Tag: Performance

Flawed Execution: Performance Matters – Surface Pro 3

Softpedia recently published an article titled, “New Intel Drivers Boost Surface Pro 3 Performance by 30 Percent“.  According to the article:

Intel says that the new drivers released for the HD 4400/5000 graphics used on the Core M processor range can improve performance by up to 30 percent, which means that the Surface Pro 3 can now reduce loading times and make the device faster overall.

Our Surface Pro 3, i7, is already a high performance machine, however, a free boost in performance from Intel would make it even more so.  The “updates” can be downloaded as zip files for both 32 and 64 bit machines.  Before you do … read on.

After downloading, extracting the files, and attempting to install we were greeted with the following unexpected message:

Intel Driver Update

We clicked “Yes” and read the information provided on the Intel support website.  After realizing that this could get more complicated than we anticipated, we decided to leave things as they are.

This little exercise certainly demonstrates what “Flawed Execution” looks like:  Excellent Intentions, Rough Plan, Execution Failure.  Our Surface Pro 3 configuration is factory set, “as is” out of the box.  Did someone actually succeed with the installation?  We can’t be sure.  The only comment posted under the article suggests that at least one other person tried and also failed.

Certainly other articles may have been published to announce the same performance improvements.  For now, it looks like we have a little more research to do before pursuing this potentially significant performance enhancement.

Update:

Even comments in the “Related Articles” suggest varying degrees of success.  Waiting for Microsoft to incorporate this improvement in a future update is likely the better route to take.  We attempted updating directly from Intel’s site, only to stumble on more concerns than we had hoped for.

Your feedback matters

If you have any comments, questions, or topics you would like us to address, please feel free to leave your comment in the space below or email us at feedback@leanexecution.ca or feedback@versalytics.com.  We look forward to hearing from you and thank you for visiting.

Until Next Time – STAY lean

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Vergence Analytics
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Are you Winning? A Hockey Lesson for Lean Metrics.

Toronto Maple Leafs
Image via Wikipedia

The world of sports is rife with statistics and hockey is no exception, especially here in Canada.  Over the past few weeks, local Toronto hockey fans anxiously watched or listened for the results of every Toronto Maple Leafs hockey game – all the while hoping for a win and a shot at making into the playoffs.

As has been the case for the past many years, the Leaf’s contention for a playoff spot was equally dependent on their own performance and that of their competitors.  The Leaf’s finally started to win games as did their competitors.  In the end they didn’t make it.

It is interesting to note that, despite their lack luster record, the Maple Leafs are one of the top franchises in the National Hockey League (NHL).  Thanks to the Toronto Star, I learned that we have 45 reasons to hope.

What is the lesson here?

All the statistics or metrics in the world won’t change the final outcome for the Toronto Maple Leafs and neither will any of the excuses for their poor performance.

These players are paid professionals, hired for the specific purpose of contributing to the overall performance of their team to win hockey games.  In the end, no one cares about player performance data, injuries, shots on net, penalties, goals against, or any other metric.

To me it really comes down to one question:

Are you Winning?

The answer to this question is either Yes or No.  There is no room for excuses or “it depends”.  You either know or you don’t.  In hockey, it’s easy.  The metric that matters is the final score at the end of the game.

We are all paid to peform – excuses don’t count.  Determine which metric defines winning performance and be ready when someone asks:

Are you Winning?

As any rainmaker knows, customers expect a quality, low cost product or solution, delivered on time, and in full.  To do anything less is inexcusable.

Until Next Time – STAY lean!

Vergence Analytics
Twitter:  @Versalytics

Communication Breakdown – The Language of Lean

Getting people engaged and “on the same page” requires everyone to understand the language used to convey the message.  Even the method of disseminating information can create confusion.  Oral presentations can convey a completely different message than one delivered in writing.

The tone used in an oral presentation cannot be delivered in writing using the same words.  Voice inflections, body language, and atmosphere all add to the message.  How many times have you heard the expression, “When s/he spoke those words, you could almost hear a pin drop”.  It is obviously not just the spoken words but how they are delivered that create an aura of suspense or awe.

How does this apply to lean?  The answer is quite simple.  Don’t assume that people understand – just because you told them either verbally or in writing.  Lean is “hands-on” management.  Go to the process and see what is actually happening (or not happening).

If a picture is worth a thousand words, how many words would it take to describe the experience of seeing the real process first hand?  “Don’t just tell me – show me” are words that should be uttered most often by leadership, managers, or any lean practitioner.

The first step to implementing lean is going out to SEE what opportunities exist.  Unlike computer programs that have explicit meanings, people are intelligent and capable of interpreting the real message behind the words.  Computers do not have an intuitive sense.

The Language of Lean can be summed up in two words – QUICK ACTION.  Successful lean organizations understand that ACTION is truly LOUDER than WORDS.  See it, Solve it, Share it executed in real-time.

Until Next Time – Stay LEAN!

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!

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.

OEE Integration – Where do We Measure OEE? – Part I

OEE Integration Part IX – Where do we measure OEE?

Our recent posts have included numerous examples to calculate OEE correctly. We also discussed integration of OEE as an effective metric for managing your processes and ultimately how to analyze and use the data to improve your profitability.  We spent little time discussing where this measurement should occur.  OEE can be measured for both manual and automated lines as well as any stand alone operation.

The OEE factors (Performance, Availability, and Quality) are process output results.  The expectation, of course, is to manage the inputs to the process to assure the optimal result is achieved.  Availability, Performance, and Quality can be measured in real-time during production. However, the results should be subject to a due diligence review when production is complete.

At a minimum, it makes sense to measure OEE at the end (output) of the line or process but this is not always ideal.  The complexity of OEE measurement occurs when single or multiple sub-cells are constrained by an upstream or downstream operation or bottleneck operation.  The flow, rate, or pace of a process is always  restricted or limited by a  sequence / process constraint or bottleneck operation.  Just as a chain is only as strong as its weakest link, so too is the line speed limited by the bottleneck operation.

We contend that the “Control-Response” loop for any process must enable immediate and effective corrective action based on the measured data and observations.  Measuring OEE in real-time at the bottleneck process makes it an ideal “Trigger Point” metric or “Control-Response” metric for managing the overall process even in “isolation” at the bottleneck operation.  Any variations at the bottleneck correlate directly to upstream and downstream process performance.

A disruption to production flow may occur due to a stock-out condition or when a customer or supplier operation is down.  While these situations affect or impact the OEE Availability factor, external factors are beyond the scope of the immediate process.

Real-time OEE requires that these events and others, such as product disposition, are reported in real-time as well.  External events are more difficult to capture in real-time and by automated systems in particular.  Operator interfaces must accommodate reporting of these events as they occur.

Reporting PITFALL – After-the-Fact events

If a quality defect is discovered several days after reporting production and all parts are placed on hold for sorting or rework, the QUALITY Factor for that run should be changed to ZERO.  In turn, the net OEE for that run will also be ZERO.  If the system is not changed, the integrity of the data is lost.  This also exemplifies that real-time data can be deceiving if proper controls are not in place.

“Where do we measure?” is followed by “When do we measure?” The short list of examples provided here are likely events that are far and few between.  If this is a daily occurrence, consider adopting the banking policy of, “adjustments to your account will be reflected on the following business day”.  Your process / system is in need of a rapid fix.

OEE is one of the few vital signs or key performance metrics for your manufacturing operation.  As such, measure where you will reap the greatest benefit and focus your attention on the process or operation accordingly.  OEE is as much a diagnostic tool as it is a monitoring tool.

Until Next Time – STAY lean!

Vergence Analytics
Versalytics

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!

Vergence Analytics

OEE Measurement Error

How many times have you, or someone you know, challenged the measurement process or method used to collect the data because the numbers just “don’t make sense” or “can’t be right”?

It is imperative to have integrity in the data collection process to minimize the effect of phantom improvements through measurement method changes.  Switching from a manual recording system to a completely automated system is a simple example of a data collection method change that will most certainly generate “different” results.

Every measurement system is subject to error including those used to measure and monitor OEE.  We briefly discussed the concept of variance with respect to actual process throughput and, as you may expect from this post, variance also applies to the measurement system.

Process and measurement stability are intertwined.  A reliable data collection / measurement system is required to establish an effective baseline from which to base your OEE improvement efforts.  We have observed very unstable processes with extreme throughput rates from one shift to the next.  We learned that the variance in many cases is not always the process but in the measurement system itself.

We decided to comment briefly on this phenomenon of measurement error for several reasons:

  1. The reporting systems will naturally improve as more attention is given to the data they generate.
  2. Manual data collection and reporting systems are prone to errors in both recording and data input.
  3. Automated data collection systems substantially reduce the risk of errors and improve data accuracy.
  4. Changes in OEE trends may be attributed to data collection technology not real process changes.

Consider the following:

  1. A person records the time of the down time and reset / start up events by reading a clock on the wall.
  2. A person records the time of the down time event using a wrist watch and then records the reset /start up time using the clock on the wall.
  3. A person uses a stop watch to track the duration of a down time event.
  4. Down time and up time event data are collected and retrieved from a fully automated system that instantly records events in real time.

Clearly, each of the above data collection methods will present varying degrees of “error” that will influence the accuracy of the resulting OEE.  The potential measurement error should be a consideration when attempting to quantify improvement efforts.

Measurement and Error Resolution

The technology used will certainly drive the degree of error you may expect to see.  A clock on the wall may yield an error of +/- 1 minute per event versus an automated system that may yield an error of +/- 0.01 seconds.

The resolution of the measurement system becomes even more relevant when we consider the duration of the “event”.  Consider the effect of measurement resolution and potential error for a down time event having a duration of 5 minutes versus 60 minutes.

CAUTION!

A classic fallacy is “inferred accuracy” as demonstrated by the stop watch measurement method.  Times may be recorded to 1/100th of a second suggesting a high degree of precision in the measurement.  Meanwhile, it may take the operator 10 seconds to locate the stop watch, 15 seconds to reset a machine fault, and 20 seconds to document the event on a “report” and another 10 seconds to return the stop watch to its proper location. 

What are we missing?  How significant is the event and was it worth even recording?  What if one operator records the “duration” after the machine is reset while another operator records the “duration” after documenting and returning the watch to its proper location?

The above example demands that we also consider the event type:  “high frequency-short duration” versus “low frequency-long duration” events.  Both must be considered when attempting to understand the results.

The EVENT is the Opportunity

As mentioned in previous posts, we need to understand what we are measuring and why.  The “event” and methods to avoid recurrence must be the focus of the improvement effort.  The cumulative duration of an event will help to focus efforts and prioritize the opportunities for improvement.

Additional metrics to help “understand” various process events include Mean Response Time, Mean Time Between Failures (MTBF), and Mean Time To Repair (MTTR).  Even 911 calls are monitored from the time the call is received.  The response time is as critical, if not more so, than the actual event, especially when the condition is life-threatening or otherwise self-destructive (fire, meltdown).

An interesting metric is the ratio between Response Time and Mean Time To Repair.  The response time is measured from the time the event occurs to the time “help” arrives.  Our experience suggests that significant improvements can be made simply by reducing the response time.

We recommend training and providing employees with the skills needed to be able to respond to “events” in real time.  Waiting 15 minutes for a technician to arrive to reset a machine fault that required only 10 seconds to resolve is clearly an opportunity.

Many facilities actually hire “semi-skilled” labour or “skilled technicians” to operate machines.  They are typically flexible, adaptable, present a strong aptitude for continual improvement, and readily trained to resolve process events in real time.

Conclusion

Measurement systems of any kind are prone to error.  While it is important to understand the significance of measurement error, it should not be the “primary” focus.  We recommend PREVENTION and ELIMINATION of events that impede the ability to produce a quality product at rate.

Regrettably, some companies are more interested in collecting “accurate” data than making real improvements (measuring for measurements sake). 

WHAT are you measuring and WHY?  Do you measure what you can’t control?  We will leave you with these few points to ponder.

Until next time – STAY Lean!

OEE For Dedicated – Single Part – Processes

OEE For Dedicated – Single Part – Processes

Definition: 

Dedicated – Single Part – Process:  A process that produces a single product or slight variations on a theme and does not require significant tooling or equipment changeover events.

A single part process is the easiest application for a OEE pilot project.  The single part process also makes it easier to demonstrate some of the more advanced Lean Thinking tools that can be applied to improve your operation or process.  In our “Variation, Waste, and OEE” post, we introduced the potential impacts of variance to your organization.  We also restated our mission to control, reduce, and eliminate variation in our processes as the primary objective of LEAN.

We need to spend more time understanding what our true production capabilities are.  The single part process makes the process of understanding these principles much easier.  The lessons learned can then be applied to more complex or multipart processes.  In multipart or complex operations, production part sequencing may have a significant impact on hourly rates and overall shift throughput.  How would you know unless you actually had a model that provided the insight?

Process Velocity:  Measuring Throughput

Let’s start this discussion by asking a few simple questions that will help you to get your mind in gear.  Do you measure variation in production output?  Do you measure shift rates?  Do you use the “average” rate per hour to set up your production schedules?  How do you know when normal production rates have been achieved?  Does a high production rate on one shift really signify a process improvement or was it simply a statistically expected event?

Once again an example will best serve our discussion.  Assume the following data represents one week of production over three shifts:

Machine A:  Production Process Performance Report

Cycle Time (Seconds):   57      
Shift Standard (440 minutes) 440      
             
Day Shift Planned Quantity
Production Time Total Test Scrap Accept
Mon 1 440 420 1 2 417
Mon 2 440 390 1 1 388
Mon 3 440 320 1 3 316
Tue 1 440 361 1 1 359
Tue 2 440 392 1 5 386
Tue 3 440 365 1 2 362
Wed 1 440 402 1 7 394
Wed 2 440 317 1 6 310
Wed 3 440 430 1 1 428
Thu 1 440 453 1 5 447
Thu 2 440 419 1 3 415
Thu 3 440 366 1 1 364
Fri 1 440 400 1 2 397
Fri 2 440 411 1 4 406
Fri 3 440 379 1 2 376
Totals 15 6600 5825 15 45 5765

The following table is an extension of the above table and shows the unplanned downtime as well actual, standard, and ideal operating times.

Day Shift Unplanned Operating Time
Down Time Actual Standard Ideal
Mon 1 25 415.0 399.0 396.2
Mon 2 55 385.0 370.5 368.6
Mon 3 122 318.0 304.0 300.2
Tue 1 84 356.0 343.0 341.1
Tue 2 65 375.0 372.4 366.7
Tue 3 82 358.0 346.8 343.9
Wed 1 45 395.0 381.9 374.3
Wed 2 130 310.0 301.2 294.5
Wed 3 30 410.0 408.5 406.6
Thu 1 5 435.0 430.4 424.7
Thu 2 40 400.0 398.1 394.3
Thu 3 90 350.0 347.7 345.8
Fri 1 45 395.0 380.0 377.2
Fri 2 45 395.0 390.5 385.7
Fri 3 60 380.0 360.1 357.2
Totals 15 923 5677 5533.8 5476.8

The table below shows the OEE calculations for each day and shift worked.  Note that this table is also an extension of the above data.

Day Shift Overall Equipment Effectiveness (OEE)
Availability Performance Quality OEE
Mon 1 94.3% 96.1% 99.3% 90.0%
Mon 2 87.5% 96.2% 99.5% 83.8%
Mon 3 72.3% 95.6% 98.8% 68.2%
Tue 1 80.9% 96.3% 99.4% 77.5%
Tue 2 85.2% 99.3% 98.5% 83.3%
Tue 3 81.4% 96.9% 99.2% 78.2%
Wed 1 89.8% 96.7% 98.0% 85.1%
Wed 2 70.5% 97.1% 97.8% 66.9%
Wed 3 93.2% 99.6% 99.5% 92.4%
Thu 1 98.9% 98.9% 98.7% 96.5%
Thu 2 90.9% 99.5% 99.0% 89.6%
Thu 3 79.5% 99.3% 99.5% 78.6%
Fri 1 89.8% 96.2% 99.3% 85.7%
Fri 2 89.8% 98.8% 98.8% 87.7%
Fri 3 86.4% 94.8% 99.2% 81.2%
Totals 15 86.0% 97.5% 99.0% 83.0%

The results from the table above suggest that the process is running just short of world-class OEE (83% versus 90% for dedicated processes.  Note that 85% is considered world-class for multipart variable processes).  As you can see from the daily and shift results, a lot of variation is occurring over the course of the week.  This is the opportunity that we need to pursue further.  A quick scan of the data suggests that Wednesday 2nd shift and Monday 3rd shift are the main contributors to the reduced OEE.  We will investigate the data a little further to really understand what opportunities exist.

A dedicated, continuous process should yield a higher OEE since the process is not subject to continual setup and change over.  Although some model changes or variations to the existing product may exist, they are typically less disruptive.  A OEE of 90% may be an achievable target and is typical for most dedicated operations.

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!

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