Tag: Efficiency

Time Studies with your BlackBerry

Performing a time study is relatively easy compared to only few years ago.  The technologies available today allow studies to be conducted quite readily.

Time Studies and OEE (Overall Equipment Effectiveness)

The Performance factor for OEE is based on the Ideal Cycle Time of the process.  For fixed rate processes, the Name-Plate rate may suffice but should still be confirmed.  For other processes such a labour intensive operations, a time study is the only way to determine the true or ideal cycle time.

When measuring the cycle time, we typically use “button to button” timing to mark a complete cycle.  It can be argued that an operator may lose time to retrieve or pack parts or move containers.  Including these events in the gross cycle time will hide these opportunities.  It is better to exclude any events that are not considered to be part of the actual production cycle.

When calculating the Performance factor for Overall Equipment Effectiveness (OEE), the efficiency shortfalls will be noted by the less than 100% performance.  The reasons for this less than optimal level of peformance are attributed to the activities the operator is required to perform other than actually operating the machine or producing parts.

All operator activities and actions should be documented using a standardized operating procedure or standardized work methodology.  This will allow all activities to be captured as opposed to absorbed into the job function.

The BlackBerry Clock – Stopwatch

One of the tools we have used on the “fly” is the BlackBerry Clock’s Stopwatch function.  The stopwatch feature is very simple to use and provides lap time recording as well.

When performing time studies using a traditional stopwatch, being able to keep track of individual cycle times can be difficult.  With the stopwatch function, the history for each “lap” time is retained.  To determine the individual lap time or cycle time, we recommend dividing the total lapsed time by the number of completed cycles (or laps).

The individual lap times are subject to a certain degree of uncertainty or error as there will always be a lead or lag time associated with the pushing of the button on the BlackBerry to signal the completion of a cycle.  Although this margin of error may be relatively small, even with this level of technology, the human element is still a factor for consideration.

Once the time study is complete you can immediately send the results by forwarding them as an E-mail, PIN, or SMS.

The BlackBerry Camera – Video Camera

Another useful tool is the video camera.  Using video to record operations and processes allows for a detailed “step by step” analysis at any time.  This is particularly useful when establishing Standard Operating Procedures or Standardized Work.

Uploading videos and pictures to your computer is as easy as connecting the device to an available USB port.  In a matter of minutes, the data is ready to be used.

Video can also be used to analyze work methods, sequences, and also serves as a valuable problem solving tool.

Until Next Time – STAY Lean!

We are not affiliated with Research In Motion (RIM).  The intent of this post is to simply demonstrate how the technology can be used in the context described and presented.
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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 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

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!

SPC for OEE

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

Our simple answer is this:

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

Negative trends may have Positive returns

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

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

Scope of Analysis – Keep it Simple

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

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

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

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

Statistics on a larger scale

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

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

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

A final note

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

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

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

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

Until Next Time – STAY lean!

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OEE 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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