Introduction to Overall Equipment Effectiveness

Introduction to Overall Equipment Effectiveness(lean Manufacturing)There are several ways you can optimize your process to improve profitability. But it can be difficult to understand the overall effectiveness of a complex operation so you can decide where to make improvements. Overall Equipment Effectiveness (OEE) can help. Learn how you can use this structured metric to evaluate the health and reliability of your process and equipment.

Introduction to Overall Equipment Effectiveness 5 minutesOverview

How efficient is my process?In today's economy, you're expected to continuously improve your Return On Total Capital. And as capital to build new, more efficient plants becomes more difficult to obtain, you often have to meet growing production demands with current equipment and facilities while continuing to cut costs.

There are several ways you can optimize your processes to improve profitability. But it can be difficult to understand the overall effectiveness of a complex operation so you can decide where to make improvements. That's especially true when the process involves multiple pieces of equipment that affect each other's effectiveness.

One metric that can help you meet this challenge is Overall Equipment Effectiveness, or OEE. OEE measures the health and reliability of a process relative to the desired operating level. It can show you how well you're utilizing resources, including equipment and labor, to satisfy customers by matching product quality and supply requirements.

This course provides a brief introduction to key concepts of OEE. The four courses that follow provide more detail on individual components of OEE, as well as sample calculations.

A short quiz at the end of the course helps you review what you've learned and earn valuable Reward Points.

Hint: As you go through the topics in this course, watch for answers to these questions: What are the three factors used in calculating OEE? Which groups in a plant can use the results of OEE calculations?

Ready to start? Just click the ">"icon below.

Next: What does OEE measure? >

What does OEE measure?

Overall Equipment Effectiveness (OEE) measures total performance by relating the availability of a process to its productivity and output quality.

OEE addresses all losses caused by the equipment, including

not being available when needed because of breakdowns or set-up and adjustment losses

not running at the optimum rate because of reduced speed or idling and minor stoppage losses

not producing first-pass A1 quality output because of defects and rework or start-up losses.

OEE was first used by Seiichi Nakajima, the founder of total productive maintenance (TPM), in describing a fundamental measure for tracking production performance. He challenged the complacent view of effectiveness by focusing not simply on keeping equipment running smoothly, but on creating a sense of joint responsibility between operators and maintenance workers to extend and optimize overall equipment performance.

First applied in discrete manufacturing, OEE is now used throughout process, batch, and discrete production plants.

Next: Calculating OEE >

Calculating OEE

OEE is calculated by multiplying three factors: availability, productivity, and quality.

% OEE = ( % Availability ) * ( % Productivity ) * ( % Quality )

The values used can reflect an entire processing plant, a process line, or an individual piece of equipment.

For individual equipment, the performance of the equipment is compared to earlier values for the same equipment or to similar pieces of equipment. Changes in OEE or its elements are tracked and trended over time.

OEE for a process line treats the entire line as a single unit, regardless of how much equipment it includes. For multiple-recipe or batch operations, OEE is calculated for each product produced.

Like a process line, a process plant performs as a whole, and OEE is therefore calculated for the entire plant as a unit.

Next: Putting OEE to work >

Putting OEE to work

The OEE calculation provides focus and simplicity to aid in decision making. It can help you

Identify areas for improvement

Assess incremental revenue opportunities

Benchmark your operation against similar or competitor processes

For example, by tracking the factors that determine OEE, you can determine whether your equipment experienced more downtime (planned or unplanned) than expected, or was running at a slower pace or with minor stops, or produced more defects.

Root cause analysis begins by focusing on the type and extent of loss, not the OEE percentage rating itself. Both Operations and Maintenance should be involved in making improvements whether reducing unplanned downtime, increasing process productivity, or improving product quality.

Published benchmark values for the factors of OEE are also excellent indicators of a process's competitiveness in the market. For example, when measuring Overall Equipment Effectiveness for the first time, process plants may find they are only achieving around 40%-70% OEE (batch) or 50%-80% (continuous process). International best practice figures are recognized to be +90% (batch) and +95% (continuous process).

World-class Overall Equipment Effectiveness

Availability>90%

Productivity>95%

Quality>99%

OEE>85%

Source: Nakajima

Next: Use the quiz to review and earn valuable Reward Points >

The following questions will help you review what you learned in this course.

Each question has only one correct answer. You'll earn a valuable Reward Point for each one you answer correctly.

Ready? You'll find the first question on the next page. >

Availability

Overview

How can I measure and improve availability?Availability is simply a way to quantify how much of the time your equipment or process is up and running as it should. The higher the availability, the more you can produce and the greater your Return on Assets.

Your goal, therefore, is to minimize downtime especially unplanned downtime by improving process and equipment reliability. This course provides an overview of availability as a factor in OEE.

A short quiz at the end of the course helps you review what you've learned and earn valuable Reward Points.

Hint: As you go through the topics in this course, watch for answers to these questions: What availability level is reasonable for processes like yours? How does unplanned downtime affect revenue and profitability? How is equipment availability calculated?

Ready to start? Just click the ">"icon below.

Next: Availability benchmarks >

Availability benchmarksEven the best operations have some downtime. What makes them the best is keeping availability as high as possible.

Here are some typical availability values to benchmark your own process against.

QuartileProcess TypeWorst

3rd

2nd

Top

Continuous

91%

Batch

90%

Chemical, Refining, Power

95%

Paper

94%

Source:Fluor Global Services Benchmark study NA, AP, EU 1996For large complex assets or fleets of capital equipment, availability typically runs between 85%-95%.

The 5%-10% of non-availability is split between "planned downtime" (scheduled maintenance) and "unplanned downtime" (breakdowns).

Next: Unplanned downtime >

Unplanned downtime

Equipment wear and tear can degrade performance and therefore production. Performing normal maintenance and repairs during scheduled shutdowns allows for proper planning and minimal lost time while restoring equipment performance.

The loss is greater, however, when a unit shuts down unexpectedly especially because you often have to find the problem before you can fix it.

Not only do you lose production time; in many cases the problem also affects quality and production rate before and after the outage.

Unplanned downtime has high fixed and variable costs. One of the largest impacts is revenue loss resulting from demand exceeding supply. The cost is not just the loss of profit margin on the lost revenue, but also the value of the total revenue lost less the direct avoided costs of production such as materials or energy.

Another impact of unplanned downtime is environmental costs for off-spec or waste product. Safety and regulatory compliance could also be a large factor.

The costs of returning to normal operations also must be considered. These could include overtime for emergency repairs, airfreight for materials or spare parts, and loss of customer goodwill.

For these reasons, reducing or eliminating unscheduled outages offers the opportunity for dramatic improvements in profitability.

Next: Improving availability >

Improving availability

Understanding the failure rate of various pieces of equipment is key to preventing unscheduled downtime.

You can improve component availability through early detection of variances or irregularities in the equipment, and by providing condition-based real-time maintenance.

Adopting these predictive maintenance strategies especially for high-priority equipment can often help you identify problems before they affect production.

Benefits include significantly reducing downtime caused by equipment failure, as well as avoiding the higher repair costs of unexpected catastrophic failures.

Predictive maintenance also reduces the need to schedule downtime for preventive servicing, which guarantees increased availability.

You can learn more about this topic in the PlantWeb University course series on applying streamlined maintenance practices.The PlantWeb advantage

Fault detection and monitoring capabilities available with PlantWeb architecture can not only help reduce the number of plant shutdowns, but also provide valuable data that can be used in pre-emptive maintenance and scheduling.

Our intelligent field devices have extensive diagnostics, including PV status, that integrate smoothly with our AMS Suite software. More information is available on true plant status, which leads to faster troubleshooting.

Next: Calculating availability >

Calculating availability

Equipment availability isn't just assumed to be the length of the shift in which it is operated. Instead, it's based on actual operating time, as a percentage of the possible production time.

% Availability = Actual production time

Possible production time

Here's an example:

A process line is operated 24 hours a day, 5 days a week (120 hours). Planned downtime for preventive maintenance is 1 hour each week. Unplanned downtime due to equipment failure and equipment adjustment is 7 hours.

% Availability = (120 - 1 - 7)

(120 - 1)

= 112

119

= 94%

Next: Use the quiz to review and earn valuable Reward Points > ProductivityOverview

How can I improve OEE by increasing productivity?While the availability portion of Overall Equipment Effectiveness describes the percentage of available operating time that equipment is actually running, productivity measures how much is produced during that run time.

Many process plants are capable of higher productivity than they currently achieve. The difference between current and potential productivity is an opportunity to increase output and profits.

This course covers some of the causes of low productivity in process plants, an approach for improving productivity, and how to calculate the results as part of Overall Equipment Effectiveness.

A short quiz at the end of the course helps you review what you've learned and earn valuable Reward Points.

Hint: As you go through the topics in this course, watch for answers to these questions: Why might operators decide not to use the optimum setpoints for a process? What is the role of process control in improving productivity? How is productivity calculated?

Ready to start? Just click the ">"icon below.

Next: What causes low productivity? >

What causes low productivity?

Possible reasons for low productivity include poor quality materials that can only go through the equipment at slower speeds, or lack of operator expertise because of poor training. A process may be run slowly to match the supply of materials or parts from a preceding process, or to reduce the perceived risk of equipment breakdowns.

In many cases, no matter what control strategy is implemented, operators will set the individual process variable setpoints at "safe" but not necessarily optimum targets. Until operators are confident the regulatory control system is capable of safe and reliable operation at or near process limits this "operator safe factor" can reduce throughput and therefore productivity.

For example, operators may be reluctant to let the control system set a control valve at its design position. Instead, they will leave the valve at a very conservative position. This conservatism can reduce attainable throughput by as much as 5%. The PlantWeb advantage

The device diagnostics and the AMS Suite software in PlantWeb architecture can help avoid this problem by

accurately reporting the actual valve position

ensuring that the correctly-sized valve is in use

diagnosing hysteresis and other valve problems

signaling if the loop is off control.

This generally increases the operators' confidence in the correct operation of the valves, so they'll be more likely to allow the process to run closer to optimum.

Next: Improving productivity through process optimization >

Improving productivity through process optimization

One strategy for addressing low productivity is to carry excess production capacity for example, by building a plant slightly larger than necessary so product can be inventoried to cover unplanned downtime, or by carrying spare units to replace those that fail. However, this strategy is costly because of the capital to purchase the additional capacity, as well as the added maintenance expenses associated with a larger facility.

A better approach is optimize the process so that it runs as smoothly and productively as possible.

Process optimization helps ensure that the equipment throughout the plant is working as effectively as possible over the entire control range. Load changes and setpoint changes are controlled with optimal effectiveness, and unplanned upsets are minimized.

Data analysis and statistical tools available for process optimization make it possible to effectively diagnose and troubleshoot poor performance, whether from improper controller tuning or poorly designed or maintained process equipment. They are great tools to determine where to spend capital dollars. For the best return, look at economic optimization of the entire unit, rather than just productivity optimization of a particular piece of equipment.

Whether you use large-scale, model-based advanced process control software, or a small-scale modular approach to process optimization, solid process control is the foundation for success. When process control is no longer a constraint, bottlenecks caused by other factors show up more clearly so you can focus attention and efforts where they're most needed to improve productivity.

Next: Calculating productivity >

Calculating productivity

Productivity can be calculated by looking at the actual output produced by the equipment as a percentage of the theoretical output, given its optimum speed and actual running time.

Here's an example:

The sustained capacity of a plant is 600,000 tons per year. Last year it produced 560,000 tons.

% Productivity = actual production

optimum capacity

= 560,000 tons

600,000 tons

= 93%

Next: Use the quiz to review and earn valuable Reward Points > Quality

How can I improve the quality factor in Overall Equipment Effectiveness?The third factor that affects profitability is product quality the percentage of "on-spec" output produced during the first pass through the production sequence.

This course examines ways to improve quality by improving process variability. A short quiz at the end of the course helps you review what you've learned and earn valuable Reward Points.

Hint: As you go through the topics in this course, watch for answers to these questions: What are the major sources of process variability? How can intelligent field devices help reduce variability and improve quality? How is the quality factor in OEE calculated?

Ready to start? Just click the ">"icon below.

Next: Process variability >

Process variability

Although a number of factors such as raw-material quality can affect product quality, one of the greatest opportunities for improving quality is by reducing process variability. The more consistently your process operates, the less profit-draining scrap and rework you'll have deal with.

Fortunately, you can reduce variability by using

intelligent field devices

improved regulatory control

advanced process control.

We'll look at each of these in turn.

Next: Intelligent field devices >

Intelligent field devices

Naturally, the accuracy of field devices such as sensors, transmitters, and control valves affects process variability. You can't maintain product quality if you can't accurately measure or control what's happening in the process.

But intelligent field devices those with capabilities beyond single-variable measurement or control offer ways to reduce process variability that go beyond general improvements in accuracy.

These additional benefits include:

Improving device stability so the desired performance is maintained over extended periods of time and changing field conditions

Reducing the response time to generate a representative process variable signal.

Providing diagnostics to help detect problems before they affect product quality

Next: Improved regulatory control >

Improved regulatory control

A regulatory control system can help you produce a uniform product that consistently meets customer quality demands at the lowest cost. It does this by minimizing variance throughout the processing cycle whether that variance is caused by changing feedstock quality, ambient conditions, equipment performance, or a host of other factors.

Without an effective regulatory control system, each successive unit operation can introduce variation that can accumulate throughout the process. The cumulative variation is reflected in final product quality and the overall cost of production.

Industry studies indicate that 20 - 40% of process controllers are operated in manual mode, missing the opportunity to reduce variability through automated control.

Studies have also shown, however, that more than 30% of the loops that are automated loops actually increase variability over manual control because of poor tuning. Many of these loops have equipment problems, including oversized and undersized valves; excessive hysteresis, resolution, or stick-slip in the valves; and measurement problems.

The enhanced functionality and performance of intelligent field devices help minimize these problems, allowing operators to turn on "auto" control. Easy access to device data enhances loop inspection capabilities to eliminate factors affecting variability in a control loop and ensure the reliability of the field measurements. Critical control loops can now be effectively tuned to achieve the next level of additional revenue generating opportunities.

Advanced control systems control the process as each variable relates to overall productivity or effectiveness. These systems are not single-loop controls, but a multi-variable envelope representing the constraints of pressure, temperature, and other factors. Within the envelope, the process is continuously maximizing effectiveness.

Advanced control systems run continuously; responding to changes, reducing the impact of upsets, and exploiting opportunities to create more profit. They are especially valuable where production targets or the quality and availability of raw materials can all change relatively quickly, so that the operating constraints and the scope for improvement vary from day to day.

Properly tuned control loops are also vital for Advanced Process Control (APC) to function effectively. The reliability and performance of the field device is the most significant contributor to implementing and optimizing APC.

Plants that are not properly maintained and monitored can show significant performance degradation in APC initiatives. Diagnostics and maintenance data helps keep field device performance and availability at the levels necessary to maintain long-term quality benefits.

Next: Calculating quality rate >

Calculating quality rate

The quality rate used in OEE calculations is defined as:

% Quality = Product produced - (scrap & rework)

Product produced

For example, a plant produced 550,000 tons of product, but only 485,200 tons met specifications on the first pass.

% Quality = 550,000 - (550,000-485,200)

550,000

= 550,000 - 64,800

550,000

= 485,200

550,000

= 88%

Next: Use the quiz to review and earn valuable Reward Points

How does PlantWeb improve OEE?Overview

Now that you've seen how availability, productivity, and quality each affect profitability, let's look at how they combine to measure Overall Equipment Effectiveness and how using Emerson's PlantWeb architecture can improve results.

To do that, this course uses the case of an example plant. We'll evaluate the plant's performance and PlantWeb's impact on each of three factors, as well as the cumulative effect on OEE.

A brief quiz at the end of the course helps you review the key points we cover and earn valuable Reward Points.

Hint: As you go through the topics in this course, watch for specific ways that PlantWeb helps improve availability, productivity, and quality.Ready to start? Just click the ">"

Plant backgroundThe example petrochemical plant has a low-density, high-pressure tubular polyethylene plant, a vinyl chloride monomer plant, and an emulsion and a suspension PVC plant, along with a fuel and steam system.

Ethylene and chlorine feedstocks, mainly from local sources, total 670,000 tons per year. Feed costs are $254.5 million, or $379/ton.

Yearly production is 650,000 tons, valued at $540 million. Gross operating margin is $245 million or 45% of revenue.

The plant's management has several specific concerns, including

Margins are eroding as new capacity with newer process technology comes onstream overseas.

Production of higher-quality product that will open up more markets is difficult because of high variability and off-spec production.

Downtime is affecting profitability: Scheduled downtime averages 9 days per year, and unscheduled outages average 11 days per year.

Production loss due to trips and variable constraints averages 60,000 tons of feed per year.

During extremely hot weather, the steam header pressure letdown valve constrains operation of the feed compressor turbines in the polyethylene unit. The operators don't like to run the valve near its limit, because if it does go off control most of the units will be affected and trips may occur.

Production is limited by the amount of stripping steam that can be forced into the continuous VCM recovery stripper of the emulsion plant. If steam flow rate is set higher than 95%, the controller becomes unstable. To make their life easier, the operators leave this at an average of 91%.

Next: PlantWeb architecture >

PlantWeb architecture

The benefits of using PlantWeb architecture in the example plant reflect incremental improvements from using PlantWeb instead of a traditional mixture of DCS/PLC and direct-wired instrumentation.

The example calculations assume that PlantWeb is used for all critical instruments (those that on failure will immediately cause the process to start moving away from the desired operating point), as well as all non-critical measurements and modulating valves.

The PlantWeb architecture includes DeltaV and Ovation process automation systems and AMS Suite application software for the whole plant, along with Emerson field devices with FOUNDATION fieldbus communications or HART communications.

Next: Availability >

Availability

Without PlantWeb, availability is calculated as follows:

Possible production time =(365 - 9) days

=356 days

Actual production time =(365 -9 - 11) days

=345 days

Availability =Actual production time

Possible production time

=345 356

=97%

The 3% lost availability represents expensive product going to flare or being downgraded to scrap.

Improving availability with PlantWeb. PlantWeb's fault detection and monitoring capabilities can not only reduce the number of plant shutdowns, but also provide valuable data that can be utilized to provide pro-active maintenance and scheduling.

PlantWeb can help improve availability through:

Use of intelligent field devices with diagnostics integrated with AMS Suite: Intelligent Device Manager to minimize unnecessary maintenance. More information on true plant status also leads to faster troubleshooting.

A single user interface for HART, analog, and FOUNDATION fieldbus instruments also makes troubleshooting easier

Improved instrument reliability.

These same features and tools can also reduce the turn around time during planned shutdowns. When applied to the example plant, the downtime can be reduced by 15%.

Downtime reduction with PlantwebUnscheduled shutdowns =15% of 11 days

=1.65 days reduction in forced outages

Scheduled shutdowns =15% of 9 days

=1.35 days

Downtime reduction =1.65 + 1.35 days

=3 days

ProductivityWithout PlantWeb, the plant's productivity on an annual basis is 90.8%:

% Productivity =

actual productionoptimum target production

=

650,000 tons - 60,000 tons 650,000 tons

=

590,000650,000

= 90.8%

Better results with PlantWeb. PlantWeb can help the example plant improve capacity utilization, for total benefit of $3,365,000/year.

The operator will attempt to keep the plant at the presently limiting set of constraints. It's inevitable that the operator will be cautious, and there will always be a differential between the actual operation and optimum productivity.

PlantWeb can help the operator move closer to that optimum. Device diagnostics and AMS Device Manager software help by accurately reporting the actual valve position, by ensuring that the correctly-sized valve is in use, by diagnosing hysteresis and other valve problems, and by signaling if the loop is off control. These all increase the operator's confidence in the correct operation of the control valves.

With less process variability, improved accuracy, and more standard and reliable controls, the operator will also feel confident moving closer to other plant constraints.

For example, the steam header letdown valve constraint affects most of the units in the example plant. The letdown valve is generally put at a conservative limit, affecting throughput of plants where compressors are the bottleneck during hot weather, as well as steam feed to process heat exchangers. With PlantWeb the letdown valves are assumed to move from their present average equivalent 85% throughput to 87.5% while still giving operators steady control.

Other constraints that are relieved by PlantWeb concern the VCM monomer feed transfer valves, the level on the reflux condensers in the suspension plant reactor, the temperature measuring devices that the operator uses to control the reactor throughput and melt index in the high pressure polyethylene reactor, and the ethylene compressor discharge pressure.

Advanced control technology typically permits a 2% increase in the feed rate to units that are required to operate at their maximum capacity. In this example, the steam letdown valve limit is used to calculate the improved plant throughput using PlantWeb. Calculations for the other process constraints are similar.

The steam letdown average is increased by 1%, from 85% to 86%. If the operational range of the valve is 65% (30% to 95% valve opening), the 1% increase in valve opening represents a 1.5% increase in steam flow. Assuming the incremental increase in steam flow is equal to the incremental increase in plant production, the improved profit is calculated as follows:

Plant production level = (after reduction in downtime)

590,000 tons/yr * 348 days/year345 days/year

=

595,130 tons/year

Gross margin =

$245,000,000 /year650,000 tons / year

=

$377 / ton

Increased profit =

0.015 increase * 595,130 tons/yr * $377 / ton

=

$3,365,000 / year

And the new productivity calculation is

% Productivity =Actual production

optimum target production

=595,130 tons * 1.015 capacity

650,000 tons

=92.9%

Next: Quality >

QualityThe plant feed is 670,000 tons/year, and production (before the reductions in downtime) was 650,000 tons/year. The difference 20,000 tons/year was off-spec product.

Before using PlantWeb architecture, therefore, the example plant's quality calculation was:

% Quality =

product produced - (scrap & rework)product produced

= 670,000 - 20,000 tons/year670,000 tons/year

= 97%

Improving quality with PlantWeb. The multivariable, model predictive control (MPC) technology used by DeltaV and Ovation automatically accounts for process interactions and difficult process dynamics. For example, DeltaV Predict easily handles excessive deadtime, long time constants, inverse responses, and loop interactions. Through these advanced control techniques, the variability in key process variables can be dramatically reduced.

With improved measurement and control using PlantWeb architecture, 5% of the off-spec material can be converted to prime product.

Increased prime product =

5% * 20,000 tons/year

=

1000 tons/year

At $377 / ton gross profit margin,

Increased profit =

1000 tons/year * $377 / ton

=

$377,000 /year

And the new quality rate is

% Quality =

650,000 + 1,000670,000

=

97.2%

Next: OEE calculations >

OEE calculations

For the example plant, OEE improved from 85.4% to 88% using PlantWeb.

Without PlantWeb:OEE = Availability*Productivity*Quality

= 97% x 90.8% x 97%

= 85.4%

With PlantWeb:OEE = 97.4% x 92.9% x 97.2%

= 88%

As the example shows, however, OEE is simply a metric. It tells you if you're making progress in improving your plant's profitability. The real value comes from the savings and increased profit opportunities that PlantWeb offers by improving availability, productivity, and quality.

Next: Use the quiz to review and earn valuable Reward Points >