Category: Quality

Strategies to improve Quality are typically founded on rework and scrap reduction. Measurement of Quality is not limited to the Quality factor for OEE. The cost of non-Quality is also a key metric to managing overall quality performance.

How to Improve OEE – Any Questions?

Ask any Quality or Engineering manager and they will tell you that measurement systems are valuable tools to identify problems and opportunities.  The measurement system itself is not the answer – it is the data source, the EVIDENCE that drives the questions.  It is a part of the discovery and validation process to confirm the opportunity or problem and the effectiveness of the solutions to resolve it.

A well integrated OEE system should provide the data to answer the questions on everyone’s mind, “What do we need to do to improve?” or “Why aren’t we improving?”  The simple answer is, “We need to fix it.”  Of course the real question may not be, “What do we need to fix?” but, “Why did it break?”

Yes, we will likely have to replace the part(s) that failed to get the line back up and running, but what really caused the failure to occur?  What was the real root cause?  This introductory post to problem solving and root cause analysis will kick start some of the techniques used to solve problems effectively.

The Problem Statement:

The key to effective problem solving starts with identifying the problem to be solved.  This is typically a brief statement describing the problem.  For external concerns, the problem is usually stated in customer terms.

This post presents some simple examples of problems to be solved.  You will quickly discover that defining the problem may not be as simple as it looks.  We will discuss this in more depth in our future posts.

Root Cause Analysis

Identifying the real root cause(s) for the failure is the secret to successful problem solving.  The method you use to arrive at the root cause should allow you to confirm and validate your solution before taking action.  Here is an important point to remember:

Do not confuse symptoms with root causes.  

For example, you are driving down the road and suddenly find yourself struggling to maintain control of your vehicle.  Your expert driving skills allow you to pull over and stop on the side of the road.  You get out of the car and walk around to discover that you have a flat tire.  The flat tire is a symptom – not the root cause.

As luck would have it, a police officer who just happened to be following you in an unmarked car, notices your sudden erratic driving behavior and charges you with recklessness and careless driving.  Since none of the tires on the police car are flat, the officer presumes the condition of your vehicle is the direct result of your poor driving skills and bad habits after many years on the road.  Another point to remember:

Do not jump to conclusions

You, like many people, would argue that your many years of driving provided you with the experience necessary to avert danger.  The officer quickly recognizes that your many years of experience have caused you to lose perspective of the potential hazards of driving.  The officer advises that your driving record shows no record of any tickets or accidents and clearly suggests that you have had very few “experiences” with the law and minimal exposure to poor road conditions.

The officer proceeds to charge you, the operator, because you simply weren’t paying attention to the conditions and potential hazards of the road.  You are given a ticket to serve as a reminder to pay more attention to the road and to be mindful of your driving habits in the future.  Then to add insult to injury, the officer advises you to fix your tire and drive carefully. 

Unforgiving of the circumstances and since quota’s have to be met, the charges stand and you find yourself on your way to court.  As you sit in your vehicle, stunned that you just got a ticket for getting a flat tire, you are conflicted and fuming because the officer blamed you, your poor driving skills, and your bad habits for driving recklessly down the road!  The following tip will help you remember:

Operator Error is not a Root Cause

Many times, management is too quick to attribute the root cause to operator error.

5 WHY Analysis

One of the best methods for identifying the real root cause is the 5-Why approach.  The concept of asking the question “WHY?” five times is quite simple.  In practice though, you will find it may not be that easy.  Why?  Because the wrong answer will lead you through a continuing series of wrong answers that ultimately lead to the wrong conclusion.

There is always more than one answer – Which one is correct?

Referring back to our example of the flat tire, you now need an argument to absolve yourself of any blame for the incident on the highway.  In court, the judge asks, “How you plead to the charges before you?”  You answer, “Not Guilty your honor.”

  1. Why?  While I was driving down the road, I got a flat tire.
  2. Why?  Because all the air ran out of my tire.
  3. Why?  Because there was a hole in it.
  4. Why?  Because the tire didn’t have anti-puncture technology.
  5. Why?  Because the manufacturer didn’t design it properly.

Were it not for my expert driving skills, this situation could have been much worse.  As it was, using my superior driving skills, I successfully managed to maneuver my vehicle, without incident, to the side of the road, averting what could have been a disastrous crash.  Therefore, I request to be completely absolved of any and all wrongful doing and I am filing a class action suit against the tire manufacturer to cover court costs, lost wages, and damages as well as my emotional stress.

Clearly not satisfied, the judge requests you to take a 10 minute break to rethink your case.  On your return to the courtroom, you are prepared to present the following argument:

  1. Why?  While I was driving down the road, I got a flat tire.
  2. Why?  Because all the air ran out of my tire.
  3. Why?  Because there was a hole in it.
  4. Why?  Because there was a nail on the road.
  5. Why?  Because the government refuses to keep the highways clean.

Were it not for my expert driving skills, this situation could have been much worse.  As it was, using my superior driving skills, I successfully managed to maneuver my vehicle, without incident, to the side of the road, averting what could have been a disastrous crash.  Therefore, I am filing a class action suit against the government to cover for court costs, lost wages, and damages as well as my emotional stress.  To resolve this matter quickly, I request that all charges be dropped and I in turn will drop my counter-claim.

The purpose of the above example was to demonstrate how the answer to the question – WHY? – can lead to completely different conclusions.  On one hand we’re ready to sue the tire manufacturer and on the other, we’re ready to take on the government.  If there was indeed a nail on the road, how did it get there?

Don’t Assign Blame

Solving problems and getting to the root cause is not about assigning blame to someone or something.  You can’t blame the government or the tire company for the fact that there was a nail on the road.  It is to easy to assign blame and it happens everywhere, everyday.  Perhaps the nail manufacturer should be sued as well for failing to provide adequate protections should the nail become lost or misplaced.

The question that wasn’t asked is, “Why was the nail on the road?”  The answer may be that it likely fell out of a board or from a truck or trailer that may have been carrying construction materials.  Again, being careful with the answer, we don’t want to come to the conclusion that nails should be banned completely.

On the other hand, it may be worthwhile to advise that all companies and contractors must make a reasonable effort and take appropriate precautions and measures to ensure that all loads are secure and free from loose raw materials.  Any nails must be placed in a sealed container and secured to the vehicle for the purpose of transport.  A maximum fine of $2,000.00 may be imposed and made payable to the “Operator Error Trust Fund.”

Leading the Witness:  The solution BIAS

STOP! – if you think you already know the answer – Stop!  We know that the right question doesn’t always lead to the right answer as we attempted to show in our example.  Another major pitfall is thinking we already have the answer and we just need to frame the questions and answers to support that conclusion.  This isn’t problem solving, this is creative story telling.  Don’t lead your team into following what “appears” to be a logical conclusion – be prepared to prove it.

Don’t Assume Anything – Follow the EVIDENCE

At a minimum, follow the evidence.  What is the data telling you?  It’s time to start thinking like a crime scene investigator (CSI) or good lawyer.  Asking questions and continuing to probe for answers is the secret to uncovering the less obvious and, more than likely, real solution.

Many OEE equipment / software integrators provide the ability to record and track downtime events in real time.  This data is extremely valuable for trouble shooting and problem solving; however, they are not necessarily root causes.  The integrators provide the capability to readily identify what part of the process failed or what is broken.  While this may be the cause of the line down condition, it is not the root cause of the problem.

Do not confuse the Point of Failure (Source) with the Root Cause

Don’t fall into this trap:

  • Supervisor:  “The OEE system report showed that we lost two hours on the paint line last night.”
  • Maintenance:  “Yeah, I saw the report too.  This OEE system tracks everything!”
  • Supervisor:  “Why did the line go down?”
  • Maintenance:  “The A-Tank feed pump overheated.  The OEE system told us exactly which pump failed.  It saved us a ton of time.”
  • Supervisor:  “What did you do?”
  • Maintenance:  “Oh, we replaced it.  The line is running fine now.”
  • Supervisor:  “OK, that’s good.  Thanks.”

End of conversation.

So, WHY did the pump overheat?  Some questions just never get asked, but I’m sure the OEE will be just fine on the next shift.  We recognize that most effective TPM managers are sharper than this.  Our point is that not everyone is looking at the data from the same perspective.

We’ll discuss “How to Improve OEE” in more detail in our next post:  “How to use the 5 Why Approach.”

Until Next Time – STAY lean!

OEE Integration – Part VI

In the automotive industry, an effective quality system forms the basis of many standard operating procedures used in the manufacturing environment. Specifically, the major automotive OEM’s require that suppliers implement an effective quality system that conforms to the TS16949 Technical Specification and be able to demonstrate compliance to these requirements.  This is one area where a fully integrated OEE system can shine.

In this regard, the quality system is also responsible for a signficant portion of the infrastructure that exists within a manufacturing facility as well.  The process used to integrate the many requirements to support the quality function can and should be used to support the implementation and integration of OEE into your organization.

Your OEE data / system can be used to demonstrate compliance to many of the requirements of TS16949.  Process efficiency, preventative maintenance, quality, standardized work, and training effectiveness are a few of the areas where OEE data can be used to demonstrate continuous improvement.

We will examine the various areas where OEE can be used and aligned with the TS16949 standard.  Integrating OEE into the very infrastructure of your organization is a core requirement to ensure its long term viability as key metric.  We would encourage you to review the TS16949 standard for yourself to determine where and how OEE can be used.

The standard is a quick read, at approximately 40 pages.  It would also serve as a good refresher to determine what is really required.  We recognize that customer specifics are also part of the overall quality system, however, our intention at this time is to focus on the core TS16949 technical specification.

We will be ammending this post as we delve further into the specific details.  Integration of your OEE system must be supported by a solid infrastructure if it is going to yield the benefits you are looking to achieve.

Until next time – STAY Lean

OEE Integration – Part V

Defining overall equipment effectiveness (OEE) criteria as part of the scope of work or purchase order agreement is quickly becoming standard practice throughout the automotive industry and manufacturing in general.

OEE Assessments

OEE criteria should be performed for every new purchase.  Often times, a high speed machine may be incorporated into a mixed technology production environment.  It is also possible that the machine or equipment under review is not the perceived production constraint or bottleneck.  This should not exclude the process from an OEE assessment.

Although LEAN manufacturing encourages single piece flow, it may be more feasible and cost effective for a machine to run independently.  This situation could occur in instances where business has grown within a commodity base and now the capacity of the machine must be shared across multiple product lines.

Engineering and Finance must consider the optimum production model that will yield the most cost effective strategy and subsequent process routing.  This assessment is best supported using Value Stream Analysis and Standardized Work procedures to fully understand the planned costs associated with inventory at all levels or stages of the process (raw, work in progress, and finished goods), labour (direct and indirect), and burden or overhead.

Lastly, it is important to understand the real or full potential of the equipment or process being purchased or developed.  Future business costs and opportunities for future growth are important considerations for any capital investment.  Press shops or metal stamping suppliers recognize open capacity to drive current and future business growth demands.  Idle machines don’t make money.  Open capacity is money lost.

The Purchase Agreement

To eliminate any misconceptions or lack of understanding, OEE expectations must become an integral part of the purchase agreement.  This can be accomplished by creating a Statement of Work, incorporating the requirements into a tooling, machine, or equipment standard, or, at a minimum, as purchase order line item stipulating the OEE criteria to be satisfied as a condition of purchase.

The objective of these tools is to ensure that all parties are aware of the their obligations and responsibilities to deliver a robust process that meets the OEE objectives.  We recall an incident (after the fact) where the scope of work clearly stated that machine setup or change over time was to be calculated as part of the availability factor.  For the most part, the equipment met the required performance and quality criteria, however, the supplier assumed availability only pertained to the downtime experienced while the machine was running.  This, coupled with downtime during the run, resulted in a less than satisfactory availability factor and resulting OEE index.

In this case, the equipment supplier lost a significant percentage of their final payment for failing to meet the OEE criteria defined in the purchase order and statement of work.  Setup is a planned activity directly related to the production of parts and greatly affects the available capacity of the machine or equipment.

We recommend defining the criteria for each individual factor and the overall equipment effectiveness (OEE).  The values you choose for each factor will depend on your operation or the process under review and may include considerations such as low versus high volume or inventory costs, make-to-order versus warehousing / storage.

For buy-off purposes, we expect a new process to provide a minimum of 90% Availability, 95% performance, and 100% Quality.  We will not accept any process that is less than 85% for a dedicated process.  Mixed model production equipment may be subject to different criteria, specifically regarding availability as tool change complexity increases.

Availability includes change over or setups.  Increased model mix and low volume production may reduce availability.  This assessment should be determined in conjunction with detailed change over / setup instructions.

Typically, Quick Die Change or Tool Change strategy is deployed for most, if not all, new programs.  The investment in these techniques increases your agility as a supplier and maximizes your machine up time.  A rapid change-over / setup strategy can significantly reduce the dependence on high volume production to sustain profitability.

Suppliers to the automotive industry have certainly felt the impact of low or significantly reduced volumes over the last quarter of 2008 and certainly the first quarter of 2009.  An effective tool change strategy to maximize up-time and support low volume runs has never been in demand more than now.

The 95% performance factor takes into account operator functions outside of the normal machine process cycle.  A standardized work process will enable you to determine what performance level is achievable.

If Six-Sigma is your objective, then anything less than 100% quality at machine buy-off is a formula for failure. 

In Conclusion

So when should OEE integration start?  At the onset of every new program and the OEE criteria should be incorporated into the purchase agreement.  This will ensure that OEE becomes and remains an integral part of the process.

In the past, many tools were bought-off by simply running 300 pieces or in other cases a minimum of 8 hours.  The only true measure was up time throughput and the quality of the product.  Today, there is more to running an efficient operation than simply having the ability to produce parts.  Safely producing a quality product at rate – effectively – is the mission.

More on this topic to follow.

Until next time – STAY lean!

Season Greetings!

We wish all our visitors, past and present, the best of this holiday season.  We look forward to serving you in 2009.

Look for our guide to “Lean In Real Time” – scheduled for release in 2009.

On behalf of the LeanExecution Advanced Strategy Team,

Seasons Greetings and Happy New Year!

Improving OEE: A Hands On Approach

We have explored Overall Equipment Efficiency (OEE) from several perspectives and how it can be used as an effective performance metric.  The purpose of measuring and monitoring OEE, at a minimum, should be three fold:

  1. To ensure the current performance levels are sustained,
  2. To identify new opportunities for improvement,
  3. To assess the effectiveness of current improvement initiatives.

The Culture of Continuous Improvement and Innovation

A continuous improvement “mindset” must be part of the organizational culture to achieve maximum results.  Too many companies charge the engineering department or some other “arm” of the organization to generate the ideas that can be implemented to improve availability, performance, and / or quality.  We strongly urge you to include everyone in the improvement process, especially the very people who perform the tasks on a daily basis.  Why?  The simple answer is, “They are the eyes and ears of the process”.

Despite some of the old school thinking that may persist in industry, most people take pride in their work and want to do a good job.  OEE is as much a performance metric for the individuals on the shop floor as it is for the management and leadership of the company.  Even the most educated doctor will ask the patient what the symptoms are as part of the assessment process.

While it may be difficult to assess what level of improvement can be achieved, it has been suggested that world class OEE is 85%.  We suggest that you establish a reasonable baseline and determine relative improvements accordingly.  The baseline you use should be comprised of two key components:

  1. Historical data for OEE and each factor (Availability, Performance, and Quality)
  2. A detailed Standard Operating Procedure for each process under consideration

Getting Started – Collect and Communicate Data

Almost every continuous improvement (CI) activity or project is accompanied by a list of actions that must be implemented.  Where does this list come from?

There are at least two very basic approaches to getting the improvement process underway:

  1. Collect and analyze data from the current process
  2. Set up a FLIP Chart at the line or machine

Step 1 should be fairly straightforward.  The premise here is that OEE data is already being collected and analyzed on a regular basis.  Step 2 may not be as familiar to you.

FLIP Charts

This is probably one of the most fundamental and basic data collection tools available on the market.  This approach may seem overly simplistic but the objective is to keep it simple and effective.

Advantages:

  1. Data collection in “real time”
  2. Anyone can add to the List
  3. Anyone can update the List
  4. Readily Available to ALL
  5. Writing Skills ONLY
  6. Instant Feedback
  7. Highly Visible

What do we record on the FLIP chart?  We have experienced the best success with the following simple format.  At the top of the FLIP chart write down Today’s Date and Shift, then setup the following headings:

Time   Problem/Concern   Assigned To   Task Completed   By (Initials)

Any time an event occurs or an opportunity arises for improvement, simply enter the appropriate data under the headings shown.  The flip chart can also be used to track progress – INSTANTLY.  Whenever a task is completed, the person responsible for the “fix” simply enters the time / date and their initials.

FLIP Chart – Built in Accountability

Using the flip chart as a living “action item list” introduces accountability from all levels to the process on the shop floor.  As tasks or actions are completed, everyone will see that the concerns are being addressed causing the improvement cycle to continue and reinforcing the value of everyone’s input to the process.

Our experience has shown the FLIP chart to be one of the most engaging improvement processes on a continuing basis.  Improvement history is readily available on the shop floor.  No complex searches, computer programs, or advanced skill set is required to see what is going on and what is being done about it.  As much as we don’t like to put problems on display, you may be surprised how impressed your customers are with this type of interactive CI process.

The FLIP chart is a very primitive but effective tool for collecting data and communicating results.

Improving OEE

Since OEE is comprised of three elements, it stands to reason that at least three major improvement initiatives exist:  Availability, Performance, and Quality.  How do we go about improving these elements?

Availability: Start with a downtime assessment:

  1. Categorize Events (Planned vs. Unplanned)
  2. Frequency / Occurrence Rate
  3. Duration
  4. Type:  Planned, Preventable, Predictable, Unplanned, Unknown

From our previous discussions on Availability, the known “Planned” events may include such change events as materials, tooling, and personnel (shift changes and / or breaks).  Improving availability requires the elimination of UNPLANNED events and reducing the duration of PLANNED events.  Successful improvements can only be developed and achieved if there is integrity in the baseline information and data.

Implementing SMED (single minute exchange of dies) is one strategy to reduce the duration of die changes.  A detailed die change process is used to determine the activities that can be performed while the machine is still running (External Events) and those that can only be performed while the machine is down (Internal Events).  Further assessments are conducted to determine what improvements are possible to reduce the duration of the internal events.  Such improvements may include hydraulic clamping, quarter turn screws, standardized shut heights, standardized locating pins, standardized pass heights to name a few.

Scheduling sequences may also be an important factor in the change over process.  If a common material (type or color) is used for two different parts, it may be more effective to run them back to back through the same machine.  Tooling may be shared among different part numbers and would require less change over time if they were considered as a product family for scheduling purposes.

Policy changes and capital investments are easily justified when you are able to demonstrate the improvements using a “plan vs actual” strategy that is complimented by data and a standard operating procedure.

Performance: Improving performance is not to be confused with reducing the process time (making it faster).  They are two different activities entirely.  If the original cycle time or process rate was calculated correctly, then 100% performance should be achievable right?  Once again, the answer to this question depends on company policy and the method that was used to establish the standard.

Our purpose is not to introduce more confusion, but rather, to make sure that whatever policy is in place is clearly defined and understood.  Remember, the only real industry standard for OEE is the formula used to calculate the result:  A x P x Q.  A standard definition or criteria for determining the individual factors does not exist.

The cycle time for an automated process can easily be determined by measuring the output without disruption over a known period of time.  Is this consistent with company policy?  Is the standard cycle time based on the stated nameplate capacity (rate) or is it based on the actual achieved (optimum) cycle time?

A “button to button” cycle time may be established for a manual operation in a similar manner.  Although it may be perceived as a flaw, the button to button analysis may not necessarily consider container changes or restocking of components that may be required from time to time.  If these “other” tasks are not factored into the cycle time, then it would be impossible to achieve 100% performance unless someone other than the operator was made responsible for those activities.

Start with a Performance Assessment

  1. Confirm company policy and methods for calculating the cycle time.
  2. Confirm the Cycle Time or Production Rate (Time Study)
  3. Compare the Actual versus Standard Operating Procedure
  4. Review the process performance history and data records.
  5. Equipment Condition Assessment – Preventive Maintenance
  6. Process Type:  Automation, Semi-Automation, Manual (Human Effort)
  7. Confirm Reporting Integrity

Only after you have reviewed the data and discussed the opportunities with the team will you be able to develop a performance improvement plan.

Using the “button to button” manual process described above, we already indicated that a person other than the operator could be responsible for restocking components and changing containers to allow the operator to run the machine without interruption.  There may be other activities as well that could be performed someone other than the operator.  A detailed Standard Operating Procedure complete with clearly defined steps (step tasks) and timing for each is the best tool available to improve performance.

Is it possible to change the method or sequence of events that the operator is following to reduce the time taken to perform a step task.  Is the operation “handed”, in other words, does it favor right versus left handed people?  Is the material arranged in such a way as to optimize (minimize) the operator’s movements during the cycle?  Are all operator’s performing the step tasks per the standard operating procedure?  Is the machine itself performing at the optimized cycle or is it running at a slower speed due to electrical, mechanical, or fluid faults?

Some of the activities identified may result in speed increases that will lead to performance improvements relative to the current standard.  Again, company policy should dictate when and how standards are to be updated.  If the standard is updated everytime the cycle time is reduced, how will you recognize the improvement?  We would recommend resetting the standards annually in conjunction with the new fiscal year.  The new performance levels should also be reflected in the business plan.

Quality: This is perhaps one of the easiest to factors to define and may be one of the more difficult factors to improve.  Again this will depend on the definition or criteria used to calculate the Quality factor.  The typical definition adopted by most manufacturers states that any parts failing to meet First Time Through quality criteria include those designated as scrap, test, rework, sort, and / or hold.  In other words, First Time Through quality applies only to those parts that are considered acceptable at the point and time of production.

When do you start counting?  Should set up parts be included in the Quality definition?  We would argue against including set up parts in the quality calculation, however, that doesn’t mean they shouldn’t be accounted for because the material loss is a real cost to the company.  We would define set up time as starting from the last good part produced to the first good part produced for the next job in.

The objective of any Quality improvement strategy is obviously zero defects.  The task is getting it done.

Quality: Start with a Quality Assessment:

  1. Review Process Failure Modes Effects and Analysis (PFMEA)
  2. Review Current Quality Control Plans (Inspection Requirements)
  3. Review and Analyze Quality Performance Data
  4. Review scrap and rework analysis
  5. Identify Top Opportunities (Pareto Analysis)
  6. Initiate Problem Solving Activities (DMAIC, PDCA, PDSA, IDEA Loops)
  7. Execute problem solving strategy
  8. Update Lessons Learned and Best Practices

The ultimate goal for any quality program is to achieve a level of zero defects.  A second, closely related goal is to eliminate, reduce, and control variation in our processes.  Variation and defects are directly correlated and are typically quantified by statistical modeling tools such as the normal distribution or bell curve.  Many tools are available to study and analyze the various attributes of a process to effectively determine the root cause for a given defect.

Some of the many problem solving methods and tools include 8-Discipline Analysis, 5 Why, Fault Tree Analysis, Cause and Effect Diagrams, Pareto Analysis, Design of Experiments (DOE), Analysis of Variance (ANOVA) tools among others.

Next Steps

We have identified the various methods to generate improvement activities. The key to success is developing the action plans and executing them in a timely manner.  This is the critical part of the improvement process.

A word of caution:  Don’t confuse activity with action.  Too many times, the data collection and study processes consume all the resources and more time is spent on data presentation than real analysis.  The goal is to improve the process, solve the problems, and eliminate the defects.

No Input Change = No Output Change

Lessons Learned and Best Practices

It is possible that the wrong process was selected for the product being manufactured.  This may range from the actual tooling to the very equipment that is used to run it.  It is also possible that the capability of the machine was overstated or over-rated prior to purchase.

Maintaining a lessons learned database is one way to make sure that we don’t make the same mistake twice.  It can also serve as a future reference when developing standards for future products or processes.

Perhaps a product or process requires a technology that simply doesn’t exist.  Could this be the stepping stone for a future research and development project?  How do we take things to the next level – the break through?

Until next time – STAY lean!

Twitter:  @Versalytics

Please feel free to forward your questions or comments to us by e-mail at LeanExecution@gmail.com

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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Availability and OEE

What is Availability?

In its simplest form, availability measures the uptime of a machine or process against the planned production time.  As one of the factors of Overall Equipment Efficiency (OEE), Availability is expressed as a percentage.  The uptime is calculated by taking the difference between the planned production time and total duration of the downtime events that occurred during the planned production period.

We specifically address the “Availability” factor in this post for the simple reason that the definition of availability is likely to be one of the most debated and hotly contested topics of your OEE implementation strategy.  The reason for this, in many cases, is the lack of clarity in some of the most basic terminology.  The purpose of this discussion is to present some topics for consideration that will allow you to arrive at a clear definition that can perhaps be formed into a standard policy statement.

We will also demonstrate that it is possible to calculate the downtime by simply knowing the cycle time or process rate, the quantity of parts produced, and the planned production time.  We recommend using this technique to validate or reconcile the actual documented downtime.  We would argue that the first and foremost purpose of any machine monitoring or downtime event measurement system is to determine the “WHY and WHAT” of the downtime events and secondly to record the “When and How Long”.

You will learn that monitoring your processes to determine causes and duration of downtime events  is key to developing effective action plans to improve availability.  The objective of any machine automation, sensor strategy, or data collection and analysis is to determine methods and actions that will improve the availability of the equipment through permanent corrective actions, implementing more effective trouble shooting strategies (sensor technologies), improved core process controls, or more effective preventive maintenance.

Define the purpose of OEE

While it looks like we’re taking a step back from the topic of discussion, bear with us for just a paragraph or two.  A clear statement of purpose is the best place to start before executing your OEE implementation strategy:

To identify opportunities to improve the effectiveness of the company’s assets.

You will quickly realize that, when attempting to define the measurement criteria for the OEE factors, in particular Availability, your team may present rationale to exclude certain elements from the measurement process.  These rationalizations are typically predicated on existing policy or perceived constraints that simply cannot be changed.  People or teams do not want to be penalized for items that are “out of their control” or bound by current policy.  Continuous improvement is impeded by attempts to rationalize poor performance.

We understand that some of these “exclusions” present a greater challenge, however, we do not agree with the premise that they cannot be improved.  Again, it is a matter of “purpose”.  Limiting the scope of measurement will limit the scope of improvement.  Now it’s time to explore what could be the foundation for a sound definition of availability.

Availability Considerations

It may seem reasonable to assume that, at a minimum, the only planned down time events that should be excluded from the availability factor are  planned preventive maintenance activities, mandatory break periods, and scheduled “down” time due to lack of work.  We would argue and agree that the only justification for an idle machine is “Lack of Work”.

What would be the reason to settle for anything less?  If Preventive Maintenance is critical to sustaining the performance of your process, doesn’t it make sense to consider it in the measurement process?  The rationale that typically follows is that Preventive Maintenance must be done and it’s really out of our control – it is a planned event.  We would argue that the time to complete Preventive Maintenance can be improved.

Is it possible that the Mean Time Before Failure or Required Maintenance can be extended?  Is it possible to improve materials, components, or lubricants that could extend the process up time?  Is it possible to improve the time it actually takes to perform the required maintenance?  If so, what is the measure that will be used to show that additional capacity is available for production.

If set up times for die changes or tool changes can be improved from hours to minutes, could the same effort and devotion to improve Preventive Maintenance techniques yield similar results?  We think so.

One example is the use of synthetic oils and lubricants that have been proven to significantly extend the life of tools and components and also reduces the number changes required over the service life of the machine.  Quick change features that can assist with easy and ready access to service points on tooling and machines can also be implemented to reduce preventive maintenance times.

The other exclusion that is often argued is break times.  Labour laws require you to provide break times for your employees.  However, since automated processes are not subject to “Labour Laws”, the “mandatory break times” do not apply.  We would argue that methods should be pursued to reduce the need for human intervention and look for ways to keep the machine running.  Is it possible to automate some of the current processes or rotate people to keep the machine running?

Aside from this more obvious example, consider other organizational policies that may impact how your organization runs:

  1. Shift start-up meetings
  2. Employee Communication Meetings
  3. End of Shift clean up periods
  4. Quality first off approval process
  5. Shift first off versus Run first off
  6. Weld Tip changes – PM or Process Driven

 What is the purpose of the shift start-up meeting?  What is the purpose of the monthly employee communication meeting?  Could this information be conveyed in a different form?  What length of time is really required to convey the message to be shared?  Is the duration of the meeting actually measured or do you resort to the standard time allotted?

Clean up periods at the end of the shift  are also a common practice in many plants.  What is being cleaned up?  Why?  Is it possible to maintain an orderly workplace during the shift – clean up as it happens in real-time?  Again, do you record the actual clean up time or do you just enter the default clean up time allotted?

How much time is lost to verify the integrity of the product before allowing production to commence?  What process parameters or factors would jeopardize the quality of the product being produced?  No one wants to make scrap or substandard components, however, the challenge remains to determine what factors influence the level of quality.  If it is possible to determine what factors are critical to success in advance, then perhaps the quality verification process becomes a concurrent event.

Measuring Downtime.

 There are other factors that can impact availability including, but certainly not limited to, personnel (illness, inclement weather), material availability, other linked processes (feeder / customer), material changes, tool changes, quality concerns, and unexpected process, equipment, or machine faults.

It is possible to use manual or automated systems to collect various machine or process codes to record or document the duration and type of downtime event.  We recommend and support the use of automated data collection systems, however, they should be implemented in moderation.  One of the primary impediments to success is overwhelming volumes of data that no one has the time to analyze.

The Goal = 100% Up Time = ZERO Down Time = Zero Lost Time = Zero Defects = 100% Availability

The goal is to use the data and tools available to either permanently resolve the problem by implementing an effective corrective action or to assist the trouble shooting process by identifying the failure mode and to minimize the duration of the downtime event.

We have witnessed data collection strategies where an incredible number of sensors were installed to “catch” problems as they occur.  The reality was the sensors themselves became the greater cause of downtime due to wear or premature failure due to improper sensor selection for the application.  Be careful and choose wisely.

When used correctly, automation can be a very effective tool to capture downtime events and maintain the integrity of the overall measurement process.  With the right tools, trouble shooting your process will minimize the duration of the down time event.  Monitoring the frequency of these events will also allow you to focus your attention on real opportunities and circumvent nuisance faults.

The objective of collecting the “downtime event” history is to determine what opportunities are available to improve uptime.

Duration versus Frequency

The frequency of a downtime event is often overlooked as most of the attention is devoted to high duration downtime events.  Some sources suggest that short duration downtime events (perhaps as little as 30 seconds) are not worth measuring.  These undocumented losses are reflected, or more accurately hidden, by a corresponding reduction in the performance factor.

Be careful when setting what appears to be simple policy to document downtime.  A 20 second downtime event that occurs 4 times per hour could quickly turn into 10 minutes a shift, 30 minutes a day, 2.5 hours a week, 125 hours a year.  Rather than recording every event in detail, we recommend implementing a simple “tick” sheet to gain an appreciation for the frequency of failures.  Any repetitive events can be studies and reviewed for corrective action.

Verify the Downtime

One of the advantages of OEE is that it is possible to reconcile the total time – OEE should never be greater than 100%.  Of course this statement requires that the standard cycle time is correct and the total quantity of parts produced is accurate.  So, although all of the downtime events may not be recorded, it is very easy to determine how much downtime occurred.  This will help to determine how effectively downtime data is being recorded.

A perfect example to demonstrate this comes from the metal stamping industry.  Progressive dies are used to produce steel parts from coil steel.  The presses typically run at a fixed “predetermined” optimum run rate.  Depending on the type of part and press, progressive dies are capable running at speeds from as low as 10 strokes per minute up to speeds over 300 strokes per minute.

For ease of calculation, assume we have a press that was scheduled to run a part over an 8 hour shift having two 10 minute breaks.  The standard shift hours are 6:45 am – 3:15 pm and 3:30 pm – 12:00 am.  The company provides a 30 minute unpaid meal break after 4 hours of work.  The optimum press speed to run the part is 20 strokes per minute (spm).  If a total of 6200 parts were made – how much downtime was incurred at the press?

To determine the press time required (also known as earned time), we simply divide the quantity of parts produced by the press rate as follows:

Machine Uptime:  6200 / 20 = 310 minutes

Our planned production time was 8 hours or 480 minutes.  Assuming that company policy excludes break times, the net available time to run the press is 480 – (2 x 10) = 460 minutes.

Calculated downtime = Available – Earned = 460 – 310 =150 minutes

Availability = Earned Time / Net Available Time = 310 / 460 = 67.39%

We can see from the above example that it easy to determine what the downtime should have been and, in turn, we could calculate the availability factor.  This calculation is based on the assumption that the machine is running at the stated rate.

The Availability TWIST (1):

Knowing that press and die protection technologies exist to allow presses to run in full automatic mode, the two break periods from our example above do not apply to the equipment, unless company policy states that all machines or processes must cease operations during break periods.

Assuming that this is not the case, the press is available for the entire shift of 480 minutes.  Therefore, the availability calculations from above would be:

Calculated downtime = Available – Earned = 480 – 310 =170 minutes

Availability = Earned Time / Net Available Time = 310 / 480 = 64.58%

The Availability TWIST (2):

Just to expand on this concept just a little further.  We also indicated that the company provided an unpaid lunch period of 30 minutes.  Since meal breaks don’t apply to presses, the reality is that the press was also available to run during this period of time.  The recalculated downtime and availability are:

Calculated downtime = Available – Earned = (480 + 30) – 310 =200 minutes

Availability = Earned Time / Net Available Time = 310 / 510 = 60.78%

The Availability TWIST (3):

Finally, one last twist (we could go on).  We deliberately indicated that there was a 15 minute break between shifts.  Again, is there a reason for this?  Does the machine have to stop?  Why?

Availability – NEXT Steps

As you begin to look at your operations and policies, start by asking WHY do we do this or that?  The example provided above indicates that a significant delta can exist in availability (close to 7%) although the number of parts produced has not changed.  The differing results are related to policy, operating standard, or both.

If the performance (cycle time or production rate) and total quantity of parts produced data have integrity, the availability factor can be reconciled to determine the integrity of the downtime “data collection” system.  From this example it should also be clear that the task of the data collection system is to capture the downtime history as accurately as possible to determine the opportunities to improve availability NOT just to determine how much downtime occurred.

This example also demonstrates why effective problem solving skills are critical to the success of your lean implementation strategy and is also one of the reasons why programs such as six sigma and lean have become integrated as parallel components of many lean execution strategies.

The Goal:  100% uptime / Zero downtime / Zero lost time /100% availability

Regardless of the measurement baseline used, be consistent.  Exclusions are not the issue, it is a matter of understanding what is involved in the measurement process.  For example, maintenance activities performed during break periods may be a good management practice to improve labour efficiencies, however, the fact that the work was performed during a break period should not exclude it from the “downtime” event history.  We would argue that all activities requiring “equipment time” or “process time” should be recorded.

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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Variance, Waste, and OEE

What gets managed MUST be measured – Including VARIANCE.

It is easy to get excited about the many opportunities that a well implemented LEAN Strategy can bring to your organization.  Even more exciting are the results.

Achieving improvement objectives implies that some form of measurement process exists – the proof.  A clear link should be established to the metric you choose and the activity being managed to support the ongoing improvement initiatives.

Measure with Meaning

Why are you “collecting” OEE data?  While OEE can and should be used to measure the effectiveness of your manufacturing operations, OEE on its own does not present a complete solution.  It is true that OEE presents a single metric that serves as an indicator of performance, however, it does not provide any insight with respect to VARIANCES that are or may be present in the system.

We have encountered numerous operations where OEE data can be very misleading.  OEE data can be calculated using various measurement categories:  by machine, part number, shift, employee, supervisor, department, day, month, and so on.

VARIANCE:  the leading cause of waste!

Quality professionals are more than familiar with variance.  Statistically capable processes are every quality managers dream.  Unfortunately, very little attention or focus is applied to variances experienced on the production side of the business.

Some may be reading this and wonder where this is going.  The answer is simple, rates of production are subject to variance.  Quite simply, if you review the individual OEE results of any machine for each run over an extended period of time, you will notice that the number is not a constant.  The performance, availability, and quality factors are all different from one run to the next.  One run may experience more downtime than another, a sluggish machine may result in reduced in performance, or material problems may be giving rise to increased quality failures (scrap).

So, while the OEE trend may show improvement over time, it is clear that variances are present in the process.  Quality professionals readily understand the link between process variation and product quality.  Similarly, variation in process rates and equipment reliability factors affect the OEE for a given machine.

We recommend performing a statistical analysis of the raw data for each factor that comprises OEE (Availability, Performance, and Quality) for individual processes.  Analysis of OEE itself requires an understanding of the underlying factors.  It is impractical to consider the application of ANOVA to OEE itself as the goal is to continually improve.

How much easier would it be if you could schedule a machine to run parts and know that you will get them when you needed them?  You can’t skip the process deep dive.  You need to understand how each process affects the overall top-level OEE index that is performance so you can develop and implement specific improvement actions.

The best demonstration we have seen that illustrates how process variation impacts your operation is presented through a “process simulation” developed from Eli Goldratt’s book, The Goal.  We will share this simulation in a separate post.  Experiencing the effect of process variation is much more meaningful and memorable than a spreadsheet full of numbers.

Conflict Management and OEE

In some environments we have encountered, the interpretation of LEAN strategy at the shop floor level is to set minimum OEE performance objectives with punitive consequences.  This type of strategy is certainly in conflict with any Lean initiative.  The lean objective is to learn as much as possible from the process and to identify opportunities for continual improvement.

Management by intimidation is becoming more of a rarity, however, we have found that they also give rise to the OEE genius.  If performance is measured daily, the OEE genius will make sure a high performing job is part of the mix to improve the “overall” result.  This is akin to taking an easy course of study to “pull up” your overall average.

It is clear from this example, that you will miss opportunities to improve your operation if the culture is tainted by conflicting performance objectives.  The objective is to reveal sources of variation to eliminate waste and variation in your process, not find better ways to hide it.

Variance in daily output rates are normal.  How much are you willing to accept?  Do you know what normal is?  Understanding process variance and OEE as complementary metrics will surely help to identify more opportunities for improvement.

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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Problem Solving with OEE – Measuring Success

OEE in Perspective

As mentioned in our previous posts, OEE is a terrific metric for measuring and monitoring ongoing performance in your operation.  However, like many metrics, it can become the focus rather than the gage of performance it is intended to be.

The objective of measuring OEE is to identify opportunities where improvements can be made or to determine whether the changes to your process provided the results you were seeking to achieve.  Lean organizations predict performance expectations and document the reasons to support the anticipated results .  The measurement system used to monitor performance serves as a gauge to determine whether the reasons for the actual outcomes were valid.  A “miss” to target indicates that something is wrong with the reasoning – whether the result is positive or negative.

Lean organizations are learning continually and recognize the need to understand why and how processes work.  Predicting results with supported documentation verifies the level of understanding of the process itself.  Failing to predict the result is an indicator that the process is not yet fully understood.

Problem Solving with OEE

Improvement strategies that are driven by OEE should cause the focus to shift to specific elements or areas in your operation such as reduction in tool change-over or setup time, improved material handling strategies, or quality improvement initiatives.  Focusing on the basic tenets of Lean will ultimately lead to improvements in OEE.  See the process in operation (first-hand), identify opportunities for improvement, immediately resolve,  implement and document corrective actions, then share the knowledge with the team and the company.

Understanding and Managing Variance:

OEE data is subject to variation like any other process in your operation.  What are the sources of variation?  If there is a constant effort to improve performance, then you would expect to see positive performance trends.  However, monitoring OEE and attempting to maintain positive performance trends can be a real challenge if the variances are left unchecked.

Availability

What if change-over times or setup times have been dramatically reduced?  Rather than setting a job to run once a week, it has now been decided to run it daily (five times per week).  What if the total downtime was the same to make the same number of parts over the same period of time?  Did we make an improvement?

The availability factor may very well be the same.  We would suggest that, yes, a signficant improvement was made.  While the OEE may remain the same, the inventory turns may increase substantially and certainly the inventory on hand could be converted into sales much more readily.  So, the improvement will ultimately be measured by a different metric.

Performance

Cycle time reductions are typically used to demonstrate improvements in the reported OEE.  In some cases, methods have been changed to improve the throughput of the process, in other cases the process was never optimized from the start.  In other instances, parts are run on a different and faster machine resulting in higher rates of production.  The latter case does not necessarily mean the OEE has improved since the base line used to measure it has changed.

Quality

Another example pertains to manual operations ultimately controlled through human effort.  The standard cycle time for calculating OEE is based on one operator running the machine.  In an effort to improve productivity, a second operator is added.  The performance factor of the operation may improve, however, the conditions have changed.  The perceived OEE improvement may not be an improvement at all.  Another metric such as Labour Variance or Efficiency may actually show a decline.

Another perceived improvement pertains to Quality.  Hopefully there aren’t to many examples like this one – changing the acceptance criteria to allow more parts to pass as acceptable, fit for function, or saleable product (although it is possible that the original standards were too high).

Standards

Changing standards is not the same as changing the process.  Consider another more obvious example pertaining to availability.  Assume the change over time for a process is 3o minutes and the total planned production time is 1 hour (including change over time).  For simplicity of the calculation no other downtime is assumed.  The availability in this case is 50% ((60 – 30) / 60).

To “improve” the availability we could have run for another hour and the resulting availability would be 75% (120 – 30) / 120.  The availability will show an improvement but the change-over process itself has not changed.  This is clearly an example of time management, perhaps even inventory control, not process change.

This last example also demonstrates why comparing shifts may be compromised when using OEE as a stand-alone metric.  What if one shift completed the setup in 20 minutes and could only run for 30 minutes before the shift was over (Availability = 60%).  The next shift comes in and runs for 8 hours without incident or down time (Availability = 100%).  Which shift really did a better job all other factors being equal?

Caution

When working with OEE, be careful how the results are used and certainly consider how the results could be compromised if the culture has not adopted the real meaning of Lean Thinking.  The metric is there to help you improve your operation – not figure out ways to beat the system!

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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OEE for Multiple Parts – Single Machine (Multipart Processes)

How to Calculate OEE for Single Machine and Multiple Parts.

Flexible manufacturing provides the advantage of producing many different parts on the same piece of equipment.  The same is true for processes such as stamping presses, molding machines, or machining operations.

The first question most often asked is, “How do we calculate OEE for a piece of equipment that is capable of manufacturing multiple parts?”  The overall OEE for a stamping press, molding machine, machining process, or other “multipart” process is easily calculated using the same formulas presented in our previous posts “How to Calculate OEE” and “Practical OEE“.

We presented three machines running at various rates and producing unique products.  We demonstrated how to calculate the OEE for each part individually and for all parts collectively.  The machines A, B, and C could very easily be parts A, B, and C running on one machine.  The application of the OEE formulas presented for these three machines is the same for multiple parts running on the same machine.

We have prepared two Excel spreadsheets that demonstrate how to calculate OEE for a single machine that produces multiple parts.  We have also created a separate Excel spreadsheet that will show you how to calculate OEE for Multiple Departments and Multiple Machines running Multiple Parts.

Calculating OEE for any period of time, department, or group of equipment is a simple task.  With the understanding that OEE measures how effectively Net Available Time is used to produce good parts at the ideal rate, the formula for any OEE calculation follows:

OEE (Any Category) = Total SUM of IDEAL Time / Total SUM of NET Available Time

Once this basic premise for OEE calculations is clearly understood, any combination of OEE summaries can be prepared including OEE summaries by Shift, Operator, Manager, Division, Process, and Process Type.

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.

Multipart OEE – Confronting the Challenges

Most manufacturing environments are challenged with the task of minimizing inventories requiring more frequent change-overs or setups.  By far, the greatest challenge of multipart equipment is managing the change-over process and is usually reflected in the OEE Availability factor.

We recommend including setup or change-over time as part of the unplanned downtime calculation.  Then, by definition, one method to improve Availability is to reduce change-over or setup time.  Reductions in change-over time will also be reflected by improved Availability.  The Availability factor is now a useful metric for tracking improvements.

According to our definition, change-over time or setup time is measured from the end of the current production run (“the last good part made”) to the start of the next production run (“first good part produced”).  We have worked with some manufacturers that decided to do change-overs on the off shift so that they could avoid the down time penalty.  They clearly didn’t get the point – deferring the time when the change-over is performed doesn’t change the time required to perform it.

Several programs such as SMED (single minute exchange of dies) are available and, when coupled with best practices for quick die change (QDC) or quick tool change techniques, can greatly reduce the time lost during your tool change events.

We will consider posting best practices for SMED or QDC and would welcome any reader comments in this area.

We always welcome your feedback and comments.  Feel free to send us your questions or comments to leanexecution@gmail.com

Until Next Time – STAY Lean!

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