We recently received an e-mail regarding OEE calculations for batch processes and more specifically the effect on down stream equipment that is directly dependent (perhaps integrated) on the batch process. While the inquiry was specifically related to the printing industry, batch processing is found throughout manufacturing. Our more recent experiences pertain to heat treating operations where parts are loaded into a stationary fixed-load oven as opposed to a continuous belt process.
Batch processing will inherently cause directly integrated downstream equipment (such as cooling, quenching, or coating processes) to be idle. In many cases it doesn’t make sense to measure the OEE of each co-dependent piece of equipment that are part of the same line or process. Unless there is a strong case otherwise, it may be better to de-integrate or de-couple subsequent downstream processes.
Batch processing presents a myriad of challenges for line balancing, batch sizes, and capacity management in general. We presented two articles in April 2009 that addressed the topic of where OEE should be measured. Click here for Part I or Click here for Part II.
Ideally, we want to measure OEE at the bottleneck operation. When we apply the Theory of Constraints to our production process, we can assure that the flow of material is optimized through the whole system. The key of course is to make sure that we have correctly identified the bottleneck operation. In many cases this is the batch process.
While we are often challenged to balance our production operations, the real goal is to create a schedule that can be driven by demand. Rather than build excess inventories of parts that aren’t required, we want to be able to synchronize our operations to produce on demand and as required to keep the bottleneck operation running. Build only what is necessary: the right part, the right quantity, at the right time.
Through my own experience, I have realized the greatest successes using the Theory of Constraints to establish our material flows and production scheduling strategy for batch processes. Although an in-depth discussion is beyond the scope of this article, I highly recommend reading the following books that convey the concepts and application through a well written and uniquely entertaining style:
In his book “The Goal“, Dr. Eliyahu A. Goldratt presents a unique story of a troubled plant and the steps they took to turn the operation around.
Another book titled “Velocity“, from the AGI-Goldratt Institute and Jeff Cox also demonstrates how the Theory of Constraints and Lean Six Sigma can work together to bring operations to all new level of performance, efficiency, and effectiveness.
I am fond of the “fable” based story line presented by these books as it is allows you to create an image of the operation in your own mind while maintaining an objective view. The analogies and references used in these books also serve as excellent instruction aids that can be used when teaching your own teams how the Theory of Constraints work. We can quickly realize that the companies presented in either of the above books are not much different from our own. As such, we are quickly pulled into the story to see what happens and how the journey unfolds as the story unfolds.
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We have learned that an industry standard or definition for Overall Equipment Effectiveness (OEE) has been adopted by the Semi Conductor Industry and also confirms our approach to calculating and using OEE and other related metrics.
The SEMI standards of interest are as follows:
SEMI E10: Definition and Measurement of Equipment Reliability, Availability, and Maintainability.
SEMI E35: Guide to Calculate Cost of Ownership Metrics.
SEMI E58: Reliability, Availability, and Maintainability Data Collection.
SEMI E79: Definition and Measurement of Equipment Productivity – OEE Metrics.
SEMI E116: Equipment Performance Tracking.
SEMI E124: Definition and Calculation of Overall Factory Efficiency and other Factory-Level Productivity Metrics.
It is important to continually learn and improve our understanding regarding the development and application of metrics used in industry. It is often said that you can’t believe everything you read (especially – on the internet). As such, we recommend researching these standards to determine their applicability for your business as well.
Best practices and methods used within and outside of your specific industry may bring a fresh perspective into the definition and policies that are already be in place in your organization. Just as processes are subject to continual improvement, so are the systems that control them. Although many companies use benchmarking data to establish their own performance metrics, we strongly encourage benchmarking of best practices or methods – this is where the real learning begins.
World Class OEE is typically defined as 85% or better. Additionally, to achieve this level of “World Class Peformance” the factors for Availability, Performance, and Quality must be at least 90%, 95%, and 99.5% respectively. While this data may present your team with a challenge, it does little to inspire real action.
Understanding the policies and methods used to measure performance coupled with an awareness of current best practices to achieve the desired levels of performance will certainly provide a foundation for innovation and improvement. It is significant to note that today’s most efficient and successful companies have all achieved levels of performance above and beyond their competition by understanding and benchmarking their competitors best practices. With this data, the same companies went on to develop innovative best practices to outperform them.
A Practical Example
Availablity is typically presented as the greatest opportunity for improvement. This is even suggested by the “World Class” levels stated above. Further investigation usually points us to setup / adjustment or change over as one of the primary improvement opportunities. Many articles and books have been written on Single Minute Exchange of Dies and other Quick Tool Change strategy, so it is not our intent to present them here. The point here is that industry has identified this specific topic as a significant opportunity and in turn has provided significant documentation and varied approaches to improve setup time.
In the case of improving die changes a variety of techniques are used including:
Quick Locator Pins
Standard Pass Heights
Standard Shut Heights
Quarter Turn Clamps
Dual Coil De-Reelers
Change Over Teams versus Individual Effort
Standardized Changeover Procedures
As change over time becomes less of a factor for determining what parts to run and for how long, we can strive reduced inventories and improved preventive maintenance activities.
The manufacturing community has been devastated by the recent economic downturn. We are challenged to bring out the best of what we have while continuing to strive for process excellence in all facets of our business.
Today was another day to do a little maintenance. We spent a little time revamping our look and feel. We hope you enjoy the changes and find our site a little easier to navigate. We updated our Free Downloads page to present another easier and more direct venue to get your files instantly using Box.Net. If you’re already familiar with WordPress, you know how great this widget is. Downloads could never be faster or easier.
We also took some time to update some of our pages. We would suggest, however, that the best detailed content appears in the individual articles that we have posted.
Upcoming Topics for 2009
Tracking OEE Improvements: We have noticed an increase in the number of requests to discuss tracking OEE improvements. We have been working on a few different approaches even for our own consulting practice and look forward to sharing some thoughts and ideas here.
How OEE can improve your Cost of Non-Quality. It’s more than yield.
What OEE can do for your Inventory. Improvements should be cascading to other areas of your operation – including the warehouse.
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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.
OEE is a great metric to help identify where you may be incurring losses in your processes or operation. As one of the goals of implementing a Lean strategy is to reduce costs, it only seems natural that we should be able to determine what processes to focus on that are driving the greatest losses.
From the example developed in our previous posts we determined that the OEE and related factors for our three processes were as follows:
Based on the OEE results, one would be inclined to take a look at Machine C as it has the lowest OEE. Is this really the greatest opportunity? The only way to answer the question is to understand what factors are driving costs and ultimately affecting profitability.
The performance factor for machine C is definitely pulling down the OEE for this process. What would you think if the machine is 100% automated (no labour) and the cycle time, although it may be less than standard, is still meeting the takt time to meet customer demand? Is there really a cost? Of course there is, but the impact to your business may be minimal in terms of cost when compared to the other machines.
It is clear that we need to develop a model to understand what losses and ultimately costs are associated with each of the factors. In turn, we will be able to better understand the overall OEE.
What costs do we consider? We recommend keeping the model simple. There are typically three cost components associated with any given process or product: Material, Labour, and Overhead. Burden is another term used for Overhead and we will use these terms interchangeably.
Our goal over the next few posts will be to develop a simple cost model for each process and, in turn, determine which one may be the process of choice for improvement. For now, we will provide a general discussion of some of the potential cost considerations.
Improving quality typically yields the greatest return on investment because all of the cost elements stated above are impacted by the Quality factor. Raw material, Labour, and Burden are all expended to produce a part scrap part.
The costs associated with Quality losses are further challenged when considering the number of parts that would have to be produced in order to recover these lost costs. If you are lucky enough to enjoy a 10% profit margin (clear), then, at a minimum, 10 parts would have to be produced for every part scrapped. Of course, more parts would have to be produced to recover other infrastructure costs incurred including documentation, record keeping, and scrapping of the actual parts.
Performance losses typically affect labour and overhead. Labour losses are easy enough to understand. If a machine is operator dependent, then we will have to pay a person to stand at the machine to run it. If it is running slowly, more costs are incurred to cover the additional labour time.
In many cases, direct losses related to overhead are sometimes difficult to assess unless a truly activity based costing system is in place. The reason for the complexity arises because some of the costs are “fixed”. Because the equipment exists, expenses such as depreciation or property taxes are incurred whether or not the equipment or, for that matter, the plant is running. The performance of the machine or any of the other factors for that matter won’t change this fact.
Availability then becomes somewhat more obscure when it comes to calculating hard costs. If the labour can be redeployed to another process when a machine goes down, perhaps some of the labour losses can be avoided. If not, then waiting for a machine to be repaired or material to be delivered is a real loss that should be addressed.
Intangible costs are also difficult to quantify but we should be aware of their existence. The costs associated or related to poor OEE may include overtime, expedited freight, and infrastructure costs related to extra handling of material or management of non-conforming material (containment, extra inspection, rework, and scrap). Although this is a relatively short list, it addresses the most obvious potential losses. With a little more thought, the list could easily grow longer.
Other key metrics in your facility such as customer delivery or quality performance indicators may also point to problems that can be traced directly to poor OEE performance. Although difficult to measure, a company’s competitive position is compromised when efficiencies are low and eventually the costs of poor performance make their way into the “burden” costs required to manage the operation.
While OEE is an effective metric for operations, on its own, it does not provide a direct indicator of real financial losses. As Lean Practitioners we are challenged to provide an analysis that not only improves the metrics of the business but also translate into real financial improvements on the balance sheet and ultimately – the bottom line. We would suggest that OEE is a time driven metric (asset time management strategy) versus our proposed COEE which is Finance or “Value” driven (cost management strategy). We are presently developing a model that will allow your OEE data to be sensitized with cost data as demonstrated by the table below.
We have coined the term COEE or Cost of Overall Equipment Effectiveness. Consider the following OEE results converted to Cost based drivers using standard costs as our baseline. The sample data and spreadsheet used to calculate this data will be available as a download soon. The overall spreadsheet is quite large and based on a fully detailed three shift operation.
Our OEE cost model clearly presents the real costs or “losses” incurred per part. Our Weighted OEE Cost Model will change the way you view OEE data, enabling you to set priorities and identify real, quantifiable, opportunities for improvement. The above snapshot represents the goal of our COEE project – a clean, clear, summary of the losses incurred correlated directly to your OEE index. Another advantage is that the Availability, Performance, and Quality factors are recalculated based on cost and presents a realistic breakdown of losses for each of these factors from a financial perspective. Our spreadsheet presents an advanced OEE example that will bring real value to your OEE implementation strategy.
NOTE: The fully developed spreadsheet is available from our FREE Downloads page or from the FREE Downloads box on the sidebar.
A well implemented OEE strategy should become evident on the balance sheet through improved material utilization, reduced labour variance (straight and overtime reductions), reduced scrap costs, reduced rework costs, and other burden account reductions.
Take quick, effective, and efficient action to solve the problems having the greatest financial impact to your business. Last but not least, don’t confuse activity with action. Decisions are not actions and talking about a problem or even writing about it could be construed as activity. Real actions produce real, measurable, results.
Change requires Change. Profit is to business as oxygen is to humans – you need it to survive.
We have created a number of Excel spreadsheets that are immediately available for download from our FREE Downloads page or from the Free Downloads widget on the side bar. These spreadsheets can be modified as required for your application. There are no hidden files, formulas, or macros and no obligations for the services provided here.