Tag: OEE Training

Using TRIZ for Problem Solving – Introduction

Using TRIZ for Problem Solving – Introduction

A famous quote from Albert Einstein, “The problems that exist in the world today cannot be solved by the level of thinking that created them.“, applies to the discussion of problem solving and more so to the topic of TRIZ, The Theory of Inventive Problem Solving, developed by Genrich S. Altshuller.

TRIZ – Theory of Inventive Problem Solving

Genrich S. Altshuller developed TRIZ based on his search for a standard method to solve problems.  At the very basic level, once a problem is identified the objective is to determine whether a similar problem has already existed elsewhere.  If so, study the solution and determine whether it can be incorporated into the current solution being sought.  Taken one step further, consider the possibility that a different perspective of the problem may also present a unique inventive solution.

It does not seem too far fetched that the problem to be solved has occurred elsewhere in a completely different context.  The solution that is found may also be out of the context but the concept may lead to an innovative solution for the current problem at hand where one never before existed.

The application of TRIZ requires an open mind.  We often bring our “tool box” of experience to the table and draw on those tools and our wealth of knowledge to create a solution.  TRIZ is a tool that can be used to create completely new and unique solutions to a given problem.  This doesn’t mean that we need to abandon our current technology and know-how; it simply means that there may be other options where the current know-how and / or technology may not apply or it may be applied in a manner that is quite different than it is today.

Identify the Real Problem to be Solved

Any problem solving method can only be successful if the true root cause is identified.  Once found, a clear and concise problem statement must be formulated to assure that the solution developed and implemented indeed addresses the true root cause.

Searching for Solutions:

Once a problem has been identified, the next question is, “How do we solve it?”  There are a number of techniques that can be used such as brain storming and idea mapping, however, one seldomly used technique is TRIZ:  Theory of Inventive Problem Solving.

Every day we are challenged with a diverse range of problems from machine malfunctions to defective parts.  The very nature of any company’s operations requires an immediate fix to restore operations to “normal”.  Recognizing that a problem exists is not the same as understanding what the problem is and effectively solving the problem requires that we have identified the true root cause and not just the symptoms.

Many tools are readily available to even help us address these concerns or identify where opportunities exist to make improvements.  Unfortunately, these tools seldom provide the solution to the problem.  Too often we are trapped inside the box of current thinking, technologies, standards, methodologies, present knowledge, and even company policy.  Our own levels of thinking and plausible solutions are influenced and limited by our current understanding and knowledge of the problem as well as our own experiences.

The Basis for Using TRIZ to Solve Problems:

Technology

In some cases, product or part designs themselves may be constrained as engineers and designers work to generate a design tailored to a specific, known, technology.  Quality Function Deployment is one strategy that provides a platform to explore alternative design and process approaches before committing to a specific technology or process.

It is worth noting that, although product design is critical, processes and technologies used to manufacture the product itself are often overlooked and seldom are the process constraints and their affects ever considered.  There are many examples where numerous hours are wasted attempting to develop tools using traditional technologies to produce parts that conform to the wishes of engineers and designers.

How do we actually go about solving problems where the technology or the design present constraints that prevent success?  This is the basis for TRIZ:  We have clearly identified the problem to be solved, now we need a solution to resolve it.

Problem Classifications

Although problems may have varying degrees of difficulty, the solutions for them can only fall into one of two overly simplified categories:  Known or Unknown.  While this classification may appear simple on the surface, consider the unknown solution.  Is it truly unknown or is it only unknown to you.
  1. Known:  Surrogate process already proven and only requires adaptation for the current situtation.  The “problem solver” has an awareness or experience related to the solution.
  2. Unknown:  Typically, solutions are often limited by the scope of experience of the person or person(s) attempting to solve the problem.
    1. The problem solver is not aware of the solution’s existence (Personal)
    2. The solution is outside the problem solver’s scope of experience, training, or field of expertise, but may exist within the company (Company)
    3. The solution is not known within the company but is known within the industry (Industry)
    4. A solution can be realized although it does not presently exist (Outside Industry).
    5. Requires an inventive solution that goes beyond improving the existing condition and is not known to exist anywhere.
  3. Although a solution may be found or developed internally, it may not necessarily be ideal.  We recommend continual review of trade journals, going to trade shows, and networking not only with industry peers but outside your areas of expertise as well.

We will pursue the TRIZ methodology as both a learning and problem solving method.  Often times the solution to a problem requires a different perspective to achieve an effective resolution.

Applying TRIZ in the real world:

TRIZ can be used to develop solutions in a wide range of applications.  As Contingency Plans are developed, you may determine that a solution is required to address a problem or crisis that company has not yet experienced.  As we have discussed, the information or solution to the pending “crisis” may already exist elsewhere.  Similarly, improvements to Overall Equipment Efficiency may require solutions to be developed to address problems or opportunities that are inhibiting continued improvement. 

We will continue to pursue the application of TRIZ in the real world and present a more detailed case study.  

Note:  We would also recommend and encourage you to visit http://www.mazur.net/triz/ for an indepth presentation and detailed discussion of TRIZ.  This site provides greater detail and background that is presently beyond the application or scope of this series.

Until Next Time – STAY Lean!

 

 

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Going DEEP with OEE

Does anyone actually look at their daily equipment availability? Instead of using TEEP that is typically based on calendarized availability, looking at the Daily Equipment Effectiveness Performance of your operation may provide some interesting insights.

Working overtime due to material or equipment availability occurs many times.  Unfortunately, we find that sometimes these very same machines are idle during the week.

A detailed explanation for calculating DEEP can be found in one of our earlier posts, “OEE, Downtime, and TEEP.”  Understanding machine utilization patterns may provide greater insight into the actual versus planned operating pattern of your process.

Just something to invoke some thoughts for your operation and to perhaps identify another opportunity to improve performance.

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.

Please forward your questions, comments, or suggestions to LeanExecution@gmail.com.  To request our services for a specific project, please send your inquiries to Vergence.Consulting@gmail.com.

We welcome your feedback and thank you for visiting.

Until Next Time – STAY Lean!

OEE in the Automotive Industry

The automotive industry appears to be rebounding at a faster rate than most (if not all) experts may have anticipated.  Many OEM’s and their suppliers are attempting to boost production to replenish inventories and support renewed demand for their products.  Reduced inventories throughout the supply chain are creating demand that is difficult to replenish at the rate required.  Short runs to bootstrap the “pipeline” are taking their toll on OEE rates but also provide the opportunity to identify new improvement initiatives.

General Motors and Toyota have both announced that increased demand for their product is anticipated for the next few months.  The increases are exciting for all involved, however, the ramp up to recovery may be more painful to achieve for some.  How is your company performing?  Those with fixed “cells” or processes may not be experiencing the same degree of frustration as those having flexible processes running multiple part numbers.

Overall Equipment Effectiveness (OEE) typically suffers during these times due to the frequent changeovers and short volume runs.  If there was a time when you can’t change over or setup and run fast enough, this may be it.  Hang on and enjoy the ride.

Until Next Time – STAY lean!

OEE For Manufacturing

We are often asked what companies (or types of companies) are using OEE as part of their daily operations.  While our focus has been primarily in the automotive industry, we are highly encouraged by the level of integration deployed in the Semiconductor Industry.  We have found an excellent article that describes how OEE among other metrics is being used to sustain and improve performance in the semiconductor industry.

Somehow it is not surprising to learn the semiconductor industry has established a high level of OEE integration in their operations.  Perhaps this is the reason why electronics continue to improve at such a rapid pace in both technology and price.

To get a better understanding of how the semiconductor industry has integrated OEE and other related metrics into their operational strategy, click here.

The article clearly presents a concise hierarchy of metrics (including OEE) typically used in operations and includes their interactions and dependencies.  The semiconductor industry serves as a great benchmark for OEE integration and how it is used as powerful tool to improve operations.

While we have reviewed some articles that describe OEE as an over rated metric, we believe that the proof of wisdom is in the result.  The semiconductor industry is exemplary in this regard.  It is clear that electronics industry “gets it”.

As we have mentioned in many of our previous posts, OEE should not be an isolated metric.  While it can be assessed and reviewed independently, it is important to understand the effect on the system and organization as a whole.

We appreciate your feedback.  Please feel free to leave us a comment or send us an e-mail with your suggestions to leanexecution@gmail.com

Until Next Time – STAY lean!

OEE for Batch Processes

Coke being pushed into a quenching car, Hanna ...
Image via Wikipedia

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

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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OEE Integration: Can you fix it?

As we are all aware, inspecting or measuring parts does not change the quality of the product.   Likewise, measuring and reporting OEE alone does not solve problems or improve performance.  While it is fair to say that increased focus and measurement of any process usually results in some degree of improvement, these are typically attributed to changes in human behavior due to observation and not necessarily real process improvements.

Using OEE to identify opportunities in your operation is the equivalent of turning the light on in a dark room.  Although the room hasn’t changed, we certainly have a better understanding of what it looks like.  As such, OEE is a vantage point metric that can be used to illuminate our understanding of the process and identify opportunities to drive improvements.

It is essential for your team to develop and utilize effective problem solving skills to successfully identify systemic and process root causes for failure and to develop and execute permanent corrective actions to resolve them.  Our experience suggests that the lack of solid and proven problem solving skills coupled with poor execution is the leading cause of failure for new initiatives such as OEE.

We introduced an approach to improving OEE in our “Improve OEE:  A Hands On Approach“, post (03-Jan-09).  Although we identified some of the tools that could be used to solve of the problems, we didn’t spend much time going into the details.  Over the next few posts, we’ll discuss some of the ideas in a little more detail.

The real problem for most companies is identifying what the real underlying root cause of the current “failure” mode is.  Without a good understanding of the root cause, the solutions developed and implemented will not be effective, only serving to temporarily cure the immediate superficial symptoms.

Using effective problem solving skills to analyze the OEE data and to develop and execute permanent corrective actions will assure sustainable and ever improving performance.

Until Next Time – STAY lean!