Robert C. Baldwin, CMRP, Editor

That conversation will focus on the goals of the enterprise and how they are to be met. Reliability and maintenance leaders will have an opportunity to respond and possibly sell some best practice concepts that previously fell on deaf ears.

What I have picked up from various conversations with speakers, exhibitors, and attendees at recent conferences (Society for Maintenance & Reliability Professionals and Noria's Practicing Oil Analysis) and a recent press briefing by Rockwell Automation, is that top management may be ready to listen.

The C-level (CEO, CFO, CIO, etc.) has invested heavily in enterprise level information systems to avoid the effects of Y2K and assure the enterprise has a solid infrastructure on which to base operations in the so-called new economy. Much of this activity has resulted in a flat or negative return on investment (ROI) because not much has happened at the bottom line.

Meanwhile, Wall Street is putting earnings performance under the microscope. Projections must be met or exceeded. Companies are responding by changing their behavior. They are more focused on the bottom line. They are embracing the elimination of waste through lean manufacturing, searching for best practices to assure operational excellence, and freeing up capital by eliminating excess inventory. The term "predictable capacity" is heard.

Return on net assets (RONA) fed by overall equipment effectiveness (OEE) is the primary metric of this new business era. Reliability and maintenance leadership that has done its homework and developed an implementation plan for processes and technology to improve RONA may find an eager ear at the C-level. (If you need a refresher on how reliability and maintenance performance connects to RONA and the bottom line, check out the article links in the box on the first page of our website at www.mt-online.com.)

The C-level will be looking for some quick wins. And reliability and maintenance is in a position to provide them. The installation of best practices can reduce substantially the indirect cost of manufacturing, and that's what C-level people want to hear.

You better be ready because it many not be hot air that's blowing through that open window of opportunity. MT

For as long as I can recall, there have been varying degrees of confusion about what people mean when they use terminology that involves the word "failure."

Failure is an unpleasant word, and we often use substitute words such as anomaly, defect, discrepancy, irregularity, etc., because they tend to sound less threatening or less severe.

The spectrum of interpretations for failure runs from negligible glitch to catastrophy. Might I suggest that the meaning is really quite simple:

Failure is the inability of a piece of equipment, a system, or a plant to meet its expected performance.

This expectation is always spelled out in a specification in our engineering world, and, when properly written, leaves no doubt as to exactly where the limits of satisfactory performance reside. So, failure is the inability to meet specifications. Simple enough, I believe, to avoid much of the initial confusion.

Additionally, there are several important and frequently used phrases that include the word failure: failure symptom, failure mode, failure cause, and failure effect.

Failure symptom: This is a telltale indicator that alerts us (usually the operator) to the fact that a failure is about to exist. Our senses or instruments are the primary source of such indication. Failure symptoms may or may not tell us exactly where the pending failure is located or how close to the full failure condition we might be. In many cases, there is no failure symptom (or warning) at all. Once the failure has occurred, any indication of its presence is no longer a symptom—we now observe its effect.

Failure mode: This is a brief description of what is wrong. It is extremely important for us to understand this simple definition because, in the maintenance world, it is the failure mode that we try to prevent, or, failing that, what we have to physically fix.

There are hundreds of simple words that we use to develop appropriate failure mode descriptions: jammed, worn, frayed, cracked, bent, nicked, leaks, clogged, sheared, scored, ruptured, eroded, shorted, split, open, torn, and so forth. The main confusion here is clearly distinguishing between failure mode and failure cause—and understanding that failure mode is what we need to prevent or fix.

Failure cause: This is a brief word description of why it went wrong. Failure cause is often very difficult to fully diagnose or hypothesize. If we wish to attempt a permanent prevention of the failure mode, we usually need to understand its cause (thus the term, root cause failure analysis). Even though we may know the cause, we may not be able to totally prevent the failure mode—or it may cost too much to pursue such a path.

As a simple illustration, a gate valve jams "closed" (failure mode), but why did this happen? Let's say that this valve sits in a very humid outside environment—so "humidity-induced corrosion" is the failure cause. We could opt to replace the valve with a high-grade stainless steel model that would resist (perhaps stop) the corrosion (a design fix), or, from a maintenance point of view, we could periodically lubricate and operate the valve to mitigate the corrosive effect, but there is nothing we can do to eliminate the natural humid environment. Thus, PM tasks cannot fix the cause—they can address only the mode. This is an important distinction to make, and many people do not clearly understand this distinction.

Failure effect: Finally, we briefly describe the consequence of the failure mode should it occur. To be complete, this is usually done at three levels of assembly—local, system, and plant. In describing the effect in this fashion, we clearly see the buildup of the consequences. With our jammed gate valve, the local effect at the valve is "stops all flow." At the system level, "no fluid passes on to the next step in the process," and finally, at the plant level, "product production ceases (downtime) until the valve can be restored to operation."

Thus, without a clear understanding of failure terminology, reliability analyses not only become confusing, but also can lead to decisions that are incorrect. MT

Anthony M. "Mac" Smith, San Jose, CA, is a pioneer in the application of Reliability-Centered Maintenance (RCM) to complex plants and facilities. Mac has 47 years of engineering experience, the past 18 of which focused on RCM program installation. He is recognized internationally for his book Reliability-Centered Maintenance.

Wall Street values a company's stock based upon the company's ability to predict production and earnings. Consistently low estimates are just as bad as overestimates. Persistent production reliability problems can have a direct impact on a company's market share and revenues. Good, reliable production demonstrates control. The question that investors ask is: "Does management have control of the business?".

Investors will put their faith (and their money) into companies that demonstrate the best control. Meeting production targets is a very important part of demonstrating this control. Highly reliable plants consistently meet production goals, which gives the perception that the plant is well run. Reliability problems can erode this perception. The need for reliable operations can be summed up in one word: Predictability. Predictability is one of the most sought after, yet rarely achieved, aspects of modern business. While better predictability is the goal, it is not clear how to achieve it.

New decision-making schemes must accompany advances in information technology. Risk, reliability, projections, and experience must be brought together to understand current business needs and future functions. Investment in technology must be used to satisfy those needs. These new processes often require a paradigm shift in order to be successful. This paradigm shift necessitates that we implement new processes and modify or eliminate old processes.

How do we get people to accept, comprehend, and use statistical techniques applied to computerized maintenance management systems (CMMS) data? Some will tell you, "Ah, that data is just garbage!" Others will say, "I just don't have the time to get to it." Still others contend that "even if I had the time, I dont know what data to use in order to understand reliability."

Resistance to change is quite often the largest barrier to successful implementation of new technologies and procedures. People resist change and tend to trust familiar practices more than new ones.

Reliability analysis
Reliability analysis is a business practice that will make your business more competitive. The goal of analyzing installed assets is to uncover the reasons, symptoms, causes, and effects of equipment unreliability to get a handle on unexpected equipment failures.

An effective reliability program rests on one fundamental principle: future probability of failure can be accurately predicted using previous failure data. If the decision-makers in the company do not accept this concept, there is virtually no probability of success for the program. Success can range from solving a few problems to the tracking of reliability of all major assets and allowing reliability results to influence the "repair-overhaul-replace" decisions that are made on a daily basis. This is not to imply that this should be the only criterion used to make these decisions, but that reliability analysis can be used to modify this approach to improve reliability.

Some companies track failure data separately, simply to report on reliability. In some industries, regulatory agencies require failure tracking and some even require adherence to limits on failure rates of assets installed in their facilities. This kind of regulatory requirement ensures that reliability problems get addressed as a regular part of doing business.

CMMS were not necessarily designed to capture and report reliability data. These systems were optimized to manage, organize, and plan complex maintenance schedules. Because these systems were not originally intended for reliability tracking purposes, some people argue that reliability analysis on this data is invalid. While this is true in some cases, many plants have excellent record keeping in their CMMS, and analysis conducted on this data can be very helpful.

Data integrity is a key issue in using CMMS data for reliability analysis. The analysts need to know the data collection practices used to gather the CMMS data. Is the data submitted in a consistent fashion or is each work order subject to a high level of variability? Variability in data capturing is the enemy of good reliability analysis.

What data is needed?
Some of the basic assumptions of reliability theory are that equipment "times to failure" can be modeled with statistical analysis techniques. The first step in this modeling is to create a set of data based upon failure records for the equipment under study. Some CMMS capture the failure data needed to conduct the reliability analysis. Work orders often contain a vast amount of information, including:

Asset or equipment ID

Asset type

Manufacturer

Model number

Event type (PM, repair, etc.)

Description of work

Out of service date or failure date

Maintainable item or failed part

These data fields are used to extract failures against individual assets, manufacturers, or asset types. This is important when trying to model failures from the same cause (Weibull) or different causes (growth). Once we have created the set of data that describes the failures, statistical tools are applied to reveal additional information about the nature and cause of failure, the expected current reliability of equipment, the future reliability of the equipment if we solve the current problem, prediction of future failure time if no action is taken, and the evolution of a failure.

CMMS offer many ways to collect data about maintenance activities. In many systems, there are often areas for cost data, spare parts, and other fields to capture comments and descriptions. There are often many date fields that describe when work is scheduled to start, actually started, scheduled to be completed, and actually completed. When using CMMS data to perform failure analysis, care must be taken to use the proper data. In order to understand what data is available, definitions of each field need to be understood by the analyst.

When a piece of equipment fails in service, a sequence of events occurs. The same sequence happens, in most cases, independent of the CMMS used; listed chronologically, it goes like this:

The item fails

Someone notices that the item has failed

Someone contacts maintenance or enters a work request into the CMMS

The item is scheduled to be repaired

The repairs are conducted

The item is tested and made available for service

The item is returned to service.

While there are variations on this process, this list describes, in a generic sense, how the failed item is recognized and repaired. The CMMS entry noted is the first interaction by a worker with the CMMS. This may or may not coincide with the actual date/time of the failure. Reliability analysts need to keep this in mind when extracting data for use by the CMMS. Analysts often assume that the delay between when the item has failed and when it is reported to the system is short compared with the life of the equipment. For most analyses, this is a good assumption, especially for critical equipment. Sometimes more accurate failure estimates can be extracted from process data or operations logs.

Reliability analysis of the data
What constitutes a reliability analysis? There are many different ways to conduct a reliability analysis. Each method provides a slightly different answer that needs to be interpreted differently. For analyzing reliability data, we suggest the following five steps:

Step 1. Determine the goal of the reliability analysis.

Step 2. Extract the necessary data from the history brief view using a query.

Step 3. If a growth model is desired, build a query of the data that identifies the population of equipment that you want to model.

Step 4. Build the necessary reliability documents to satisfy the goal of the reliability analysis.

Step 5. Interpret the results and implement a corrective action if possible.

The goal of the reliability analysis
Each reliability analysis should have a goal. The goal helps to decide which tools to use. Unfortunately, sometimes analysts will use the wrong tool for the goal they wish to achieve. Two types of reliability modeling techniques are popular in industry today: Distribution analysis (Weibull, normal lognormal, and exponential distributions) and growth modeling.

In some cases, both a Weibull analysis and a growth model need to be constructed to get a complete picture of what is going on.

Weibull analysis
Weibull is by far the most popular approach for failure data analysis since the probability density function adapts itself to the population. A Weibull analysis provides information that can help an analyst to understand if the assets are experiencing end-of-life failures, infant mortality failures, or simply random failures with no discernable pattern. Weibull analysis results also can be used to estimate the time until a certain level of unreliability has been reached.

Growth modeling
In trying to understand the overall reliability of a set of equipment failure data, a popular technique called reliability growth (also called AMSSA Growth, Duane-AMSSAA or AMSSA-Crow) is often used. Growth is valid for all failure causes and can be set up to include assets that have not experienced failures. It is the ideal tool for understanding the overall reliability of equipment.

The growth model produces two parameters: beta and lambda. A beta value greater than 1 shows improving mean time between failure (MTBF). When beta is less than 1, MTBF is decreasing and reliability is deteriorating. These values of beta and lambda also can be applied to a formula which can be used to calculate time to next failure. These estimates have proven to be very accurate when the data is complete and failure data is accurate.

Accuracy of reliability data
Reliability programs use historical data to predict the future. When doing this, plant personnel are immediately faced with questions about the accuracy of the data being used to make the predictions. This accuracy is always of concern whenever future business decisions need to be based on historical datais the data accurate? Two features of reliability analysis help here.

The first is that the analysis itself can be used to sort out inaccurate data. Poor Weibull curve fit results point to inaccurate or "dirty" data. Mixed mode data can be sorted out through the visual inspection of the curve fit. Finally, significantly changing MTBF, detected through growth analysis, gives additional clues to a lack of data integrity.

The second feature of reliability analysis that helps overcome data inaccuracies is that the most important piece of information used for the reliability analysis is the failure date and time. This data for the most part is often quite accurate because it requires no interpretation by the user and, in many systems, is captured automatically.

Case history
During a routine inspection of maintenance costs data, a reliability specialist had come across a rather disturbing result. A Pareto chart of pump costs by location ID showed a particularly alarming result.

The graph, built with Meridium software, showed that maintenance costs for the pump at location Pump-1000 were over $300,000 in 1998. This result triggered an investigation by the reliability personnel. The first step in the investigation was to understand the cause for such high costs for this asset in this location. Another data query was constructed to extract the total maintenance costs for the entire period for which data exists, since 1990. The total maintenance costs over the 9-year period was $445,891.

Upon examining the results of the work order history query, the company found that $329,800 was associated with one event, a single work order conducted on January 19, 1998. The client investigated this work order further to understand the cause. This work order query revealed that 68 work orders had been written against this location over the 9-year period from January 1, 1990 to December 31, 1998. Out of the 68 work orders, 57 were described as "routine repairs" indicating that the asset had been experiencing a high failure rate in addition to the high costs. The work order history query was refined to extract only the routine repairs. A Weibull analysis then was conducted on the data set.

The reliability analysis that was conducted showed a rather low MTBF of 60 days. Typical values of MTBF for this type of asset (centrifugal pump) normally exceed 700 days in practice and 1458 days, according to reliability database. This analysis resulted in a Weibull parameter beta of 0.84, which indicates an infant mortality failure mode.

Since infant mortality is not an expected failure mode for this type of machinery, we can attribute these failures to a procedural deficiency rather than to a design deficiency. Further investigation revealed that the cause of failure was an inadequate lubrication program for this machine. This analysis was critical in bringing attention to this occurrence so that mitigating tasks can be put into place to prevent recurrence of this type of failure.

The reliability analysis bears out the results of the investigation as a procedural problem for this pump. The Weibull results, while not used in this case to solve the problem, provide additional information that further defines and illustrates the severity of the problem. This case shows that procedural problems can be very costly and disruptive to an organization. Identifying and eliminating these types of problems through regular analysis can lead to marked improvements in efficiency and cost performance.

The data contained within the CMMS can be used to describe reliability problems with machinery. Using statistical reliability analysis techniques, managers can identify the general area that causes the problem. The results of the reliability analysis can be compared with the results of analyses done on other equipment from the same site, analyses on like equipment from other sites, and industry data.

In order to manage the reliability of the equipment, you need to measure the reliability of the equipment. A good source of data for this analysis can be the CMMS, if the data can be manipulated to provide useful results. By conducting these types of analysis on problem areas, the organization can take appropriate corrective measures before the next failure can occur, costly in-service failure and unexpected downtime. This means better predictability for the plant, better performance for the company, and higher stock prices for investors. MT

Bob Matusheski is senior consultant at Meridium, Inc., supplier of enterprise reliability management software, Roanoke, VA; (540) 344-9205

Most companies spend a lot of money training their maintenance personnel to troubleshoot hydraulic systems. If the focus were on preventing system failure, less time and money would be needed for troubleshooting.

We often accept hydraulic system failure as normal and use resources preparing for failure rather than deciding not to accept hydraulic failure as the norm and strive to eliminate it. When I worked for Kendall Co. in the 1980s, we changed our focus from reactive to proactive maintenance and practically eliminated unscheduled hydraulic failure.

Lack of maintenance of hydraulic systems is the leading cause of component and system failure, yet most maintenance personnel dont understand proper maintenance techniques of a hydraulic system. The basic foundation to perform proper maintenance on a hydraulic system has two areas of concern. The first area is preventive maintenance which is key to the success of any maintenance program whether in hydraulics or any equipment for which we need reliability. The second area is corrective maintenance, which in many cases can cause additional hydraulic component failure when it is not performed to standard.

Preventive maintenance
Preventive maintenance (PM) of a hydraulic system is basic and simple and, if followed properly, can eliminate most hydraulic component failure. PM is a discipline and must be followed as such in order to obtain results. We must view a PM program as performance oriented rather than activity oriented. Many organizations have good PM procedures, but do not require maintenance personnel to follow them or hold the personnel accountable for the proper execution of these procedures. In order to develop an effective preventive maintenance program for your system, you must follow these steps:

First, identify the system operating condition: Does the system operate 24 hours a day, 7 days a week? Does the system operate at maximum flow and pressure 70 percent or better during operation? Is the system located in a dirty or hot environment?

Second, what requirements does the equipment manufacturer state for preventive maintenance on the hydraulic system?

Third, what requirements and operating parameters does the component manufacturer state concerning the hydraulic fluid ISO particulate?

Fourth, what requirements and operating parameters does the filter company state concerning its filters ability to meet this requirement?

Fifth, what equipment history is available to verify the above procedures for the hydraulic system?

As in all PM programs, we must write procedures required for each PM task. These steps or procedures must be accurate and understandable by all maintenance personnel from entry level to master.

PM procedures must be part of the PM job plan that includes tools or special equipment required to perform the task, parts or material required to perform the procedure with store room number, safety precautions for this procedure, and environmental concerns or potential hazards.

Preventive maintenance tasks for a hydraulic system could include the following:

• Change the return or pressure hydraulic filter
• Obtain a hydraulic fluid sample
• Filter hydraulic fluid
• Check hydraulic actuators
• Clean the inside of a hydraulic reservoir
• Clean the outside of a hydraulic reservoir
• Check and record hydraulic pressures
• Check and record pump flow
• Check hydraulic hoses, tubing, and fittings
• Check and record voltage reading to proportional or servo valves
• Check and record vacuum on the suction side of the pump
• Check and record amperage on the main pump motor
• Check machine cycle time and record.

Preventive maintenance is the core support that a hydraulic system must have in order to maximize component and life and reduce system failure. PM procedures that are written properly and followed properly will allow equipment to operate to its full potential and life cycle. The process allows a maintenance department to control a hydraulic system rather than the system controlling the maintenance department. We exercise control by deciding when we will perform maintenance and how much money we will spend. The alternative is breakdown maintenance at a much higher cost.

Hydraulic knowledge
People say knowledge is power. This is also true in hydraulic maintenance. Many maintenance organizations do not know what knowledge and skills their maintenance personnel should possess. I believe hydraulic skills fall into two groups.

One includes the skills of the hydraulic troubleshooter, who must be the organizations expert in maintenance. In general, no more than 10 percent of your work force should be in the troubleshooter category. The remainder are general hydraulic maintenance personnel, who provide the preventive maintenance expertise. This ratio is based on a company developing a true preventive or proactive approach to maintaining its hydraulic systems. Typical skills for each group are outlined in the accompanying section "Hydraulic Technician Skill Sets."

Measuring success
In any program we must track success in order to have support from management and maintenance personnel. We also must understand that any action will have a reaction, negative or possible. We know successful maintenance programs will provide success but we must have a checks and balances system to ensure we are on track.

In order to measure success of a hydraulic maintenance program we must have a way of tracking success but first we need to establish a benchmark. A benchmark is a method by which we will establish certain key measurement tools that will tell you the current status of your hydraulic system and then tell you if you are succeeding in your maintenance program.

Before you begin the implementation of your new hydraulic maintenance program it would be helpful to identify and track the following information:

• Downtime (in minutes) on the hydraulic system. Record daily and answer the following questions.
  • What component failed?
  • Cause of failure?
  • Was the problem resolved?
  • Could this failure have been prevented?

• Cost associated with the downtime. Record the following daily.
  • Parts and material cost
  • Labor cost
  • Production downtime cost
  • Any other cost that can be associated with a hydraulic system failure.

• Hydraulic system fluid analysis results. Track the following from samples taken monthly.
  • Copper content
  • Silicon content
  • Water content
  • Iron content
  • ISO particulate count
  • Fluid condition (viscosity, additives, and oxidation).

When the tracking process begins, you need to trend the information that can be trended. This allows management the ability to identify trends that can lead to positive or negative consequences.

A computerized maintenance management system can track and trend most of this information accurately for you.

Root cause failure analysis
As in any proactive maintenance organization you must perform root cause failure analysis in order to eliminate future component failures. Most maintenance problems or failures will repeat themselves unless someone identifies what caused the failure and proactively eliminates it. A preferred method is to inspect and analyze all component failures. Identify the following: Component name and model number, location of component at the time of failure, sequence or activity the system was operating at when the failure occurred, what caused the failure, and how the failure will be prevented from happening again.

Failures are not caused by an unknown factor such as "bad luck" or "it just happened" or "the manufacturer made a bad part." We have found most failures can be analyzed and action taken to prevent their reoccurrence. Establishing teams to review each failure can produce major payback quickly.

Maintenance of a hydraulic system is the first line of defense to prevent component failure and thus improve equipment reliability. As spoken about earlier, discipline is the key to the success of any proactive maintenance program. MT

Ricky Smith is president of Technical Training Div., Life Cycle Engineering, Inc., 4360 Corporate Rd., Suite 100, North Charleston, SC 29405; (843) 744-7110

As many asset-intensive companies have increasingly searched for a competitive advantage, maintenance and reliability of assets have evolved as major contributors. Organizations are being challenged to improve efficiency and work with less. Various processes, such as reliability-centered maintenance (RCM), have been implemented throughout the years as part of improvement initiatives with varying degrees of success. Many of these initiatives result in some progress toward enhanced reliability of assets, but, to achieve world-class performance, a fundamental shift in the mindset of workers and the nature of work is needed. A holistic and evergreen approach to asset management processes provides the capability to change the nature of work and drive a reliability-centered culture.

The model presented here integrates "best" processes to create a world-class approach to asset management. It is illustrated in the accompanying diagram, which is divided into separate processes and sub-processes and shows the high-level flow between each. Criticality ranking, front-end failure analysis, equipment reliability strategy development, equipment reliability strategy implementation, work management, reliability analysis, and external processes comprise the model.

Elements of the model
The Asset Management Best Process Model provides the elements necessary to support a world-class asset management program. Many organizations have done a reasonable job at defining and executing standard business processes for work management. This is most often driven by a computerized maintenance management system (CMMS). The majority of new processes implemented by world-class performers have been proactive, reliability-focused processes and post-execution reliability analysis. Some organizations may find improvement by focusing on traditional work management, but to see quantum and long-term improvements, companies must implement these other processes.

A reliability-centered model for asset management seeks to better understand assets before failure, put in place proactive equipment reliability strategies to cost-effectively eliminate the likelihood and consequence of failures, and move toward an environment where the only equipment failures will be pre-determined and due to wear-out.

The Criticality Ranking Process is used to better understand and identify assets that are truly critical to the business. This process is essential to a cost-effective approach to implementing the model.

This provides the basis for focusing personnel and other resources on the equipment that has the most direct impact on the business. For instance, as a company prepares to roll out its RCM process or any other improvement initiative, this process guides the organization to that area of the facility where it should focus its efforts, along with the specific assets within that area that deserve the most attention.

Equipment identified as "critical" then enters into the Front-End Failure Analysis (FEFA) process. The FEFA process includes traditional RCM elements including identifying functional definitions for equipment (or groups of "like-kind" equipment), functional failures, failure modes and causes, and the expected functional life. The FEFA process is not dependent on equipment history, although comprehensive performance history and analyst experience will allow for better analysis and results.

Equipment Reliability Strategy Development is the natural extension of the Front-End Failure Analysis process. Equipment Reliability Strategies (e.g., one-time tasks, preventive maintenance (PM), predictive maintenance (PdM), etc.) then are developed for "critical" equipment and focus on the detection, mitigation, and/or elimination of the expected failure modes. The strategy's intent is to ensure the equipment continues to perform its intended functions for the expected functional life, within its current operating context.

Existing PM/PdM tasks, original equipment manufacturer (OEM) maintenance recommendations, and regulatory constraints will provide the basis for the strategies, but they often are improved based on a better understanding of the equipment gained through the analysis. For "non-critical" equipment, "template" equipment reliability strategies can be developed that provide a base strategy for optimal performance (most often defined by equipment type).

A key element of this model, which is often overlooked, is the Equipment Reliability Strategy Implementation process. A considerable amount of work is required to perform the front-end analysis and to develop equipment reliability strategies. Depending on the scope of assets involved and how well technology is leveraged, there also can be a sizable amount of work involved with implementation of the strategies' tasks. Once a strategy's tasks have been determined, the best implementation approach must be selected.

For instance, if the strategy calls for a recurring type of condition or process monitoring, a decision must be made whether it can be automated or not, whether it could or should be performed as part of an operator's round, or whether it should be part of a PM or other mode of implementation. There also will be opportunities to bundle tasks with consistent scheduling intervals so they can be handled more efficiently as one work effort.

The Work Management process in this model is extremely critical. Many organizations have focused on work management excellence, but in a "reactive" environment. The philosophy in a "reactive" environment is to "fix it when it breaks." This philosophy usually rewards personnel for making quick repairs at the sake of preserving evidence, understanding the cause, and updating the strategy to prevent the occurrence of that failure in the future. Elements of a traditional maintenance organization such as high percentage of reactive work, constant breaking of the schedule, little if any root cause investigation, minimal amounts of PM/PdM tasks, etc., are undeviating and perpetual. The prospect for breaking this "reactive" cycle is poor until an integrated process, focusing on proactive work, is established.

There is and always will be a place for fast and efficient repairs. However, the work management process in this model places the focus on other elements. Better work order prioritization methods based on criticality can be deployed. Proper analysis of the situation using nonintrusive condition monitoring can eliminate or delay unnecessary work. Inventory and spare parts can be forecast better through the understanding of equipment criticality. Forward-looking schedules can be planned and met. More PM/PdM tasks will be performed replacing "reactive" work. Better equipment history can be documented, providing valuable information necessary for failure and reliability analysis.

The Reliability Analysis process utilizes observed equipment behavior and compares it against the expected failure effects and modes identified as part of the FEFA, thus creating a continual or "evergreen" improvement process. This results in "evergreen" reliability strategies that are continually customized to ensure optimal performance for equipment.

The ultimate result of the "evergreen" process is to move toward an equipment-specific reliability strategy for each equipment item based on its actual performance. It is not likely that anyone would ever get to that point nor would it necessarily be prudent or cost effective, but the process provides a path to continually evaluate the actual observed conditions and create the optimal equipment reliability strategy for each asset.

This process enables the equipment reliability strategies to continually move away from a theoretical model to a realistic one based on actual performance. In other words, equipment covered by a template or equipment-group strategy will utilize the template strategy tasks as long as they are providing optimal performance. As observations are recorded, whether good performance, failures, degradation, or any other relevant information, the process provides a path to further customize the template or equipment-group based tasks to the individual equipment they are supporting, migrating from template to equipment-group to equipment-specific reliability strategies.

There are various types of reliability analyses that can be utilized. The "evergreen" process most often is triggered by a failure or other event. However, another aspect is to perform continual "ad hoc" reliability analyses. These can include the basic types of reporting such as Pareto or worst actor charts. As observed history becomes more accessible and accurate, advanced statistical modeling, such as distribution and trend analysis, can be used.

The Asset Management Best Process Model also identifies a number of important External Processes. These processes can (and many do) operate regardless of the status of this model. Each is considered important to the reliability of assets. The more integration with the external supporting processes, the better the overall enterprise asset management program.

Throughout the life of a facility, there are various Environmental/ Operational Factors that impact the Asset Management Best Process Model. The model must be flexible to respond to these factors, which include changes to business strategy, production targets, feedstock/raw material, regulatory compliance, etc. The entire model, its processes, and resulting data should be evaluated for validity upon the introduction of these factors.

For example, it is not uncommon for petroleum refiners to change their crude slate over time. In most cases, the plant was built originally to refine a "sweet" crude. If they make a decision to start using "sour" crude (indicates changing chemical composition of the crude), this has an effect on the type and frequency of deterioration expected by the equipment. With that in mind, equipment reliability strategies should be reviewed and optimized based on the expected impact of the different factors.

Implementation
The model provides the vision and the processes required to support a leading-edge asset management program based on our experiences in various asset-dependent industries and organizations. It is crucial that the implementation of this model be based on the individual needs of each organization. Each organization must evaluate how to best leverage the processes indicated in the model to meet its own strategies, goals, and objectives for asset management.

Implementation of this model also must take into account the effort required to optimize value as quickly as possible. The model, as represented, indicates a continual process, which over the long term can provide significant benefits. To see a quicker realization of benefits, implementation of certain prerequisites is necessary. These prerequisites include a short-term focus on work management basics and initial performance of the proactive elements of the model (e.g. criticality ranking, front-end failure analysis, and equipment reliability strategy development and implementation). Without the proactive elements in place for "critical" equipment, the value of the "evergreen" process is diminished.

Critical factors for successful implementation of this model include:

• Progressive vision for excellence
• Long-term commitment
• Short- and long-term objectives and goals (Key Performance Indicators)
• Build up basics while extending the model
• Leadership
• Communication
• Training
• Ownership and empowerment throughout the organization
• Technology

Benefits of the model
Significant cultural changes, cost savings, and increases in mechanical availability can be achieved by the implementation of the Asset Management Best Process Model. Short- and long-term benefits can be expected. Adoption of this model will provide the following representative benefits:

• Common vision for world-class asset management
• An excellence model to train all personnel involved with asset management
• Breakdown of departmental barriers and elimination of conflicting priorities traditionally found in organizations with a "reactive" culture
• Migration from "reactive" to "proactive and planned" reliability-centered work and culture
• Avoidance of significant events due to preventive tasks and predictive monitoring
• Increased mechanical availability/ decreased lost production opportunities
• Decreased maintenance and production costs
• Identified areas of focus for reliability improvement

Enhanced reliability of assets is a critical element in the survival of today's organizations. This recognition has brought forward the question of how to improve maintenance and reliability of assets while simultaneously freezing or trimming the maintenance budget. There are many sound methods and technologies that individually can provide significant incremental cost savings.

However, to reach quantum and long-term improvement, a change in mind-set and work is required. The reality is that this is a journey, not a destination, and unfortunately, there is no "holy grail" which will work for everyone. World-class performers are continuously pushing the envelope. Therefore, all organizations must continuously search for long-term improvement opportunities. Organizations that adopt a holistic and evergreen model such as the one presented here will set the marks for asset management excellence as we move into the 21st century.

Future articles will deal with the processes presented in this model, their interactions, and the controls an organization must provide to facilitate progress. It is our opinion that the key to world-class performance is to select and integrate the best practices available and adapt them to each organization's needs. MT

Darrell Ferguson is a senior consultant and services delivery manager within the Asset Management Consulting Group at Plumlee Associates, Inc., 2638 S. Sherwood Forest Blvd., Suite 200, Baton Rouge, LA 70816; (225) 292-4464