Tag: Quality

Lean – A Race Against Time

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Background

If “Time is Money”, is it reasonable for us to consider that “Wasting Time is Wasting Money?”

Whether we are discussing customer service, health care, government services, or manufacturing – waste is often identified as one of the top concerns that must be addressed and ultimately eliminated.  As is often the case in most organizations, the next step is an attempt to define waste.  Although they are not the focus of our discussion, the commonly known “wastes” from a lean perspective are:

  • Over-Production
  • Inventory
  • Correction (Non-Conformance  – Quality)
  • Transportation
  • Motion
  • Over Processing
  • Waiting

Resourcefulness is another form of waste often added to this list and occurs when resources and talent are not utilized to work at their full potential.

Where did the Time go?

As a lean practitioner, I acknowledge these wastes exist but there must have been an underlying element of concern or thinking process that caused this list to be created.  In other words, lists don’t just appear, they are created for a reason.

As I pondered this list, I realized that the greatest single common denominator of each waste is TIME.  Again, from a lean perspective, TIME is the basis for measuring throughput.  As such, our Lean Journey is ultimately founded on our ability to reduce or eliminate the TIME required to produce a part or deliver a service.

As a non-renewable resource, we must learn to value time and use it effectively.  Again, as we review the list above, we can see that lost time is an inherent trait of each waste.  We can also see how this list extends beyond the realm of manufacturing.  TIME is a constant constraint that is indeed a challenge to manage even in our personal lives.

To efficiently do what is not required is NOT effective.

I consider Overall Equipment Effectiveness (OEE) to be a key metric in manufacturing.  While it is possible to consider the three factors Availability, Performance, and Quality separately, in the context of this discussion, we can see that any impediment to throughput can be directly correlated to lost time.

To extend the concept in a more general sense, our objective is to provide our customers with a quality product or service in the shortest amount of time.  Waste is any impediment or roadblock that prevents us from achieving this objective.

Indirect Waste and Effectiveness

Indirect Waste (time) is best explained by way of example.  How many times have we heard, “I don’t understand this – we just finished training everybody!”  It is common for companies to provide training to teach new skills.  Similarly, when a problem occurs, one of the – too often used – corrective actions is “re-trained employee(s).”  Unfortunately, the results are not always what we expect.

Many companies seem content to use class test scores and instructor feedback to determine whether the training was effective while little consideration is given to developing skill competency.  If an employee cannot execute or demonstrate the skill successfully or competently, how effective was the training?  Recognizing that a learning curve may exist, some companies are inclined to dismiss incompetence but only for a limited time.

The company must discern between employee capability and quality of training.  In other words, the company must ensure that the quality of training provided will adequately prepare the employee to successfully perform the required tasks.  Either the training and / or method of delivery are not effective or the employee may simply lack the capability.  Let me qualify this last statement by saying that “playing the piano is not for everyone.”

Training effectiveness can only be measured by an employee’s demonstrated ability to apply their new knowledge or skill.

Time – Friend or Foe?

Lean tools are without doubt very useful and play a significant role in helping to carve out a lean strategy.  However, I am concerned that the tendency of many lean initiatives is to follow a prescribed strategy or formula.  This approach essentially creates a new box that in time will not be much different from the one we are trying to break out of.

An extension of this is the classification of wastes.  As identified here, the true waste is time.  Efforts to reduce or eliminate the time element from any process will undoubtedly result in cost savings.  However, the immediate focus of lean is not on cost reduction alone.

Global sourcing has assured that “TIME” can be purchased at reduced rates from low-cost labour countries.  While this practice may result in a “cost savings”, it does nothing to promote the cause of lean – we have simply outsourced our inefficiencies at reduced prices.  Numerous Canadian and US facilities continue to be closed as workers witness the exodus of jobs to foreign countries due to lower labor and operating costs. Electrolux closes facility in Webster City, Iowa.

I don’t know the origins of multi-tasking, but the very mention of it suggests that someone had “time on their hands.”  So remember, when you’re put on hold, driving to work, stuck in traffic, stopped at a light, sorting parts, waiting in line, sitting in the doctors office, watching commercials, or just looking for lost or misplaced items – your time is running out.

Is time a friend or foe?  I suggest the answer is both, as long as we spend it wisely (spelled effectively).  Be effective, be Lean, and stop wasting time.

Let the race begin:  Ready … Set … Go …

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics
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Killer Metrics

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Managing performance on any scale requires some form of measurement.  These measurements are often summarized into a single result that is commonly referred to as a metric.  Many businesses use tools such as dashboards or scorecards to present a summary or combination of multiple metrics into a single report.

While these reports and charts can be impressive and are capable of presenting an overwhelming amount of data, we must keep in mind what we are measuring and why.  Too many businesses are focused on outcome metrics without realizing that the true opportunity for performance improvement can be found at the process level itself.

The ability to measure and manage performance at the process level against a target condition is the strategy that we use to strive for successful outcomes.  To put it simply, some metrics are too far removed from the process to be effective and as such cannot be translated into actionable terms to make a positive difference.

Overall Equipment Effectiveness or OEE is an excellent example of an outcome metric that expresses how effectively equipment is used over time as percentage.  To demonstrate the difference between outcome and process level metrics, let’s take a deeper look at OEE.  To be clear, OEE is an outcome metric.  At the plant level, OEE represents an aggregate result of how effectively all of the equipment in the plant was used to produce quality parts at rate over the effective operating time.  Breaking OEE down into the individual components of Availability, Performance, and Quality may help to improve our understanding of where improvements can be made, but still does not serve to provide a specific direction or focus.

At the process level, Overall Equipment Effectiveness is a more practical metric and can serve to improve the operation of a specific work cell where a specific part number is being manufactured.  Clearly, it is more meaningful to equate Availability, Performance, and Quality to specific process level measurements.  We can monitor and improve very specific process conditions in real time that have a direct impact on the resulting Overall Equipment Effectiveness.  A process operating below the standard rate or producing non-conforming products or can immediately be rectified to reverse a potentially negative result.

This is not to say that process level metrics supersede outcome metrics.  Rather, we need to understand the role that each of these metrics play in our quest to achieve excellence.  Outcome metrics complement process level metrics and serve to confirm that “We are making a difference.”  Indeed, it is welcome news to learn that process level improvements have translated into plant level improvements.  In fact, as is the case with OEE, the process level and outcome metrics can be synonymous with a well executed implementation strategy.

I recommend using Overall Equipment Effectiveness throughout the organization as both a process level and an outcome level metric.  The raw OEE data at the process level serves as a direct input to the higher level “outcome” metrics (shift, department, plant, company wide).  As such, the results can be directly correlated to specific products and / or processes if necessary to create specific actionable steps.

So, you may be asking, “What are Killer Metrics?”  Hint:  To Measure ALL is to Manage NONE.  Choose your metrics wisely.

Until Next Time – STAY lean!

Vergence Analytics

Benchmarking OEE

Benchmarking Systems:

We have learned that an industry standard or definition for Overall Equipment Effectiveness (OEE) has been adopted by the Semi Conductor Industry and also confirms our approach to calculating and using OEE and other related metrics.

The SEMI standards of interest are as follows:

  • SEMI E10:  Definition and Measurement of Equipment Reliability, Availability, and Maintainability.
  • SEMI E35:  Guide to Calculate Cost of Ownership Metrics.
  • SEMI E58:  Reliability, Availability, and Maintainability Data Collection.
  • SEMI E79:  Definition and Measurement of Equipment Productivity – OEE Metrics.
  • SEMI E116:  Equipment Performance Tracking.
  • SEMI E124:  Definition and Calculation of Overall Factory Efficiency and other Factory-Level Productivity Metrics.

It is important to continually learn and improve our understanding regarding the development and application of metrics used in industry.  It is often said that you can’t believe everything you read (especially – on the internet).  As such, we recommend researching these standards to determine their applicability for your business as well.

Benchmarking Processes:

Best practices and methods used within and outside of your specific industry may bring a fresh perspective into the definition and policies that are already be in place in your organization.  Just as processes are subject to continual improvement, so are the systems that control them.  Although many companies use benchmarking data to establish their own performance metrics, we strongly encourage benchmarking of best practices or methods – this is where the real learning begins.

World Class OEE is typically defined as 85% or better.  Additionally, to achieve this level of “World Class Peformance” the factors for Availability, Performance, and Quality must be at least 90%, 95%, and 99.5% respectively.  While this data may present your team with a challenge, it does little to inspire real action.

Understanding the policies and methods used to measure performance coupled with an awareness of current best practices to achieve the desired levels of  performance will certainly provide a foundation for innovation and improvement.  It is significant to note that today’s most efficient and successful companies have all achieved levels of performance above and beyond their competition by understanding and benchmarking their competitors best practices.  With this data, the same companies went on to develop innovative best practices to outperform them.

A Practical Example

Availablity is typically presented as the greatest opportunity for improvement.  This is even suggested by the “World Class” levels stated above.  Further investigation usually points us to setup / adjustment or change over as one of the primary improvement opportunities.  Many articles and books have been written on Single Minute Exchange of Dies and other Quick Tool Change strategy, so it is not our intent to present them here.  The point here is that industry has identified this specific topic as a significant opportunity and in turn has provided significant documentation and varied approaches to improve setup time.

In the case of improving die changes a variety of techniques are used including:

  • Quick Locator Pins
  • Pre-Staged Tools
  • Rolling Bolsters
  • Sub-Plates
  • Programmable Controllers
  • Standard Pass Heights
  • Standard Shut Heights
  • Quarter Turn Clamps
  • Hydraulic Clamps
  • Magnetic Bolsters
  • Pre-Staged Material
  • Dual Coil De-Reelers
  • Scheduling Sequences
  • Change Over Teams versus Individual Effort
  • Standardized Changeover Procedures

As change over time becomes less of a factor for determining what parts to run and for how long, we can strive reduced inventories and improved preventive maintenance activities.

Today’s Challenge

The manufacturing community has been devastated by the recent economic downturn.  We are challenged to bring out the best of what we have while continuing to strive for process excellence in all facets of our business.

Remember to get your free Excel Templates by visiting our FREE Downloads page.  We appreciate your feedback.  Please leave a comment an email to leanexecution@gmail.com or vergence.consultin@gmail.com

Until Next Time – STAY Lean!

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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OEE and Capacity Management

Capacity – Available or Required?

From a scheduling perspective it is very easy to determine how much capacity (or time) will be required to manufacture a minimum quantity of parts.  However, it is not just a matter of multiplying the Standard Cycle Time by the Quantity of Parts and dividing by the part or process OEE %.

As you may recall, the availability component of OEE also accounts for set up or change over time.  Unfortunately, change over time is not typically dependent on the quantity of parts to be produced.  As such, set up or change over time must be tracked / measured  for each individual process and treated separately.

For example, in the metal stamping industry, a die change may take 20 minutes from the last good part to the first “next” good part out.  The quantity produced is variable depending on the yield of the coil (material thickness versus weight), and the number of coils run.

The duration of the run is subject to the set up time and coil / material change over times.  For this reason, unlike Performance and Quality, Availability is not a constant.  From a scheduling perspective, we can calculate the minimum run time using factor based on (Performance X Quality) and then account for availability by adding the set up and material change over times.

If the scheduled quantity is FIXED,  then  we can likely use the simple equation as originally stated.  For example if a process is scheduled to produce 500 pieces of product A on a machine having a cycle time of 30 seconds and the OEE for the process is 85%, then the time to produce the parts would be calculated as follows:

  • (500 Parts X 30 Seconds) / 85% = 17647.1 seconds

In this example 4.2 hours at standard versus 4.9 hours based on the OEE index.  As we noted above, however, because the quantity of parts is FIXED, the set up time and / or change over time is less concerning.

Repeating this process for all the parts that run through a given machine, it is possible to determine the total capacity required to run production. 

Capacity Available

If you are considering new work for a piece of equipment or machinery, knowing how much capacity is available to run the work will eventually become part of the overall process.  Typically, an annual forecast is used to determine how many hours per year are required.  It is also possible that seasonal influences exist within your machine requirements, so perhaps a quarterly or even monthly capacity report is required.

To calculate the total capacity available, we can use the formula from our earlier example and simply adjust or change the volume accordingly based on the period being considered.  The available capacity is difference between the required capacity and planned operating capacity.

Capacity Considerations and OEE

As we have mentioned in previous posts, be cognizant of the variation that may be present in the data.  A company that has been running and collecting OEE data for several months or even years will certainly be able to scrutinize the integrity of the OEE index and determine it’s statistical relevance.

A PPI (process performance index) that considers both OEE and Throughput Variance will present a more statistically relevant method of approximating capacity utilization.

VARIATION is the top form of WASTE in any business.  Although understanding variance is important, of greater concern is eliminating the source(s) of variance.

Until next time – STAY lean!

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