your OEE data collection, tracking, and monitoring system, make sure the system you plan to purchase is calculating the OEE results correctly.

The ultimate system is one that supports automated data collection technology to minimize data entry costs, reduces the risk of entry errors, and provides reporting or monitoring of OEE in real time.  These solutions may be purchased “off the shelf” or customized to your specific process application.

Excel Spreadsheets

If a database is the best approach, you may ask why we use Excel spreadsheets to present our examples or why we supply templates to allow you to track and monitor OEE.  We have four primary reasons:

  1. Almost everyone is familiar with spreadsheets and most people have access to them on their computer.
  2. We determined that a customized database solution being used was not calculating the weighted OEE factors correctly and the overall OEE index was also wrong.  We found it necessary to develop a spreadsheet that made it easy to validate the database calculations.
  3. Database enhancements were easier to develop and demonstrate using a spreadsheet.  We encountered a production process that was equipped with automated data collection capability and provided an overwhelming amount of performance data in real time.  It was easier to perform database queries and use the power of PIVOT tables to develop the desired solutions.
  4. Spreadsheet templates allow you to start collecting and analyzing data immediately.  It also allows the users to get a “feel” for the data.  Although the graphs and drill downs offered by databases are based on predetermined rules, humans are still required to make sense of the data.

Recommendations

In summary, validate the software and its capabilities prior to purchase.  We have observed installations where the OEE data is used to monitor current production performance and the reports generated by the system are used to support the results – good or bad.

We have also evaluated a number of other free OEE spreadsheet offerings on the web and observed that some of these also fail to correctly calculate OEE where multiple machines or part numbers are concerned.  Take a look at our free spreadsheets offerings (see the sidebar).  Our tutorial provides an in depth explanation of how to calculate OEE for single and multiple machines or parts.

The purpose of measuring OEE is to ensure sustained performance with the objective to continually improve over time.  Don’t fall into the trap of setting up a system that, once installed, will only be used to generate reports to justify the current results.

Take the time to train your team and demonstrate how the results will be used to improve their processes.  Involve all of your employees from the very beginning, including the system selection process, so they understand the intent and can provide feedback for what may be meaningful to them while, in turn, they can support the company’s goals and objectives.

Reference Posts.

We encourage you to visit our previous posts showing how to calculate OEE for multiple parts and machines.

  1. Single Process – Multiple Shifts:  OEE

  2. Multiple Parts / Processes:  OEE

  3. Practical OEE

  4. Weighted Calculations:  OEE

  5. How to Calculate OEE

  6. Overall Equipment Efficiency

If you have any questions, comments, or wish to suggest a topic for a future post, please forward an e-mail to leanexecution@gmail.com

We appreciate your feedback.

Until next time – STAY Lean!

OEE, Downtime, and TEEP

We have received several inquiries regarding equipment down time – periods of time when the machine is not scheduled to run.  We consider this to be scheduled down time or idle time and does not affect Overall Equipment Effectiveness (OEE), since no production was planned during this period.

OEE measures overall equipment effectiveness during planned production or SCHEDULED up time.  Do not confuse idle time with tooling or material change over as these activities should be part of the scheduled machine time – periods where the machine is not scheduled to run.  After hours or weekends are examples of idle time.

TEEP or Total Equipment Effectiveness Performance is another variable, similar to OEE, and measures the Total Equipment Effectiveness Performance based on calendar time – the total time the equipment is “present”.  If process “A” is in your plant for 24 hours a day, 7 days a week, then the total time required to make good parts is divided by the time the asset, process, or equipment is “present” and is therefore “technically available” for the time frame being considered.  Typically this is based on calendar time – 24 hours per day and 7 days per week.

Another way to view TEEP is to consider it as a measure of how effectively the total capacity of a process or machine is being utilized to make GOOD parts.  In short, TEEP could be defined as a measure of Equipment Capacity Utilization Effectiveness.

TEEP Calculation Example:

In the metal stamping business, raw coil steel is processed through a die that runs in a stamping press to manufacture the parts.  The ideal cycle time for may be 30 strokes (or parts) per minute.  While the press may be scheduled to run for 16 hours, it is technically “present” or available 24 hours.  If, in a given day, a total of 18,000 GOOD parts were produced over 16 hours of scheduled production time, the OEE is easily calculated.

We will first calculate the IDEAL hours required to produce 18,000 parts at 30 spm.  The IDEAL rate per hour is 1,800 parts (30 spm * 60 minutes  / hour).  Therefore the IDEAL time to produce 18,000 good parts is 10 hours (18,000 parts / 1,800 per hour).

If this is a two shift operation, the net available time is 16 hours (scheduled) and the OEE for the day is calculated as 10 / 16 = 62.5 %.

Since the press is always present, 24 hours per day – 7 days per week, the Daily Equipment Effectiveness Performance (DEEP) in this case is 10 / 24 = 41.7 %.  While this example only represents a single 24 hour day, the basis for calculation is the same.  If the time frame is one week, one month, one quarter, the Total Equipment Effectiveness Performance for that time frame is calculated using the following formula:

TEEP = Total IDEAL Time to Produce Good Parts / Total Gross Time Available

FREE Downloads

We are currently offering our Excel OEE Spreadsheet Templates and example files at no charge.  You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS” or click on the FREE Downloads Page.  These files can be used as is and can be easily modified to suit many different manufacturing processes.  There are no hidden files, formulas, or macros and no obligations for the services provided here.

Feel free to leave any comments or send your questions to LeanExecution@gmail.com

Until next time – STAY Lean!

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