CRL 1-hr: Nov 7 Introduction to Uptime Elements Reliability Framework and Asset Management System

Introduction

Over the last several years, managers up through the CEO have come to recognize equipment uptime as a key part of any successful operating strategy. Recognition of this need has generated the use of equipment availability as one of the key performance indicators of a maintenance organization. Goals are set based on "gut feel," or by benchmarking with similar facilities within the organization or with similar organizations within the same industry.
Both these methods involve high levels of uncertainty that can lead to overspending for maintenance and overtaxing of maintenance resources. The uncertainty of "gut feel" speaks for itself. Benchmarking involves high levels of
uncertainty due to the difficulties created by not knowing the exact guidelines each facility uses for recording unavailability. The author has worked at a site where four facilities had four different sets of guidelines for recording
downtime.

This paper examines availability in detail: the three types of availability and how they relate to each other, the factors that determine availability, and recommendations for improving the setting of goals.

Availability Types

The three subtypes of availability are inherent, achievable, and operational (see Figure 1). Each subtype has specific characteristics determined by several top-level factors.

Inherent Availability (Ai) - The expected level of availability for the performance of corrective maintenance only. Inherent availability is determined purely by the design of the equipment. It assumes that spare parts and
manpower are 100 percent available with no delays.

Achievable Availability (Aa) - The expected level of availability for the performance of corrective and preventive maintenance. Achievable availability is determined by the hard design of the equipment and the facility. Aa also
assumes that spare parts and manpower are 100 percent available with no delays.

Operational Availability (Ao) - The bottom line of availability. It is the actual level of availability realized in the day-to-day operation of the facility. It reflects plant maintenance resource levels and organizational effectiveness.

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It is important to understand the distinctions among the three subtypes in order to design, measure, and manage integrated subgoals:

• Achievable availability fulfills the need to distinguish availability when planned shutdowns are included.
• Inherent availability fulfills the need to distinguish expected performance between planned shutdowns.
• Operational availability is required to isolate the effectiveness and efficiency of maintenance operations.

These definitions and distinctions lead to crucial recognitions:

• The shape and location of the achievable availability curve is determined by the plant's hard design.
• An operation is at a given point on Aa, based on whether scheduled versus unscheduled maintenance strategies are selected for each failure. A goal of availability-based maintenance operations is to find the peak of the curve and operate at that level.
• Operational availability is the bottom line of performance. It is the performance experienced as the plant operates at a given production level.
• The vertical location of the Ao is controlled by decisions for resource levels and the organizational effectiveness of maintenance operations. By definition, its location cannot rise above Aa.

These factors have the following strategic implications:

• It is crucial to know the location and shape of the achievable availability curve. Otherwise, it is not possible to determine what is reasonable and possible for operational availability and, therefore, plant production.
• If the Aa curve is not known, manufacturing operations management may unknowingly attempt to achieve performance beyond that which is possible. The result is the overspending and overtaxing of maintenance resources.
• Management must make strategic decisions for long-term relative positions of the two curves. As plant production increases over time, changing operating conditions will place greater stress on equipment and drive Aa
down. Meanwhile, maintenance operation management will progressively move Ao upward to meet the demands of production. Eventually the two will converge to the point that dditional availability can be acquired only by modifying plant design.

The conclusion from these factors is that eventually Aa must be known. Otherwise, many of the current goals to develop world-class maintenance operations are not possible. It is the organization that makes the most money - not the one with the highest availability - that wins the game.

Factors That Determine Availability

Availability is a function of reliability and maintainability - in other words, how often equipment will fail and how long it takes to get the equipment back to full production capability. Reliability, maintainability, and therefore, availability are determined by the interaction of the design, production, and maintenance functions. The implication is that availability is largely determined by how well designers, operators, and maintainers work together.

Optimizing Availability

Profitable plant availability is the result of optimizing Ai, Aa, and Ao. Because no plant can achieve availability higher than Aa, achievable availability is the first to be optimized (see Figure 2).

All equipment fails based on its design even when operated and maintained perfectly. Every maintenance activity, whether scheduled or unscheduled, is representative of an equipment failure. Scheduled or time-based maintenance seeks to correct failures before they can affect equipment performance. Unscheduled maintenance is corrective maintenance performed as the result of breakdown or the detection of incipient failure.

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Achievable availability is the result of several factors:

  • Plant hard design determines the shape and location of the Aa curve. Therefore, this design establishes the possible achievable availability.
  • Maintenance strategies determine the plant's location on the Aa curve. Therefore, these strategies establish the actual achieved availability.
  • The right extreme of the Aa curve represents 00 percent scheduled maintenance. There are no surprises, because all maintenance is performed during a scheduled maintenance period. Availability is well below optimum. This extreme can be compared to cominginto the pits during every lap of a race to ensure that you have no breakdowns on the race course. It could be done, but you would never win the race.
  • Trading off scheduled maintenance for unscheduled maintenance results in a climb back up the availability curve to the left. A nearly linear increase in availability occurs until you reach the point where unscheduled
    maintenance due to breakdowns takes away from availability gains. Operating farther to the left places the equipment under more stress and increases organizational chaos.
  • After reaching the left of the peak Aa, further reductions in scheduled maintenance become poor strategies.

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The cost curve represents strategic decisions to invest large amounts of capital up front to increase Aa through hard design, or to spend operating dollars to increase Aa through more intensive maintenance
strategies. These decisions are driven by many factors, such as the need to get a product to market quickly, the availability of capital, and the operating mentality of the company.

The availability/cost curve relationship highlights the fact that availability is a proxy of revenues. At some point of either extreme of the cost curve or the availability curve, the cost of availability will exceed
the income it allows. Without availability management, operating beyond those intersections can occur without management's awareness; normal accounting practices and other maintenance performance indicators cannot easily reveal this practice.

The difference between achievable and operational availability is the inclusion of maintenance support. Achievable availability assumes that resources are 100 percent available and no administrative delays occur in their application. Therefore, maximum operational availability theoretically goes to achievable availability. In reality, every human endeavor has a natural upper limit of obtainable perfection that prevents Ao from reaching Aa.

The shape and location of the operational availability curve are determined by the level of maintenance operation resources and organizational effectives. Resources and organizational effectiveness have upper
bounds above which additional spending will not yield better results. At that point, achievable availability must be increased to give Ao room to move upward. Aa can be increased by new maintenance strategies,
provided that the plant is not operating at the peak of the Aa curve. Capital investment is required to move the Aa curve upward if the plant is operating on the peak.

This is an important point. Without availability engineering and management, it is easy to unknowingly spend beyond the point of maximum return. This may occur when plant performance falls short of management's desired productive capacity. Management tries to achieve gains with increased stress on maintenance support. However, the operational availability curve has already been unknowingly forced against the achievable availability curve. The result is throwing good money after bad. Spending is in the loss zone to the right of the intersection of the achievable availability and cost curves.

Determining Achievable Availability for an Existing Facility

Few physical asset managers have had the luxury of being an integral part of the design phase of their physical plant. Therefore, this section of the paper is dedicated to analyzing the current physical plant to determine its achievable availability.

Determining achievable availability is a four-step process:
1.Build a reliability block diagram (RBD) of the plant's critical systems.

- Use publicly available reliability data for failures. Using plant data skews the results based on plant organizational effectiveness.
- Use plant data or works estimation techniques to determine mean time to repair. Again, using plant data skews the results based on plant organizational effectiveness.

2. Determine logistical delays created by plant hard design

- To/From shops.
- To/From stores.
- Accessing equipment.

3. Add in scheduled maintenance downtime for the chosen preventive maintenance strategy.

4. Perform availability simulations.

The scope of the analysis is determined by resources, time, and the desired quality of the result.

Building the Reliability Block Diagram (RBD)

The reliability block diagram is a graphical representation of the plant systems, subsystems, and components arranged a way that reflects equipment interdependence (see Figures 3 and 4). The RBD is the cornerstone of the availability model because it shows how failure in a plant element affects process uptime.

Reliability in Serial and Parallel Fully Redundant Systems

It is important to note the reliability implications of the two types of systems presented in Figures 3 and 4. Serial systems are inherently unreliable. The failure of a single element in the system results in a stoppage of
the overall system. Fully redundant parallel systems are inherently reliable. The system stops only if all the redundant systems fail at the same time. Redundancy is an important tool in improving overall system reliability. See Improving Machinery Reliability, by Heinz P. Bloch, for a more complete discussion.

Refining the RBD

All complex machines are built from the same few basic machine elements of couplings, bearings, gears, motors, belts, and so on. The RBD is refined by breaking down the top-level RBD into several RBDs that represent each top-level system (see Figures 5 to 7).

Obtaining Failure and Repair Data

After the RBDs are built, failure and repair data must be obtained for use in availability simulations. Obtaining these data is a time-consuming task. The desired degree of certainty dictates the level of effort
required for this stage of building the model. It is important to remember that this is not an exact science. Perfection is not required. You need only be better than your toughest competition.

There are many sources for failure data. This is by no means an exhaustive list.Binomial and Weibull distributions typically are used to present failure data for modeling purposes. Most availability simulators accept either type of data.

Obtaining repair data is a much more difficult task. Repair data is typically not available anywhere in tabular form. Repair times are very dependent on the configuration of the equipment and the plant. Equipment with a great deal of guarding and with parts located in tight spots requires much longer repair times than equipment with little guarding and plenty of space in which to work. The two primary methods of obtaining repair time data are analyzing current plant data and using works estimation systems such as MOST ® to estimate times. Each method has its own set of difficulties.

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Identifying and Removing Process Bottlenecks

Availability is a proxy for revenue and throughput. The implication therefore, is that availability models can be used to find process bottlenecks created by equipment issues. This is in fact the case.

During the design phase the availability model can be used to pinpoint changes in equipment design that will increase plant throughput. The availability model enables the design engineer to make decisions about
redundancy levels, plant access, and equipment specifications based on their impact to overall throughput and the cost-benefit ratio (CBR) of the changes.

During the operating life of the plant the reliability engineer and the process engineer can work together using the availability model and Weibull Analysis to ferret out process bottlenecks created by poor maintenance strategies, poor initial design, and poor maintenance and operating procedures. By making changes in the availability model the engineers can analyze the CBR of changes in strategy, design, and operating parameters and make those changes which optimize capacity and create the lowest life cycle costs for the plant.

Organizational Implications

The need to understand and manage the top-level factors that affect availability and to model and understand the achievable availability of a plant has important implications for the shape of the maintenance and engineering organization. The typical organization does not have the necessary resources to accomplish these objectives. Industrial engineering and reliability engineering functions are required.

Figures 8 and 9 illustrate the functional makeup of a typical maintenance organization versus the functional makeup of an Availability Management driven organization.

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Closing the Gaps

Natural gaps exist between Ai and Aa and Aa and Ao. Closing these gaps in a cost-effective way makes the plant more successful. Closing the gaps requires a thorough understanding of the top-level factors that
determine availability and finding ways to improve in each aspect of those factors. The goal is to select strategies that ...

  • Minimize the number and length of scheduled outages to drive Aa closer to Ai.
  • Minimize the number and length of unscheduled outages to drive Ao closer to Aa

Minimizing the Number and Length of Scheduled Outages

The number and length of scheduled outages can be decreased by making operational and non-capital equipment improvements:

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Minimizing the Number and Length of Unscheduled Outages

The number and length of unscheduled outages or breakdowns can be decreased by using precision maintenance techniques and making non-capital equipment modifications:

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Improve Equipment

Quick-release guarding
Open space
Better replacement parts

Further increases in availability may be obtained only by capital investment to increase inherent availability and achievable availability.

Capital Improvements for Increasing Availability

After achievable availability is optimized by improving maintenance operations and making non-capital equipment modifications, the only recourse is to increase availability by capital investment in the plant or by capital investment in the equipment to increase inherent availability.

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Matching Availability Goals to Annual Business Need

No business operates in a static environment.
Availability goals that are appropriate today may not be appropriate next year, next month, next week, or even tomorrow. Equipment availability is as sensitive to the vagaries of the business climate as any other key performance measure. Business decisions that either enhance or impair availability are made every day at every level of the organization. Availability goals must be reviewed and managed the same as any other business goals because of their sensitivity to available capital and operating funds. Figure 10 shows typical inputs and outputs of the maintenance process.

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The business situation determines whether inputs or outputs are optimized. During times of overcapacity, the business is cost constrained. The input side of the process must be optimized to reduce costs. During
times of undercapacity, business is output constrained. The output side of the process must be optimized to increase business output. The important point to remember is that at best, business output will remain constant during periods in which process inputs are being optimized. The longer inputs are constrained, the more negative the effect on the outputs.

Conclusion

Using Availability Modeling and Simulation to engineer availability into a manufacturing facility by laying out equipment and facilities for optimum reliability and maintainability, installing reliable equipment, and engineering maintenance procedures prior to startup will give an organization its best chance of having a reliable plant that is optimally available from day one. Using Availability Modeling and Simulation to continually monitor operational
needs, improve maintenance and operating procedures, and to thoroughly understand the three subtypes of availability and the plants current status in relationship to Achievable Availability will help promote long term success by improving bottom-line results throughout the life of the plant.

ARMS Reliability Engineers has successfully used AvSim Plus® to and RCMCost® by Isograph, LTD to model, analyze, and optimize facility designs and maintenance strategies to help improve capacity and reduce costs in a variety of industries. To find out more about ARMS Reliability Engineers visit www.reliability.com.au.

© 2002 by Bill Keeter
ARMS Reliability Engineers - USA, LLC
Affiliated with ARMS Reliability Engineers - Australia

References

Availability Engineering & Management for Manufacturing Plant Performance. Richard G. Lamb, Prentice Hall Publishers, ISBN: 0-13-324112-2.

Improving Machinery Reliability (Practical Machinery Management for Process Plants, Volume 1). Heinz P. Bloch, Gulf Publishing Company, ISBN: 087201455X.

The Reliability Handbook From downtime to uptime - in no time. John D. Campbell, editor; Plant Engineering and Maintenance; Volume 23, Issue 6.

Acknowledgements

Thanks go to:

• Perry Lovelace, Chief Associate, Nepenthe Institute, 57 Don Jose Loop, Santa Fe, New Mexico for content advice and moral support

• My editor Fran Blauw, Owner, See Spot Run, Indianapolis, IN.

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