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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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.
Please leave your comments regarding this or other topics. We appreciate your feedback. Also, remember to get your free OEE spreadsheets. See our free downloads page or click on the file you want from the “Orange” box file on the sidebar.
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.
We added a new page to our site to address some of the more frequently asked questions (FAQ’s) we receive regarding OEE. We trust you will find this information to be of interest as you move forward on your lean journey. We always appreciate your feedback, so feel free to leave us a comment or send an e-mail directly to LeanExecution@gmail.com or Vergence.Consulting@gmail.com
We have had an incredibly busy summer as more companies are pursuing lean manufacturing practices to improve their performance. OEE has certainly been one of the core topics of discussion. We have found that more companies are placing a significant emphasis on Actual versus Planned performance. It would seem that we are finally starting to realize that we can introduce a system of accountability that leads to improvements rather than reprimands.
Keep Your Data CLEAN
One of the debates we recently encountered was quantity versus time driven performance data when looking at OEE data. The argument was made that employees can relate more readily to quantities than time. We would challenge this as a matter of training and the terminology used by operations personnel when discussing performance. We recommend using and maintaining a time based calculation for all OEE calculations. Employees are more than aware of the value of their time and will make every effort to make sure that they get paid for their time served.
Why are we so sure of this? Most direct labour personnel are paid an hourly rate. Make one error on their pay or forget to pay their overtime and they will be standing in line at your office wondering why they didn’t get paid for the TIME they worked. They will tell you – to the penny – what their pay should have been. If you are paying a piece rate per part, you can be sure that the employees have already established how many parts per hour they need to produce to achieve their target hourly earnings.
As another point of interest and to maintain consistency throughout the company, be reminded that finance departments establish hourly Labour and Overhead rates to the job functions and machines respectively. Quite frankly, the quantity of parts produced versus plan doesn’t really translate into money earned or lost. However, one hour of lost labour and everyone can do the math – to the penny.
When your discussing performance – remember, time is the key. We have worked in some shops where a machine is scheduled to run 25,000 parts per day while another runs a low volume product or sits idle 2 of the 5 days of the the week. When it comes right down to the crunch for operations – how many hours did you earn and how many hours did you actually work.
Even after all this discussion we decided it may be an interesting exercise to demonstrate the differences between a model based on time versus one based (seemingly) only on Quantitative data. We’ll create the spreadsheet and make it available to you when its done!
Remember to take advantage of our free spreadsheet templates. Simply click on the free files in the sidebar or visit our free downloads page.
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.
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.
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.
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).
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?
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!
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.