We are often asked what companies (or types of companies) are using OEE as part of their daily operations. While our focus has been primarily in the automotive industry, we are highly encouraged by the level of integration deployed in the Semiconductor Industry. We have found an excellent article that describes how OEE among other metrics is being used to sustain and improve performance in the semiconductor industry.
Somehow it is not surprising to learn the semiconductor industry has established a high level of OEE integration in their operations. Perhaps this is the reason why electronics continue to improve at such a rapid pace in both technology and price.
The article clearly presents a concise hierarchy of metrics (including OEE) typically used in operations and includes their interactions and dependencies. The semiconductor industry serves as a great benchmark for OEE integration and how it is used as powerful tool to improve operations.
While we have reviewed some articles that describe OEE as an over rated metric, we believe that the proof of wisdom is in the result. The semiconductor industry is exemplary in this regard. It is clear that electronics industry “gets it”.
As we have mentioned in many of our previous posts, OEE should not be an isolated metric. While it can be assessed and reviewed independently, it is important to understand the effect on the system and organization as a whole.
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As we are all aware, inspecting or measuring parts does not change the quality of the product. Likewise, measuring and reporting OEE alone does not solve problems or improve performance. While it is fair to say that increased focus and measurement of any process usually results in some degree of improvement, these are typically attributed to changes in human behavior due to observation and not necessarily real process improvements.
Using OEE to identify opportunities in your operation is the equivalent of turning the light on in a dark room. Although the room hasn’t changed, we certainly have a better understanding of what it looks like. As such, OEE is a vantage point metric that can be used to illuminate our understanding of the process and identify opportunities to drive improvements.
It is essential for your team to develop and utilize effective problem solving skills to successfully identify systemic and process root causes for failure and to develop and execute permanent corrective actions to resolve them. Our experience suggests that the lack of solid and proven problem solving skills coupled with poor execution is the leading cause of failure for new initiatives such as OEE.
We introduced an approach to improving OEE in our “Improve OEE: A Hands On Approach“, post (03-Jan-09). Although we identified some of the tools that could be used to solve of the problems, we didn’t spend much time going into the details. Over the next few posts, we’ll discuss some of the ideas in a little more detail.
The real problem for most companies is identifying what the real underlying root cause of the current “failure” mode is. Without a good understanding of the root cause, the solutions developed and implemented will not be effective, only serving to temporarily cure the immediate superficial symptoms.
Using effective problem solving skills to analyze the OEE data and to develop and execute permanent corrective actions will assure sustainable and ever improving performance.
We have stated that policies and procedures will have an impact on your OEE implementation strategy. One reader commented on Part I of this post stating that “OEE should be measured at the ‘design’ bottleneck process / piece of equipment that sets the pace of the line.” While this is certainly an effective approach, the question is whether or not company policy or procedure supports the measurement of OEE in this manner. Nothing is as simple as it looks. Take this to the boardroom and see what kind of response you get. We’re flexible.
As such, this becomes yet another consideration for what is being measured, how the data going to be used, and what is the significance of the results. While we didn’t elude to a multi-series post, the comment was indeed timely. The risk of not understanding the data could result in other inefficiencies that are built into the process that could mask either upstream or downstream disruptions.
Inventory – Hiding Opportunities
Whenever we think of the “bottleneck”, we instantly turn to the Theory of Constraints. The objective is to ensure that the bottleneck operation is performing as required – no disruptions. In many cases, process engineers will anticipate the bottleneck and incorporate buffers or safety stock into the process to minimize the effect of any potential process disruptions.
On one hand, the inventory, whether in the form of off-line storage or internalized, by using a buffer (or part queue), will in essence minimize or eliminate the effects of external disruptions. On the other hand, there is a premium to be paid to carry the excess inventory as well.
While buffers or part queue’s can serve as a visual indicator of how well the process is performing, assuming the method used to calculate the queue quantities is correct, our previous post was eluding to the fact that many manufacturers incorporate contingency strategies into the process after the fact such as inventory that was not part of the original process design or reworking product on line.
Incorporating a rework station as part of the manufacturing process because the tooling or equipment is not capable of producing a quality part at rate may eventually be absorbed as part of the “normal” or standard operating procedure. As such, it is important to manage standardized operating procedures in conjunction with Value Stream Maps to avoid degradation from the base line process.
OEE can serve as an isolated diagnostic tool and as a metric to monitor and manage your overall operation. Company policy should consider how OEE is to be applied. While most companies manage OEE for all processes, they are typically managed individually. Many companies also calculate weighted department, plant, and customer driven OEE indices.
Regardless of the OEE index reported, it is important to understand the complexities introduced by product mix and volumes when considering the use of a weighted OEE index. The variability of the individual OEE factors compounds the understanding of the net OEE index even more.
We have provided FREE Files for you to download and use at your convenience. A detailed discussion is also provided in our OEE tutorial. See the “FREE Files” BOX on the sidebar.
We have received several topic requests that we will work on for the month of March, 2009. If you have a topic that you would like to see featured on our site, send an e-mail to LeanExecution@gmail.com.
OEE on the Shop Floor – Measurement: What should we be measuring to make OEE practical at the shop floor level. What factors are critical to the person or persons doing the work? We have presented the pros and cons of various systems that are used today. We would suggest that the number of solutions is as varied as the number of companies seeking them. A customized solution for your specific business operation is likely the best option. A tailored solution is not necessarily a costly one.
OEE Innovations – TRIZ: Ultimately the reason for measuring OEE is to make improvements in capacity utilization. TRIZ is a very valuable tool that can be used to bring new and innovative solutions to improving your OEE. Many companies are likely unaware of the TRIZ process as so much focus is placed on LEAN and Six Sigma. Combining these disciplines with TRIZ can yield a highly successful solution that may just be the next generation ideal.
Capacity Planning with OEE: By definition, it only makes sense to use OEE as an integral part of your capacity planning process. We will cover the details to do this effectively. Effective capacity planning naturally extends to improved resource management and effective production planning.
OEE, Value Streams, and COST: Although some managers may rise to the challenge and volunteer, many are either assigned or designated to be project champions. In many cases, unfortunately, the scope of the project is extremely limited or restricted and project managers simply become “metric managers”. Who is in charge of OEE? The answer is quite simple: EVERYONE. OEE is a multi-discipline metric and, like other sound lean strategies, requires seamless interaction among managers and departments.
OEE cannot and should not be managed as an independent metric. Having said that, don’t get caught in the trap of “stand alone” OEE reviews. While there may be a number of strategies for improving OEE, such as constrained capacity, we will present a model that explicitly ties operational costs to your processes. When OEE data is sensitised by cost data, a completely different strategy for improvement will emerge. If the ultimate goal is to improve your bottom line, then our Cost sensitisation model will bring the concept of OEE and your bottom line to a whole new level.
OEE and Lean Agility: Can OEE be a leading indicator of your ability to respond to change? Well we think so and happen to have a few ideas that will show you how and why.
Send us your questions or comments or simply suggest a topic for a future post or article.
Incorporating and tracking performance using Lean metrics doesn’t make a company Lean any more than tracking weight puts you on a diet. Measurements are like decisions, nothing changes unless actions are taken.
How does this apply to OEE? How does OEE apply to LEAN? As we’ve mentioned in previous posts, many companies invest a significant amount of time, money, and effort to develop exorbitant OEE data collection systems. Data collection and analysis methods are in place, improvement / action plans are developed and executed, and the measurement cycle continues.
To some, this process may appear to be correct. More formally, a Plan-Do-Check-Act (PDCA) or Define-Measure-Analyze-Improve-Control (DMAIC) process improvement methodology may be used. So what is wrong with this picture?
OEE Improvements are RELATIVE
OEE does not distinguish between poorly designed and well designed processes. A poorly designed process may have significant flow constraints and excessive labour but still yield a high OEE index. The reason for this is simple, the base line or process standards are based on the current known process. Standard cycle times and quality expectations are based on the current “achievable” performance standards that the process can provide.
Changes to the current process, rates, and quality levels will be reflected in the OEE index. However, LEAN is not necessarily concerned with effective asset utilization. The focus of LEAN is to increase or optimize the value added to the product or service being provided while reducing the time required to achieve it.
Implementing OEE is not LEAN
Racing cars and regular street cars may each perform at 100% of their optimum performance levels but clearly they could not compete in a race against each other. From a LEAN perspective, the racing car will certainly out-perform a regular street car in a head-t0-head speed contest.
Similarly, OEE can provide insight into the performance of the current process, however, it does not provide an indication of how LEAN the process actually is. A process that is plagued with multiple stations and inherent Work In Progress inventory will never compete against a properly balanced single piece flow process.
The OEE index for any group of processes may be above 85% as defined by design, it doesn’t mean they are equally lean. Lean should aspire us to achieve a 100% value added process, safely producing the highest quality product in the shortest amount of time. Although this could never be achieved in the today’s manufacturing environment, VALUE STREAM mapping is the technique used to evaluate our current capabilities in this regard and to determine what a lean future state process could achieve.
So why measure OEE?
OEE measures how effectively an asset or group of assets is being utilized as defined or described by the current standards and process constraints. Of course, we want to make sure that we are utilizing our assets effectively. The message here is simple. Don’t confuse effectiveness with efficiency – they are not the same.
Even efficiency can mean different things to different people. As we’ve mentioned in previous posts, understand WHAT you are measuring and WHY. Metrics don’t make a company LEAN although many can help you achieve increasing levels of LEAN.
OEE is an excellent tool to help manage and improve our processes and even more so when the process is optimized using LEAN principles.
The next time someone says they are going LEAN, listen closely. Usually the statement is met with the typical, “We did 5S and we’re working to improve our OEE.” The real LEAN practitioner may just share the plan to reduce the cash to cash time and increase or improve the percentage of Value Added activity.
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:
To ensure the current performance levels are sustained,
To identify new opportunities for improvement,
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:
Historical data for OEE and each factor (Availability, Performance, and Quality)
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:
Collect and analyze data from the current process
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.
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.
Data collection in “real time”
Anyone can add to the List
Anyone can update the List
Readily Available to ALL
Writing Skills ONLY
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.
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?
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
Confirm company policy and methods for calculating the cycle time.
Confirm the Cycle Time or Production Rate (Time Study)
Compare the Actual versus Standard Operating Procedure
Review the process performance history and data records.
Process Type: Automation, Semi-Automation, Manual (Human Effort)
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:
Review Process Failure Modes Effects and Analysis (PFMEA)
Review Current Quality Control Plans (Inspection Requirements)
Review and Analyze Quality Performance Data
Review scrap and rework analysis
Identify Top Opportunities (Pareto Analysis)
Initiate Problem Solving Activities (DMAIC, PDCA, PDSA, IDEA Loops)
Execute problem solving strategy
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.
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?
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.
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:
Shift start-up meetings
Employee Communication Meetings
End of Shift clean up periods
Quality first off approval process
Shift first off versus Run first off
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.
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.
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:
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:
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.
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.
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.
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.