0510 Performance Metrics for Mining Equipment (4)

Performance Metrics for Mobile Mining Equipment

copyright 2005 Caterpillar Inc. May 2005 - version 1.1

Table of Contents Preface

1

1. Philosophy

2

2. Introduction

5

3. Terminology & Definitions

8

3.1.

Basic Terms

3.1.1. 3.1.2. 3.1.3. 3.1.4. 3.1.5.

3.2.

Performance Metric Key Performance Indicator Target Benchmark Shutdown / Stoppage

Elements of Time

8

3.2.1. Total Calendar Hours 3.2.2. Scheduled Hours 3.2.3. Unscheduled Hours 3.2.4. Available Hours 3.2.5. Operating Hours 3.2.6. Stand-by Hours 3.2.7. Production Delay Hours 3.2.8. Operational Delay Hours 3.2.9. Downtime Hours 3.2.10. Repair Delay Hours

4. Top Tier Metrics 4.1.

11

Equipment Maintenance Management Metrics

4.1.1. 4.1.2. 4.1.3. 4.1.4. 4.1.5. 4.1.6. 4.1.7. 4.1.8.

Mean Time Between Shutdowns Mean Time To Repair Availability Index % Scheduled Downtime Asset Utilization Maintenance Ratio Top Problems / Pareto Analysis PIP / PSP Completion Rate

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11 15 18 21 24 27 31 37

4.2.

Application / Operational Metrics

4.2.1. Fuel Consumption 4.2.2. Payload Management 4.2.3. Haul Cycle Detail

4.3.

40 43 48

MARC / Customer Satisfaction Metrics

4.3.1. Contractual Availability

53

5. Appendix 5.1.

Delay Code Development and Usage

57

5.2.

Generic Pareto Reference

60

copyright 2005 Caterpillar Inc. May 2005 - version 1.1

Performance Metrics for Mobile Mining Equipment

Preface This document compiles the experiences of various individuals from the Caterpillar’s Global Mining Division, field service consulting personnel, and other service and product support staff who have contributed directly or indirectly to its content. The knowledge gained from this experience has been applied in various locations and under varying operating environments and conditions. Caterpillar believes it is appropriate to share this information with those who own, operate and support mining equipment for the purpose of creating more uniform criteria for the evaluation of product and project management. We hope you will find this work useful in enhancing the continuous improvement efforts at your respective project.

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1.

Philosophy

The ultimate performance of any piece of mining equipment is primarily dependent upon three critical factors: the design of the product, the application that it is used in, and the maintenance that it receives during its time in service. To some degree each of these factors can be controlled, but some much more than others. The equipment design is basically set by the manufacturer based upon his knowledge of the requirements in the market place. The mining equipment manufacturer has some flexibility in its design and can use “custom shop” features to alter the base machine for a particular set of operating conditions. However, the basic design is fixed based upon the clear definition of a set of functional specifications that define the environment, application and operation that the machine will be placed in. In order to have broad market appeal and to assure that the cost of the product is not prohibitive, the manufacturer is somewhat limited in terms of how far it can go. As such, a design targeted at the 90th percentile of application severity is typically more reasonable than one targeted at the toughest application that the equipment will ever be placed in. Obviously, the design target is also a function of the consequences of failure therefore products such as nuclear power plants and commercial aircraft are far less cost-sensitive than mining equipment. Thus, they can afford to invest in a more rigorous design and can justify redundant systems that tend to drive product reliability (and costs) much higher. Manufacturers of mining equipment are far more restricted in terms of what they can do and running modifications and improvements are typically limited to “tweaking” the base machine. The application in which mining equipment is used is also somewhat fixed although mines do change over time, typically becoming more severe, i.e. deeper, steeper, longer hauls, etc. Parameters such as altitude, ambient temperature, precipitation, and the materials that are excavated and mined are pretty much fixed and it is up to the miner to determine how he can best deal with the conditions he’s faced with. He has some degree of control in terms of the equipment he selects to do the job and the manner in which he uses it but many of the challenges he has from an application standpoint he has to learn to live with. To the extent that the mine’s Engineering and/ or Operations Departments can establish criteria for haul road design and maintenance … the mine plan (grades, haul road layout, haul distances, traffic patterns, etc.), and operations (payload management, speed limits, operator training, etc.) … they can influence the performance of the equipment significantly, provided those policies are adhered to. Maintenance is the factor that offers management the best opportunity to influence and control the resultant performance of its equipment. Equipment manufacturers and suppliers do publish a set of recommendations for the maintenance of the equipment that it sells but those recommendations tend to be very generic and are frequently based upon that manufacturer’s understanding of the “typical” application for its equipment. The end-user has enormous ability to influence the performance of the equipment he purchased through the maintenance practices he establishes. The proper selection of oils copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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and lubricants, the contamination controls he implements, and the amount management is willing to invest in facilities, tooling, support equipment, and training for its staff have a direct bearing on the final results it derives from the equipment it purchases. More importantly, the organization that is put in place to maintain and support the equipment must be designed to involve all of the critical elements of that organization in the equipment management process, e.g. Maintenance, Operations/ Production, Planning, Scheduling, Parts, Training, etc. If the organization is structured such that each of the problems and issues that impact equipment performance are known, quantified, and communicated throughout the organization, the maintenance (actually, the equipment management) effort can be extremely effective in managing problems or, better still, in avoiding them altogether. Maintenance is frequently thought of as drop oil, change filters, and perform the various tasks defined by the equipment manufacturer in its maintenance recommendations. Maintenance should also be viewed as predictive and corrective in order to be fully effective. When maintenance is viewed in the broader sense of equipment management, the predictive and corrective aspects of maintenance are emphasized since the term equipment management implies a cohesive effort on the part of the entire organization and not simply those routine activities performed by the Maintenance Department. Communication, participation, contribution and accountability by each functional area within the organization are fundamental to the overall success. With information gathered and flowing across departmental boundaries, everyone involved knows and understands what the key issues affecting performance are and maintenance can be customized to focus on management, correction and avoidance of root causes of problems. Obviously, this process requires regular and ongoing review of equipment performance and appropriate revisions of maintenance and equipment management routines to address the problems at hand. Each step in the maintenance/ equipment management process should be targeted at identifying and addressing specific existing or potential problem areas. Activities that are performed for no apparent or known reason are oftentimes of questionable value. Clearly, maintenance has the greatest potential to affect equipment performance of a given piece of equipment in any given application. In order to quantify equipment performance, some set of performance criteria must be put in place. The following holds true for most activities including the management of mining equipment: You cannot manage what you cannot control, you cannot control what you cannot measure, you cannot (or at least should not) measure without a target, and, without a target, you cannot improve.

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Management without metrics is, in reality, “management” by intuition. Benchmarking is a process used to identify best practice for an industry or for specific functions or processes within that industry. Benchmarking may be used to gauge performance relative to competition (external) or to monitor progress toward a specific set of objectives (internal). Benchmarking identifies weak areas, poor practices and areas for improvement. It is a systematic, ongoing, continuous improvement process that requires honest self-evaluation and analysis. For optimum results, benchmarking requires a longterm commitment from all levels in an organization and involvement and communications among all the functional groups within the organization, i.e. management must set the tone and all participants should understand what they are doing and why it is important. Benchmarks, the result of the benchmarking process, are standards, measurements, metrics, or key performance indicators that quantify best practices of an operation. Benchmarks for mining could be operational (payload management, delays, load times, truck exchange times, production, cost per ton, etc.), application-related (grade/ grade variation, rolling resistance, haul road maintenance, traffic flow, etc.), or maintenancerelated (availability, utilization, etc.). The benchmarks we established for equipment management were designed to answer the following seven basic questions: 1) 2) 3) 4) 5) 6) 7)

How are we? Where do we stand today? How much effort have we invested in getting where we are? Is our situation the result of planned work? What are the location and frequency of our “pain”? Is our situation stable? Is it sustainable? Are we using “failures” as an information source? Can we forecast the future?

Irrespective of how good the product is, how good the maintenance is or how easy the application is, sooner or later there will be problems. What distinguishes the successful site from the less successful one is the organization that is in place and how it deals with problems when they arise. Rather than ask, how long will it be down? or when can we put it back in service?, the knowledgeable Equipment Manager should ask, why did it go down? and what can we do to prevent this from happening again? Too frequently management views unscheduled shutdowns as failures of the equipment and seeks out technical solutions. Management should also view unscheduled shutdowns as potential failures of the equipment management system. Properly used, performance metrics enable management to distinguish product issues from project issues.

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2.

Introduction

Performance metrics are some of the least understood and most often misused concepts in mining. Our experience has shown that many mine tend to collect mountains of data … some of which they use, much of which they do not. Furthermore, much of the data that is used is not used in such a way that it actually helps improve the operation. For the most part, the data that is collected is presented in the form of purely informational reports that present little more than a historical perspective as to how the product or project has performed up to a given point in time. While informational reports are important, they don’t tend to be very useful in terms of providing management with the kind of information that aids it in an understanding of how and why an operation arrived at its present condition. The purely informational report fails to give management a feel for the likelihood of a good situation remaining good … is the situation sustainable? Nor does it give any indication as to why the situation may not be meeting expectations and what can be done to reverse the trend. Clearly, the truly effective report format must be predictive and corrective as well as informative. Reports should be viewed as powerful “management tools” and be used to guide the combined efforts and resources of the entire organization in the development and implementation of action plans targeted at achieving and maintaining acceptable levels of performance over time. Why do we use performance metrics? Valid uses are: to provide a useful and meaningful delivery format for the data analysis process, to assess, quantify and document “as is” performance relative to internal targets and established benchmarks, to facilitate the use of historical performance in the prediction of future performance, to highlight shortcomings and opportunities for improvement relative to design, application, costs and maintenance, to identify problems and corrective actions, to establish priorities in the deployment of resources, and to monitor progress of proposed solutions to identified problems. Unfortunately, far too often performance metrics are used only to assign and fix blame. Metrics should help us make sense of our situation and through their use we become smarter and gain some degree of control over the outcome. Although the calculation methods vary greatly, virtually every mine measures and reports some form of availability. It is the basis on which Operations projects its equipment needs (productive hours) in order to meet the mine's production goals. And, it is typically copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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a key yardstick by which mine management quantifies the performance of its equipment fleet and/or that of the group responsible for providing maintenance to that equipment. The overwhelming majority of mines also measure and report utilization in some form or fashion whether it is utilization of availability, utilization of the asset or both. Neither of these parameters provides much more than a historical view of the past and present status or health of the product and project. That is, they fail to give the user any clear insight into why things are the way they are and what needs to be done to ensure that a healthy situation will remain healthy or how a problem situation got that way and what needs to be done to correct the problems. In addition to availability and utilization, mines frequently monitor and report any number of other performance parameters that provide information but very little else that could be viewed as either predictive (allowing the mine to be proactive) or corrective (enhancing the mine’s ability to develop suitable action plans). Reports serve three primary purposes: to provide basic performance information to top management, to identify problems or needed action, and to set priorities for the problem management (continuous improvement process. Only the latter two provide information that directly helps to improve the operation. Unfortunately, many systems concentrate on the first item and leave maintenance management searching for ways to develop data and information for the remaining two. So, what is wrong with what we have today? As previously mentioned, reports tend to be purely informational offering no more than a “historical perspective” of the past and present situation. In general they lack the analytical (why the situation is as it is), interpretive (what it all means), corrective (what needs to be done? ... how do we manage / solve problems?), and predictive (what the consequences are or are likely to be) qualities that are required to facilitate continuous improvement. They also lack standardization, which minimizes their understanding making them difficult to use. And, lastly, they tend to be driven far more by “form” than “function” … attempts to “individualize” reports limit their utilization and reduce their value. Altogether too often we find that attempts to innovate places over-emphasis on format, which trivializes content. Information should be presented in a style that best suits the content and objectives of the report and, at the same time, meets the needs of the audience. Charts, graphs and tables should be thought of only as a means to an end. The real “meat” of any good report are the conclusions made from the “picture” of the data provided by the graphics and the resulting action plans that are developed to address problems that are identified. What do we really want and need? Reports that identify problems (present and pending), document product and project health and eliminate “surprises”, e.g. cost overruns, availability shortfalls, customer dissatisfaction. Reports should also help set priorities for problem management / continuous improvement activities in order to help focus the effort of the limited resources at our disposal. They should inform and at the same time possess analytical, interpretive, predictive, and corrective characteristics, stimulating thought not simply reporting data. Reports need to be regular (typically monthly), timely copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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(management can’t manage with old information), visual (easy to use and understand at a glance), and concise (bigger is not always better; encouraging the audience to read). Reports should be driven by functional objectives (results / action oriented!) and consider design, application and maintenance. One very good “Rule of Thumb” is “don’t generate more questions than you answer”. If one thinks of reporting as, “applying what you know to what you want to know”, the task becomes much simpler. In the same sense that an airline pilot needs only five or six of the hundred or so sources of information he has at his disposal to safely land a plane, our intent is to provide a “cockpit view” of the handful of performance metrics, actually Key Performance Indicators, that mine management needs to assess their situation. Obviously, in both cases this cockpit view needs to be supported by sufficient secondary information to permit the pilot or the mine manager to proactively take necessary action should a potential problem be detected and to take the appropriate remedial steps to resolve any existing problems. Without this supporting information we find that altogether too often the strategy for curing the ills of a project is purely reactive and that frequently this knee jerk approach drives the organization even deeper in the direction that created much of the “pain” in the first place. Caterpillar has invested a great deal of time, energy and resources identifying and developing several metrics of performance (Key Performance Indicators) that we are very comfortable with to quantify and trend product and project health. Understanding what those metrics mean relative to site performance and how they interact with each other was the initial focus in our development of the process. The next step was to devise a presentation format that enables management to quickly and easily recognize critical issues facing it in order to implement solutions to meet its overall objectives. The primary objective of this document is to summarize a globally consistent measurement and evaluation system for all mining operations that use Caterpillar equipment using the measurement parameters presented herein as the basis for quantifying product and project performance.

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3. 3.1.

Terminology & Definitions

Basic Terms Performance Metric: A term used to describe the outcome of any process used to collect, analyze, interpret and present quantitative data. A measurement parameter that enables performance against some pre-defined Target or Benchmark to be monitored. A measurement used to gauge performance of a function, operation or business relative to past results or others. Key Performance Indicator: Also known as KPI; a top level Performance Metric. The collection of KPI's used to describe performance of a particular project may vary from site to site, by product, application and even one's perspective, i.e. dealer & customer, Operations & Maintenance Depts., Project & Contract Controls Dept. NOTE: All KPI's are Performance Metrics but Performance Metrics are not always KPI's. Target: A desired goal; a standard by which a Performance Metric can be measured or judged. The Target for a particular Performance Metric can be somewhat arbitrary and will likely vary by product, application or specific site. The Target is frequently determined by customer needs, his expectations and / or contractual commitments, and manufacturers’ specifications. Benchmark (noun): A world-class performance standard relative to a specific Performance Metric; represents and quantifies "best practice" of an operation or of specific functions within that operation according to a specified Performance Metric. A Benchmark may vary by product but, by contrast, is much less arbitrary than a Target. A Benchmark is determined by and represents actual, documented, sustainable performance over time relative to some Performance Metric. Shutdown / Stoppage: An event that takes a machine out of service. Shutdowns may be scheduled or unscheduled and include all types of maintenance and repair activities except daily lubes, refueling and inspections executed during lube or refueling activities. Operational stoppages, e.g. shift changes, lunch breaks, etc., are not included as shutdowns. “Grouped” repairs count as a single shutdown. Shutdown count is independent of event duration or complexity, i.e. a five-minute event counts the same as a 100-hour event and a headlight replacement counts the same as a catastrophic major component failure.

3.2.

Elements of Time Many of the performance metrics in use today (most notably availability and utilization) involve time or ratios of time as the fundamental calculation parameters. While the formulae used to calculate these metrics are similar, the results often vary somewhat from site to site due largely to differences in the interpretation of the elements of time that copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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comprise those equations. As such, it is important to define and document the individual elements of time that make up the various categories of daily minesite operations. Furthermore, many sites tend to use terms such as physical availability, mechanical availability and simply “availability” interchangeably. Due to this lack of standardization, it is impossible to tie these metrics to a global Benchmark. Therefore, we have not attempted to use these metrics as key performance indicators for equipment management. Simply put, physical availability formulae exclude all forms of downtime from the calculation of available hours while mechanical availability formulae ignore the effects of areas such as communications radios, dispatch system, fire suppression systems, tires, etc. as non-mechanical downtime and exclude only elements pure mechanical downtime in the calculation of available hours. A similar and perhaps even more compelling argument could be made for contractual availability since not only does the interpretation of the time elements vary from one site to the next, the exclusions and limitations placed on downtime counted against availability are highly variable from contract to contract. In spite of the fact that these variations make global Benchmarking meaningless if not impossible, we do consider contractual availability to be an equipment management KPI since it has not only financial implications but also contributes significantly to customer satisfaction as it relates to the product as well as the service organization responsible for its support. It should also be noted here that even in the absence of a formal contract the end-user has a set of expectations for equipment performance. A measure of these expectations will likely include some variation of the physical, mechanical or contractual availability formulae. Since the equipment manager’s ability to meet the expectations of his customer are linked to that customer-specific metric, it should be viewed as an equipment management KPI and treated in exactly the same way as contractual availability. The following graphic and descriptions (figure 1) illustrate our interpretation of the elements of time that make up the various categories of daily mining equipment operations.

Figure 1: Elements of time for mining operations

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Total Calendar Hours: Total time in the period to be analyzed, e.g. 8760 hours / year, 720 hours / 30 day month, 168 hours / week, etc. Scheduled Hours: Time that a machine is scheduled for operations. Typically determined by the mine Planning and Operations Departments in conjunction with their overall production targets. Unscheduled Hours: Hours outside the plan; lost time that result from accidents, strikes, weather, acts of God, any holidays that are observed, etc. (typically defined by the customer or contained in the Customer Support Agreement or MARC). Available Hours: operation.

Time that a machine is capable of functioning in the intended

Operating Hours: Time that a machine is actually operating in the intended function. Stand-by Hours: Time that a machine is available for operation but is not being used, e.g. no operator available, "over-trucked", etc. Also known as "Ready line" hours. Production Delay Hours: Time that a machine is operational but is waiting with the engine running due to blasting, loader wait time, etc. Production delay hours are frequently not accounted for separately and are included in the operating hours tabulation. One the other hand, some dispatch systems do track production delay hours in an effort to minimize and manage them. In either case, lost hours that result from production delays should be reconciled and not counted against machine availability. Operational Delay Hours: Time that a machine is available for operation but is not being used due to shift changes, lunch breaks, meetings, prayers, etc. Just as was the case for production delay hours, lost hours that result from operational delays should be reconciled and never counted against machine availability. On the other hand, policy at many mines ignores operational delay hours altogether and therefore, does not credit operational delay hours as either scheduled or available hours. Downtime Hours: Time that a machine is not available for operation; out of service for all forms of maintenance, repairs and modifications. Includes inspection and diagnostic time as well as any delay or wait time for manpower, bay space, parts, tooling, literature, repair support equipment, decision making, etc. May be scheduled or unscheduled. Repair Delay Hours: Time that machine is waiting for repairs due to unavailability of labor, parts, facilities, equipment or tooling. Typically not well documented in most machine downtime histories but is nonetheless included, yet unrecognized, as part of the machine downtime record. NOTE: Please refer to Appendix 5.1, “Delay Code Development and Usage” for a more complete discussion on production, operational and repair delays. copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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4.

Top Tier Metrics

4.1. Equipment Maintenance Management Metrics 4.1.1. Mean Time Between Shutdowns Definition: The average operating time between machine stoppages … the average frequency of downtime events, expressed in hours. Description: The most successful mining operations are those that manage and maintain equipment such that it is available for extended periods of uninterrupted service. MTBS is a measure that combines the effects of inherent machine reliability and the effectiveness of the equipment management organization in its ability to influence results through problem avoidance, i.e. defect detection, repair planning, scheduling and execution. MTBS is the single most important measure of equipment maintenance management performance. Calculation Methodology:

MTBS (hours) = Operating Hrs + Production Delay Hrs*

(1)

Number of Shutdowns

Data Source(s): Operating hours obtained from machine service meter reading. Note, hours obtained from dispatch systems frequently do not agree with machine SMU due to coding of production delays, etc. Note that hours taken from machine SMU will be higher than those taken from dispatch, oftentimes by as much as 10 percent. * Production delay hours may not be tracked and accounted for separately and are therefore included in the total operating hours. Sites that use dispatch systems may track and code production delay hours separate from operating hours hence they must be acquired from dispatch. Shutdown count obtained from machine workorder history and dispatch system. Dispatch information must be used to account for shutdown events that are not accompanied by a workorder.

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Benchmarks: MTBS benchmarks vary significantly by machine model, their relative size, age and design “maturity” and complexity. MTBS for large Off Highway Trucks in the 785 – 793 size class is very well documented. The benchmark for a fleet of new trucks is 80 hours; that of a “mature” fleet (one that has undergone its first round of major component rebuilds) is 60 hours. Since by definition these benchmarks represent documented, best-in-class performance sustainable over time, we are frequently asked to assess performance through a range of results. The following table represents our best judgment in this area. MTBS

Assessment / Characteristics

50 to 60 hours

Excellent; high % of scheduled downtime; Equipment Mgmt. organization is highly proactive.

40 to 50 hours

Acceptable; majority of downtime is scheduled; substantial emphasis on Equipment Mgmt.

30 to 40 hours

Marginal; approx. half of all downtime is scheduled; Equipment Mgmt. disciplines not fully functional.

20 to 30 hours

Fair; < 40% downtime is scheduled; minimal effort on Equipment Mgmt.

< 20 hours

Poor; only PM’s are scheduled; Equipment Mgmt. organization is purely reactive.

Table 1: Site performance through range of MTBS

Benchmarks for trucks smaller than the 785 and the 797 are less well known although it is believed that MTBS for trucks in the 769 – 777 size class will be significantly higher (as much 30 to 40%) while that of the 797 will be perhaps 10% lower. Similarly, benchmarks for other large mining equipment are not well documented. However, indications are that once MTBS data is collected, analyzed and validated, the results will fall into the following ranges: Machine / Model

MTBS

D10 / D11 TTT’s

55 to 75 hours

992 / 994 WL’s

55 to 75 hours

16 MG

95 to 105 hours

24 MG

55 to 75 hours

5000 HEX

55 to 75 hours

Table 2: MTBS guidelines for mining machines

Usage: In order to make valid use of MTBS as an equipment management tool, it is assumed that the organization accepts that a repair-before-failure philosophy is the most cost efficient and effective maintenance strategy for ensuring maximized fleet performance and optimum costs. Running to failure will result in excessive machine downtime, inefficient use of resources and higher repair costs.

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MTBS is used to gauge product reliability and, more importantly, the ability of the equipment management organization to influence the end result. Since availability is a function of the frequency and duration of machine downtime events, a lower than desirable MTBS is symptomatic of low availability. It is extremely important to note that problems arise when the calculation criteria are not adhered to, e.g. arbitrary modifications in the shutdown criteria or using hours other than operating hours for the purpose of “artificially” increasing MTBS invalidates the results since the benchmarks and ranges of acceptability are based on specific calculation methodology. Comparing results derived from one calculation method to benchmarks or targets established by another is of questionable value. Interpretation: MTBS should be interpreted, at least initially, by model on the basis of the consolidated fleet over a period of one month and trended over time (six to twelve months). Recognize that MTBS will vary significantly from machine to machine within a given fleet and from day to day during the period under investigation. As such, analyzing results of small populations over short intervals will result in wide variations that can be very misleading. Declining MTBS is a valid predictor of pending problems. Likewise, MTBS can be used to gauge the impact of changes that result from efforts in continuous improvement. Action: If MTBS is lower than desirable or declining over time, the organization should review the following: •

Investigate on the basis of individual machines. Pareto applies here and we typically find that a relative small percentage of machines are operating well below the overall fleet average. Attacking those machines and bringing them up to standard will have a dramatic effect on overall fleet performance.

•

Use Pareto to determine which areas of the machine (components or systems) are resulting in higher than anticipated repair frequency. Results of this type of investigation will typically point out sources of chronic product unreliability and/or equipment management shortcomings, e.g. repair redo, inability to distinguish symptom from cause, inadequate Condition Monitoring, etc.

•

Analyze machine history records to determine if unscheduled stoppages are driving the result. If this is the case, it indicates gaps in the detect-planexecute cycle and revisions to the Condition Monitoring, Planning & Scheduling and/or execution areas will be necessary.

•

Use machine history to calculate MTBS after PM. MTBS after PM should be at least 50% greater than overall MTBS. If MTBS after PM is not sufficiently

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high, investigate to determine cause(s) of premature stoppages and adjust the PM plan accordingly to compensate for the shortcomings. Random audits of PM execution may also be necessary. Has Impact On: • • •

Fleet availability & resultant production Quantity & cost of supporting infrastructure Efficient utilization of manpower & resources

Is Impacted By: • • • • •

Chronic machine defects (lack of containment strategy) Condition Monitoring (quality and/or quantity) Planning (poor use of grouped repairs) Repair quality (redo, addressing symptom not cause, lack of training) Use of information (reactive vs. proactive)

Presentation Format: Plotting monthly MTBS over a twelve-month period on an X-Y line graph (figure 2 below) is the most effective method to demonstrate trends in MTBS. 80

70

60

MTBS - (hours)

Target 50

40

30

20

10

0 Nov-02

Dec-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

Jun-03

Jul-03

Aug-03

Se p-03

O ct-03

Month - Year

Figure 2: MTBS trend versus target for large OHT’s

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4.1.2. Mean Time To Repair (MTTR) Definition: The average downtime for machine stoppages … the average duration of downtime events, expressed in hours. Description: Repair planning, management and execution are all factors that contribute to the duration of machine shutdowns. Mean Time To Repair (MTTR) is a performance measure that quantifies repair turnaround time, i.e. how quickly (or slowly) a machine is returned to service once a downtime incident occurs. MTTR combines the effects of inherent machine maintainability / serviceability and the efficiency of the equipment management organization in delivering rapid remedial action in the execution of needed repairs. Calculation Methodology:

MTTR (hours) =

Total Downtime Hours Number of Shutdowns

(2)

Data Source(s): Downtime hours obtained from machine workorder history and dispatch system. Dispatch information must be used to account for downtime that is not accompanied by a workorder. It is essential to note that repair delay time should be included in the downtime history calculation. If delay times are known, MTTR should be calculated both with and without delays. Shutdown count obtained from machine workorder history and dispatch system. Once again, dispatch information must be used to account for shutdown events that are not accompanied by a workorder. Benchmark: MTTR benchmarks vary somewhat by machine model, their relative size and design complexity but to a much lesser extent than MTBS; machine age is the primary driver of MTTR. MTTR for large Off Highway Trucks in the 785 – 793 size class is very well documented. The benchmark for a fleet of trucks in the 785 – 793 size class is 3 to 6 hours. MTTR for new trucks should be close to the low end of the range while that of a “mature” fleet (one that has undergone its first round of major component rebuilds) should be closer to the high end of the range. This is a result of the relative complexity of the repairs seen on new versus “mature” machines.

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Benchmarks for trucks smaller than the 785 and the 797 are less well known although it is believed that MTTR for trucks in the 769 – 777 size class will be slightly lower (10 to 20%) while that of the 797 will be perhaps 10% higher. Similarly, benchmarks for other large mining equipment are not well documented. However, indications are that once MTTR data is collected, analyzed and validated, the results will fall into much the same range as large OHT fleets with larger machines, e.g. 24H MG and 5000 series HEX, being as much as 30 to 40% higher. Usage: Just as it is with MTBS, valid use of MTTR as an equipment management tool requires acceptance of a repair-before-failure philosophy as the most cost efficient and effective maintenance strategy for ensuring maximized fleet performance and optimum costs. Running to failure will result in excessive machine downtime, inefficient use of resources and higher repair costs. MTTR is used to gauge product serviceability but, more importantly, the ability of the equipment management organization to influence the end result through efficient repair execution. Since availability is a function of the frequency and duration of machine downtime events, a higher than desirable MTTR is symptomatic of low availability. Viewing MTTR in the context of delays will also assist management in identifying sources of those delays and taking appropriate action to minimize them. Here again, it is extremely important to note that problems arise when the calculation criteria are not adhered to, e.g. arbitrary modifications in the shutdown criteria or using hours other than downtime hours for the purpose of “artificially” reducing MTTR invalidates the results since the benchmarks and ranges of acceptability are based on specific calculation methodology. Comparing results derived from one calculation method to benchmarks or targets established by another is of questionable value. Interpretation: MTTR should be interpreted, at least initially, by model on the basis of the consolidated fleet over a period of one month and trended over time (six to twelve months). Recognize that MTTR will vary somewhat from machine to machine within a given fleet and from day to day during the period under investigation. As such, analyzing results of small populations over short intervals will result in wide variations that can be very misleading. High or increasing MTTR is an indication of problems in the detection, planning and/or execution of repairs and inefficient use of resources while low or decreasing MTTR is an indication of “patching” rather than fixing problems. It is also worthwhile to note that availability can be “bought” by driving MTTR lower with the deployment of excessive resources, e.g. manpower, facilities, parts, etc., however this approach is results in additional costs that will impact profitability. copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

Action: If MTTR is lower than desirable, the organization should review the following: •

Focus on grouping repairs for execution during available “windows of opportunity”, e.g. PM. This can be achieved through improved detection (Condition Monitoring) and planning; backlog management is an effective equipment management tool than should help in this area. It should be noted here that “shop-found” defects are repaired far less efficiently than defects that have been detected in advance and have benefited from the planning process.

•

Devise audit procedures particularly for repetitive problems; this should minimize “patching” rather than fixing problems.

If MTTR is higher than desirable, any or all of the following could achieve reduced turnaround time, lower MTTR: •

Increase the percentage of scheduled repairs; unscheduled repairs typically result in higher than necessary downtime hours.

•

Improve personnel efficiency; control time to execute repairs; identify, focus and train on the most inefficient areas, i.e. high repair time shutdowns.

•

Identify and document sources of delay time; address the causes of delay / wait time.

•

Improve field service auxiliary equipment; fully equipped service trucks and well-trained personnel can help reduce field repair times. The majority of unscheduled stoppages occur in the field thus the organization should be well prepared to handle them.

•

Control and improve PM execution time; while average PM execution times area far less than major component exchanges, they occur far more frequently and have a much greater influence on total downtime (availability).

•

Develop specialized staff for PM routines and major component exchanges.

Has Impact On: • • •

Fleet availability & resultant production Quantity & cost of supporting infrastructure Efficient utilization of manpower & resources

Is Impacted By: • • • •

High percentage of unscheduled repairs (poor Condition Monitoring) Inadequate resources (manpower, facilities, tooling, parts, etc.) Excessive delay times Inadequate Planning & Scheduling (minimal use of grouped repairs) copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

• •

Lack of training (excessive and/or ineffective diagnostic / troubleshooting) Use of information (reactive vs. proactive)

Presentation Format: Plotting monthly MTTR over a twelve-month period on an X-Y line graph is the most effective method to demonstrate trends in MTTR. (Refer to figure 3). 10

9

8

MTTR - (hours)

7

6

5 Targe t Range 4

3

2

1

0 Nov-02

Dec-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

Jun-03

Jul-03

Aug-03

Se p-03

O ct-03

Month - Year

Figure 3: MTTR trend versus target range for large OHT’s

4.1.3. Availability Index Definition: The ratio of MTBS (average shutdown frequency) to the sum of MTBS and MTTR (average shutdown duration), expressed as a percentage. Description: Availability is the result of the frequency and duration of downtime events (shutdowns). Since idle hours and specific availability calculation methods vary significantly from site to site, a “normalized” variation of the general form was developed for the purpose of comparison. The Availability Index formula is a variation on both the mechanical and physical availability formulae therefore changes will be proportional. The Availability Index does not take into account any stand-by (idle) hours where the equipment may have been available but was not utilized by copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

production thus any effects of utilization are ignored (low utilization operations tend to exhibit “artificially” higher availability since stand-by hours are essentially “free”). Because of this mathematical relationship, if any two of the three factors are known, the third can be calculated. In addition, when the Availability Index changes, this mathematical relationship shows which of the other two factors had the greatest influence upon that change. This allows management to react appropriately to changes in the Availability Index and by focusing its effort and resources on the frequency (MTBS) or duration (MTTR) of downtime events. Calculation Methodology:

Availability Index (%) =

MTBS MTBS + MTTR

X 100

Data Source(s): Since Availability Index is derived from MTBS and MTTR, the data sources for those two metrics are applicable here as well. (See previous two sections). Benchmark: Availability Index benchmarks vary significantly by machine model, their relative size, age and design “maturity” and complexity. Availability Index for large Off Highway Trucks in the 785 – 793 size class is very well documented. The benchmark for a fleet of new trucks 92%; that of a “mature” fleet (one that has undergone its first round of major component rebuilds) is 88%. Benchmarks for truck smaller than the 785 and the 797 are less well known although it is believed that the Availability Index for trucks in the 769 – 777 size class will be somewhat higher (possibly 2 to 3%) while that of the 797 will be perhaps 1 to 2% lower. Similarly, benchmarks for other large mining equipment are not well documented. However, indications are that once the data is collected, analyzed and validated, the results will fall into much the same range as large OHT fleets with larger machines, e.g. 24H MG and 5000 series HEX, being as much as 3 to 4% lower and smaller machines, e.g. 16H, being 1 or 2% higher. Usage: Since the Availability Index ignores the effects of utilization, invariably will yield a lower result than physical, mechanical and contractual availability calculations. Thus, it provides the organization a management tool that enables it to determine the true affects of its equipment management efforts while ignoring any influence of variations in machine utilization.

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The standardized calculation methodology also facilitates realistic comparisons from site to site for the purpose of benchmarking performance relative to similar sites in other parts of the world. And by breaking availability down into its elements, frequency (MTBS) or duration (MTTR) of downtime events, management is able to react appropriately to changes in the Availability Index and by focusing its effort and resources in the right areas. Interpretation: Since the Availability Index is purely a function of the frequency (MTBS) and duration (MTTR) of downtime events and the effects of utilization are totally ignored, management is able to quantify the impact of both on the end result and respond accordingly. Availability Index should be analyzed, at least initially, by model on the basis of the consolidated fleet over a period of one month and trended over time (six to twelve months). Recognize that Availability Index will vary somewhat from machine to machine within a given fleet and from day to day during the period under investigation. As such, analyzing results of individual or even small machine populations over short intervals will result in wide variations that can be very misleading. Low or declining Availability Index is a valid predictor of pending problems. Likewise, Availability Index can be used to gauge the impact of changes that result from efforts in continuous improvement. Action: Since Availability Index is derived from MTBS and MTTR, once the contributions of each are known and understood, appropriate action can be taken to attack the problems. Please see “Action” sections for MTBS and MTTR. Has Impact On: • •

Production Customer satisfaction

(Since Availability Index is derived from MTBS and MTTR, if either one or both are contributing to a shortfall in the Availability Index, any influence will be similarly felt by variations in Availability Index). Is Impacted By: • •

MTBS MTTR

(Please see contributing factors related to both MTBS and MTTR in previous sections).

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Performance Metrics for Mobile Mining Equipment

Presentation Format:

40

100%

35

98%

30

96%

25

94%

20

92%

15

90%

10

Availability Index

MTBS / MTTR - (hours)

Plotting monthly Availability Index over a twelve-month period on an X-Y line graph is the most effective method to predict trends. Plotting MTTR and MTBS on the same graph with Availability Index is the most graphic method to determine which factor, MTTR (repair duration) or MTBS (repair frequency), is driving the end result. (Refer to figure 4 below).

88% Target Availability Inde x

5

0 Nov-02

86%

De c-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

Jun-03

Jul-03

Aug-03

Se p-03

84% O ct-03

Month - Year

Figure 4:Availability Index graphed with MTBS & MTTR

4.1.4. % Scheduled Downtime Definition: The percentage of total downtime hours performed in a given period that have been planned and scheduled. Description: Work that has passed through the planning process is generally “scheduled” as the last step in that process. By monitoring the amount of work that has been planned and subsequently scheduled, the organization can assess its effectiveness in defect detection, plan repairs and complete its work with a high level of efficiency. A simple “test” to determine if a repair is truly planned and scheduled is to ask the question, “Are the parts and necessary resources allocated to the shop bay before the machine is stopped?” copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

A high percentage of unscheduled downtime incidents results in very inefficient use of resources and excessive costs since personnel are frequently shuffled from job to job and facilities and manpower requirements need to be sufficiently large to accommodate huge swings in the number of machines down for repairs. Data collected from mine studies has shown that the average downtime for unplanned / unscheduled work is up to eight times greater than the downtime for planned / scheduled activity. Aside from MTBS, % Scheduled Downtime Hours is the most important measure of equipment maintenance management performance. Calculation Methodology:

% Scheduled Maintenance =

Scheduled Downtime Hours X 100 Total Downtime Hours

(4)

Data Source(s): Downtime hours obtained from machine workorder history and dispatch system. Dispatch information must be used to account for downtime that is not accompanied by a workorder. It is essential to note that repair delay time should be included in the downtime history calculation. Individual workorders should be coded as “scheduled” or “unscheduled in order to track the number of downtime hours that are scheduled. Benchmark: % Scheduled Downtime Hours for large Off Highway Trucks in the 785 – 793 size class is very well documented. Mines with highly effective equipment management processes in place are able to execute 80% of its maintenance and repair downtime activity on a scheduled basis. We believe that this criterion holds true for other mining equipment as well however requirements for less utilized, non-production equipment may be somewhat less. Usage: % Scheduled Downtime Hours can be used to determine if an organization is in control of the situation (proactive) or if it is simply responding to the immediate needs of the equipment (reactive). Interpretation: The % Scheduled Downtime Hours should be analyzed, at least initially, by model on the basis of the consolidated fleet over a period of one month and trended over time (six to twelve months). A low % Scheduled Downtime Hours is indicative of gaps in the detect-plan-execute cycle and revisions to the Condition Monitoring, Planning & Scheduling and/or execution areas will be necessary. Declining % Scheduled Downtime Hours is a valid predictor of pending problems and may very well predict copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

future shortages of manpower and facilities. Likewise, % Scheduled Downtime Hours can be used to gauge the impact of changes that result from efforts to improve the disciplines related to the detect-plan-execute cycle. Action: If % Scheduled Downtime Hours is lower than desirable or declining over time, the organization should review the following: •

Use Pareto to identify causes of machine unreliability that are resulting in unscheduled stoppages. Devise an improved detection and/or containment strategy to deal with these issues in order to minimize their influence or eliminate them altogether.

•

Review Condition Monitoring practices to ensure that they are focused on problems that are leading to unscheduled downtime events.

•

Refine Planning and Scheduling practices to ensure that once problems are detected they receive full benefit from the planning and scheduling activity.

•

Employ Backlog Management as an equipment management tool to deal with problems identified through Condition Monitoring.

Has Impact On: • • • •

Fleet availability & resultant production Overall repair and maintenance costs Manpower and infrastructure requirements MTBS and MTTR

Is Impacted By: • • • •

Product unreliability Condition Monitoring quality Planning and Scheduling disciplines Limited or inadequate use of Backlog Management

Presentation Format: Plotting monthly % Scheduled Downtime Hours over a twelve-month period on an XY line graph is the most effective method to monitor and predict trends. (Please see sample graphic, figure 5, on the following page).

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Performance Metrics for Mobile Mining Equipment

Scheduled vs. Unscheduled Work (Based on machine downtime hours) 50% 45% 40%

Percent Scheduled

35% 30% 25%

Tre nd (rolling ave rage )

20% 15% 10% 5% 0% Jun'99

Jul'99

Aug'99

Se p'99

O ct'99

Nov'99

De c'99

Jan'00

Fe b'00

Mar'00

Apr'00

May'00

Month/ Year

Figure 5: % Scheduled Work trend

4.1.5. Asset Utilization Definition: The proportion of time that a machine is operating (operating hours) divided by the total calendar time in the period, expressed as a percentage. Description: How effectively the Operations Department schedules equipment and efficiently it utilizes that equipment has significant implications for Maintenance. If machines are scheduled for use 24 hours a day, 7 days a week, Maintenance must respond by working with Operations to find windows of opportunity in which maintenance and repairs can be performed without increasing downtime. These opportunities typically occur during scheduled shutdowns but they may also come at shift change, lunch breaks or during operational delays such as during blasting or fueling of equipment. In all circumstances, Operations and Maintenance need to recognize that they are working together toward common goals … high availability, good machine reliability and the lowest possible cost per unit of production. Calculation Methodology:

Asset Utilization (%) =

Operating Hours

(5)

X 100

Total Calendar Hours

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Performance Metrics for Mobile Mining Equipment

Data Source(s): Operating hours are obtained from machine service meter reading and should include production delay hours. Note, hours obtained from dispatch systems frequently do not agree with machine SMU due to coding of production delays, etc. Note that hours taken from machine SMU will be higher than those taken from dispatch, oftentimes by as much as 10 percent. Total calendar hours is equal to the total time in the period to be analyzed, e.g. 8760 hours / year, 720 hours / 30 day month, 168 hours / week, etc. Benchmarks: Asset Utilization for large Off Highway Trucks in the 785 – 793 size class is very well documented. Mines with highly effective equipment management processes in place are able to achieve Asset Utilization of 90%, over 7800 operating hours per year. We believe that this Benchmark is valid for other production mining equipment however the Benchmark for less utilized, non-production equipment, although unknown, may be significantly less. Usage: Usage of the Asset Utilization metric varies substantially based upon the perspective of the user. The mine Purchasing Department views it as an indication as to whether additional equipment purchases are necessary or if Operations should simply make more efficient use of the equipment it already has. The MARC development staff views Asset Utilization as a prediction tool for contract revenue stream. The Equipment Management staff utilizes Asset Utilization as a tool to predict staffing levels as well as in the planning and scheduling of component replacement, i.e. as machine usage increases, the quantity of maintenance manpower must be increased to keep pace and components will come due for replacement sooner. Interpretation: Asset Utilization and availability are directly related, i.e. high availability generally results in high Asset Utilization. For the equipment manager, high Asset Utilization implies very good repair efficiency, a very low number of stand-by hours and, since Maintenance Ratio is a function of operating hours, it dictates staffing levels required to support the fleet. Furthermore, since component lives are a function of operating hours, high Asset Utilization means that components will come due for replacement sooner. Asset Utilization is a valid indicator of equipment management proficiency. Action: Lower than desirable Asset Utilization should be investigated in the context of the parameters that define availability as follows:

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Performance Metrics for Mobile Mining Equipment

•

If stand-by hours are excessive, it may be a result of excess haulage capacity or ineffective scheduling of operators. This is not something that the equipment manager will be able to influence but he should be aware of the issue and its impact.

•

If operational delay hours are excessive, it may be the result of excessive time lost at shift change, meals, etc. Once again, this is not something that the equipment manager will be able to influence but he should be aware of the issue and its impact.

•

If both stand-by and operational delay hours are within reasonable limits, availability (too few operating hours) is most likely the cause and the equipment manager should investigate to determine the root cause, e.g. repair efficiency / effectiveness, machine reliability, etc.

Has Impact On: •

Production, ... mine production results are related directly to Asset Utilization (and operational efficiency).

•

Revenue, ... revenue stream in a MARC environment is related directly to Asset Utilization.

•

Manpower requirements, … maintenance and repair labor costs will increase with Asset Utilization.

•

Component life cycles, … components will reach their useful lives sooner as Asset Utilization increases.

Is Impacted By: •

Repair efficiency/ effectiveness, ... efficient and effective repair execution results in less downtime, which in turn produces higher Asset Utilization.

•

Mine production goals, ... Asset Utilization is influenced directly by the mines production requirements.

•

Operator scheduling, … low Asset Utilization resulting from excessive standby hours (machine idle time) is affected by the mines ability to schedule and assign operators to the equipment.

Presentation Format: Data should be collected, analyzed and reported monthly. Plotting Asset Utilization versus time over a twelve-month period on an X-Y line graph is an effective method for identifying trends. Analyzing Asset Utilization in terms of its components and in conjunction with availability and production can be an effective method for determining cause-effect relationships. (Please see sample graphic, figure 6, on the following page). copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

Asset Utilization / Availability Index - (% )

100%

95%

90%

85%

80%

75%

70%

Oct-02

Dec-02

Jan-03

Mar-03

May-03

Jun-03

Aug-03

Oct-03

Nov-03

Month - Year

Figure 6: Asset Utilization trend

4.1.6. Maintenance Ratio Definition: The dimensionless ratio of maintenance and repair man-hours to machine operating hours. Description: Maintenance Ratio is an indication of the amount of effort required to keep equipment in service as well as the efficiency with which labor is deployed and the effectiveness of the workforce in carrying out its duties. Maintenance Ratio can be calculated as either “charged” or “direct”. “Charged” Maintenance Ratio considers only workorder man-hours (direct labor). Repair shop, e.g. Component Rebuild Center, labor is not included in the calculation. “Overall” Maintenance Ratio includes all the elements of “charged” Maintenance Ratio plus staff, supervision and idle time. Calculation Methodology:

Maintenance Ratio charged =

Maintenance & Repair Man-Hours Operating Hours

(6)

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Performance Metrics for Mobile Mining Equipment

Data Source(s): Maintenance and repair man-hours are obtained from the work order history. The result should include actual time spent working on all forms of maintenance, repairs and modifications as well as inefficiencies that result from inspection and diagnostic time or any delay or wait time for bay space, parts, tooling, literature, repair support equipment, decision making, etc. Operating hours are obtained from machine service meter reading and once again should include production delay hours. Note, hours obtained from dispatch systems frequently do not agree with machine SMR due to coding of production delays, etc. Benchmarks: Maintenance Ratio benchmarks vary significantly by machine model, their relative size, age and design “maturity” and complexity. Maintenance Ratio for large Off Highway Trucks in the 785 – 793 size class is very well documented. The benchmark for a fleet of new trucks is 0.20 man-hours/ operating hour; that of a “mature” fleet (one that has undergone its first round of major component rebuilds) is 0.30 manhours/ operating hour. Since by definition these benchmarks represent documented, best-in-class performance sustainable over time, we are frequently asked to assess performance through a range of results. The following (table 3) represents our best judgment in this area. MR

Assessment / Characteristics

0.30 to 0.35

Excellent; high % of scheduled downtime; Equipment Mgmt. organization is highly proactive.

0.35 to 0.40

Acceptable; majority of downtime is scheduled; substantial emphasis on Equipment Mgmt.

0.40 to 0.50

Marginal; approx. half of all downtime is scheduled; Equipment Mgmt. disciplines not fully functional.

0.50 to 0.60

Fair; < 40% downtime is scheduled; minimal effort on Equipment Mgmt.

> 0.60

Poor; only PM’s are scheduled; Equipment Mgmt. organization is purely reactive.

Table 3: Site performance through range of Maintenance Ratios

Benchmarks for trucks smaller than the 785 and the 797 are less well known although it is believed that Maintenance Ratio for trucks in the 769 – 777 size class will be slightly lower while that of the 797 will be somewhat higher. Similarly, benchmarks for other large mining equipment are not well documented. However, indications are that once Maintenance Ratio data is collected, analyzed and validated, the results will fall into the ranges shown in the table below. It is important to note here that machine application will play a role in Maintenance Ratio. This is particularly true in the case of large Track-type Tractors that can be deployed as either production or support equipment. (Refer to table 4 on the following page).

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Performance Metrics for Mobile Mining Equipment

Machine / Model

MR

D10 / D11 TTT’s

0.40 to 0.50

992 / 994 WL’s

0.35 to 0.45

16 MG

0.10 to 0.15

24 MG

0.15 to 0.20

5000 HEX

0.50 to 0.60

Table 4: Maintenance Ratio guidelines for mining machines

Usage: Valid use of Maintenance Ratio as an equipment management tool requires acceptance of a repair-before-failure philosophy as the most cost efficient and effective maintenance strategy for ensuring maximized fleet performance and optimum costs. Running to failure will result in excessive machine downtime, inefficient use of resources and higher repair costs. Maintenance Ratio can be monitored over time to provide an indication of workshop and manpower efficiency. It can also be used by the Maintenance Department to plan manpower and budget needs. When Operations provides Maintenance with its estimate of operating hours required to meet the production goals of the mine, the Maintenance Department can use Maintenance Ratio to project the manpower resources it must have to care for the equipment during that period. Caution: While it is tempting to do so, the Project Manager should not use the Benchmark levels to predict his manpower requirements unless he is certain that the equipment management system in place is integrated and fully functional. The Benchmark was measured at a site that was very well managed and all of the processes that comprise the equipment management system were in place and performing at a very high level. Unless this is the case, using Benchmark performance to forecast manpower needs will result in significant delays waiting on manpower thus increasing MTTR at the expense of availability. It is suggested that Project Management use historical performance to predict future manpower requirements and gauge the efficiency of its operation based on the Benchmark. To be useful as a budgeting tool, Maintenance Ratio needs to be measured for each family of machines, i.e. trucks, loaders, dozers, motor graders, etc., as each family of machines has different maintenance requirements. In addition, just as the maintenance and repair requirements for equipment change with time, Maintenance Ratio changes over time therefore Maintenance Ratio data must be analyzed relative to the age of the equipment and where it is in the component replacement cycle. On relatively new machines (those that have not yet started the component replacement cycle) Maintenance Ratio is lower. However, once components are replaced, the Maintenance Ratio will increase and remain essentially constant.

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Performance Metrics for Mobile Mining Equipment

Interpretation: In order to be best understood and utilized, Maintenance Ratio should be interpreted by model on a fleet basis for a consolidated period of one month and trended over time (six to twelve months). Recognize that Maintenance Ratio will vary somewhat from machine to machine within a given fleet and from day to day during the period under investigation depending upon the activities undertaken during the period. As such, analyzing results of small populations over short intervals will result in wide variations that can be very misleading. Since Maintenance Ratio is an indication of the amount of effort required to keep equipment in service, high or increasing Maintenance Ratio is an indication of problems in the detection, planning and/or execution of repairs. Mining operations that must deal with a high percentage of unscheduled repairs (low MTBS) require the investment of excessive manpower and shop resources to keep up with the demands placed upon them. This inefficient use of resources can only be dealt with through the deployment of excessive manpower, facilities, parts, etc. (all costs to the project) or by defining and correcting the shortcomings in the equipment management system. Conversely, lower than required Maintenance Ratio will result in excessive delay time waiting for manpower thus increasing MTTR and causing availability to suffer. Action: If Maintenance Ratio and the resultant cost of labor are too high, the organization should investigate the following: •

Analyze machine history records to determine if unscheduled stoppages are driving the result. If this is the case, it indicates gaps in the detect-plan-execute cycle and revisions to the Condition Monitoring, Planning & Scheduling and/or execution areas will be necessary.

•

Improve personnel efficiency; control time to execute repairs; identify, focus and train on the most inefficient areas, i.e. high repair time shutdowns.

•

In general, any steps taken to increase MTBS will reduce manpower requirements driving Maintenance Ratio in the right direction.

Has Impact On: •

Labor costs … Maintenance Ratio too high.

•

Repair delays / excessive MTTR… Maintenance Ratio too low.

Is Impacted By: •

High percentage of unscheduled repairs.

•

MTBS (too low). copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

•

Inadequate Condition Monitoring.

•

Poor Planning & Scheduling.

•

Insufficient resources (shop bays, tooling, equipment, etc.).

•

Inadequate training.

Presentation Format: Plotting monthly Maintenance Ratio versus time over a twelve-month period on an XY line graph is the most effective method to demonstrate trends in Maintenance Ratio. Overlaying the Maintenance Ratio graph with the Percentage of Scheduled Downtime and MTBS is the most graphic method to determine which factor is driving the end result.

Maintenance Ratio 1.3

0.9

1.2

0.8

1.1

0.7

1.0

0.6

0.9

0.5

0.8

0.4

0.7 0.6

0.3 B e n ch m ark Ran ge 0.2

0.5

0.1

0.4

0.0 Ju n -99

Ju l -99

Au g-99

S e p-99

O ct-99

Nov-99

De c-99

Jan -00

Fe b-00

Mar-00

Apr-00

"Overall" Maintenance Ratio

"Charged" Maintenance Ratio

793 OHT Fleet 1.0

0.3 May-00

Month/ Year

Figure 7: Maintenance Ratio trend

4.1.7. Top Problems / Pareto Analysis Definition: The distribution of problems affecting a fleet of equipment ranked in terms of MTBS, MTTR, impact on Availability and Costs.

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Description: All mining support operations have limited resources. The most successful operations are those that have a clear understanding of the problems and issues facing them and are thus in a position to establish priorities in order to focus their efforts and allocate the appropriate resources on remedial or containment strategies through continuous improvement. The identification and quantification of top problems by component (e.g. engine, transmission, …), system (e.g. hydraulics, electrical, …) or even process (e.g. PM) facilitates the understanding of the extent that each area is having an influence on various criteria that comprise the success of a mining support operation, i.e. shutdown frequency (MTBS), shutdown duration (MTTR), impact on Availability and Costs. With this knowledge the Project Manager is able to “drill down” to the key issues facing his site and apply the necessary resources in the most efficient manner to improve his situation. Calculation Methodology: Operating Hours Number of Shutdowns (by system)

MTBS (by system) = MTTR (by system) =

Downtime Hours (by system) Number of Shutdowns (by system)

Impact on Availability (by system) = (1 – Availability (total machine)) X

Cost per Hour (by system) =

(7)

(8)

Downtime Hours (by system) Total Downtime Hours (machine)

Cost (by system)

(10)

Operating Hours

Data Source(s): Operating hours are obtained from machine service meter reading. Note, hours obtained from dispatch systems frequently do not agree with machine SMR due to coding of production delays, etc. Shutdown count is obtained from machine workorder history and dispatch system. Dispatch information must be used to account for shutdown events that are not accounted for by a workorder. Shutdown count must be determined individually for each area of the machine as well as for the machine as a whole in order to assess not only the contribution of each area but also to calculate Availability Index. Downtime hours obtained from machine workorder history and dispatch system. Dispatch information must be used to account for downtime that is not accompanied copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

by a workorder. It is essential to note that repair delay times should be included in the downtime history calculation. If delay times are known, MTTR should be calculated both with and without delays. As is the case with shutdown count, downtime must be determined individually for each area of the machine as well as the machine as a whole in order to assess the contribution of each area. Total cost to support and maintain each of the systems and components on the machine. At a minimum it is vital to know the breakdown for costs of repairs and rebuilds of each major component on the machine. Most recordskeeping systems we have studied do a fairly poor job of documenting costs but if Project Management is to have any opportunity to manage contract profitability, costs must be known. Benchmarks: There is no set of Benchmarks that is applicable to this metric. However, over the course of investigation during EMR’s we developed a collection of generic reference guidelines for large Off Highway Trucks in the 785 – 793 size class that can be used as a gauge to evaluate MTBS, MTTR and impact on Availability. This reference defines what we believe to be a reasonable level of acceptability for frequency of downtime events (MTBS), duration of downtime events (MTTR) and impact on Availability for each of the major areas on the machine. The data is representative of a site operating at an Availability Index of approximately 90% and is, of course, generic since actual results achieved at any given mine are site-specific because results of this kind are a function of not only application severity but also of the operating environment, the maintenance the equipment receives and product design shortcomings that are particular to machines either by model or within a given range of serial numbers. Appendix 5.2, “Generic Pareto Reference – Large Off Highway Trucks” should be used as a baseline until Project Management can use individual site experience and history to determine how this reference can be modified to fit the application in question. Since there are many factors other than equipment management that influence costs (labor rates, transportation costs, import duties, taxes, etc.), it is impossible to define Benchmarks that are universally applicable to any given machine model. This being the case, we recommend that budgetary cost and component life projections be used to define target cost per hour figures and that actual cost performance be compared to those targets in order to determine if any particular area is out of line with expectations. Usage: Using the top problems distribution analysis enables Project Management to identify and prioritize critical issues affecting success of the project for investigation and resolution. The Pareto principle tends to hold true and we typically find that a copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

relatively small minority of potential issues is causing the overwhelming majority of the grief on a project. The output of this analysis should be viewed as input for the continuous improvement process. As such, resources can be applied in the areas that will derive the maximum benefit. Interpretation: In order to properly prioritize the four criteria each should be compared to some baseline of performance. MTBS, MTTR and Availability can be evaluated relative to predicted levels or acceptable historical data. If neither is available, the Project Manager can use the generic set of guidelines defined in the “Benchmark” section above until a set of references can be developed for his site. MTBS and MTTR for each area under investigation should be viewed as the ratio of target to actual demonstrated performance. In other words, if the MTBS (reliability) for a particular area of a machine is significantly lower than expected or if MTTR is higher, that area should be designated for analysis and investigation. It is also very important to note that experience has shown that a relatively high percentage of shutdowns of very short duration occur on many sites. We frequently see that 40 to 50% of all machine stoppages are one hour or less in duration. While our instincts tell us to attack the “big hitters”, it is critical and highly beneficial to identify and correct these repetitive issues since they occur so frequently that their influence on the end results can be very significant. Availability for each area under investigation should be viewed as the difference between target and actual demonstrated performance. That is, if the actual impact on availability for a particular area of a machine is significantly higher than expected, that area should be designated for analysis and investigation. Cost data should be evaluated relative to budgetary cost calculations. Just as with the impact on availability, the difference between actual and budgeted costs is the criteria for selection. When reviewing the list of potential issues for nomination onto the top problems summary, it is important to consider not only the problem itself but also the consequences of failure related to the problem. For example, an excessive number of coolant leaks when taken alone may be looked upon as more of a nuisance item however when one considers the consequences of failure and the potential for engine overheating and subsequent reduction in engine life, the issue becomes far more serious. Action: •

Once the top problems have been identified by component and system, machine repair history should be reviewed to determine the nature of the problems within each of those components and systems. In most instances we copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

find that the key specific issues result from a relatively small percentage of machines and a small number of causes. •

Submit the top problems to the Continuous Improvement process to determine root cause and identify the containment and resolution strategy.

•

Compare the top problems at the site to those listed in the Caterpillar CPI process as well as those covered under factory PIP/PSP programs. If the top problems at the site do not align reasonably well with the list of global issues, it is likely that the root cause is a result of site-specific conditions related to application and/or maintenance. Specific problems should be submitted to the Caterpillar CPI process for consideration and investigation.

Has Impact On: •

Costs, ... operating costs can be better managed and contained when key problems are known and can be effectively dealt with via the continuous improvement process.

•

Manpower requirements, ... manpower can be deployed more effectively and efficiently when top problems are identified and understood. Using intuition (guessing) results in excessive expenditures of labor.

•

Availability / reliability, … optimum availability and reliability can be achieved when key issues are known and given appropriate attention for resolution.

Is Impacted By: •

Maintenance strategy, … a repair-before-failure strategy focused on early detection and failure avoidance plays a fundamental role in problem management, i.e. Condition Monitoring (quality and quantity of inspections), Planning & Scheduling, Backlog Management, etc.

•

Maintenance execution, … resources (facilities and manpower) in adequate numbers and of sufficient quality have a direct influence on the end results.

•

Application severity, ... drives the results e.g. excessive fuel burn rate will tend to magnify engine-related downtime, overloading will accelerate power train and structural deterioration, etc.

•

Operating environment, ... haul road conditions, ambient temperature extremes and precipitation all have a role in determining which areas on the machine will experience problems.

Presentation Format: Charts and graphs can be used to analyze top problems but they tend not to be very visual and typically become extremely “busy”. A tabulated summary is the preferred presentation format. Data should be collected, analyzed and reported on a monthly copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

basis but it is also important to monitor trends over a six to twelve month interval in order to verify that corrective actions are having the desired effect and to determine when new issues are presenting themselves. While it is important to collect, analyze and monitor data for each of the four criteria on a monthly basis, cost data is subject to extreme variations due to the impact of major component rebuilds which tend to occur in groups as fleets progress through their life cycle. Consequently a plot of costs over time is stepped rather than linear thus it is important to know and understand exactly where the machine is in its life cycle to properly evaluate cost information.

Component / System

Total Downtime

Total Shutdowns

MTBS (hours)

ref. MTBS (hours)

ratio to ref. MTBS

Cooling System "unknown" Electronics & Electrical Chassis Brakes Air System & Starting Engine Differential Hydraulic System Tires & Rims

190.32 43.16 174.13 188.70 340.14 58.48 1265.92 68.85 169.28 348.61

366 23 109 39 93 52 172 12 63 128

82.25 1308.89 276.19 771.91 323.70 578.93 175.03 2508.71 477.85 235.19

1300 5850 1250 2850 1200 2100 550 6950 1250 550

15.80 4.47 4.53 3.69 3.71 3.63 3.14 2.77 2.62 2.34

Table 5: Top Problems by Repair Frequency - MTBS (hours)

Component / System

Total Downtime

Total Shutdowns

MTTR (hours)

ref. MTTR (hours)

ratio to ref. MTTR

Dump Body Final Drives PM Suspension Accidents Tires & Rims Electronics & Electrical Filters Engine Chassis

98.15 317.30 1350.46 97.95 57.79 348.61 174.13 5.21 1265.92 188.70

6 13 113 10 2 128 109 5 172 39

16.36 24.41 11.95 9.80 28.89 2.72 1.60 1.04 7.36 4.84

6.90 12.20 7.70 9.40 28.00 3.20 1.90 1.30 9.90 7.00

2.37 2.00 1.55 1.04 1.03 0.85 0.84 0.80 0.74 0.69

Table 6: Top Problems by Repair Duration - MTTR (hours)

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Component / System

Total Downtime

Total Shutdowns

MTBS (hours)

MTTR (hours)

Impact on Availability (%)

ref. Availability Impact (%)

act. - ref. Availability Impact (%)

Engine PM Brakes Tires & Rims Final Drives Electronics & Electrical Chassis Cooling System Hydraulic System Dump Body

1265.92 1350.46 340.14 348.61 317.30 174.13 188.70 190.32 169.28 98.15

172 113 93 128 13 109 39 366 63 6

175.03 266.41 323.70 235.19 2315.73 276.19 771.91 82.25 477.85 5017.42

7.36 11.95 3.66 2.72 24.41 1.60 4.84 0.52 2.69 16.36

3.52% 3.76% 0.95% 0.97% 0.88% 0.48% 0.53% 0.53% 0.47% 0.27%

1.61% 2.76% 0.45% 0.52% 0.52% 0.14% 0.22% 0.35% 0.35% 0.16%

1.91% 1.00% 0.50% 0.45% 0.36% 0.34% 0.31% 0.18% 0.12% 0.11%

Table 7: Top Problems by Availability opportunity -(%)

Component / System

Projected Cost (US $/ Hr)

Actual Cost (US $/ Hr)

Cost Difference (US $/ Hr)

Final Drives (2)

$18.50

$21.37

$2.87

Hoist Cyl. (2)

$1.39

$2.88

$1.49

Engine

$13.32

$14.13

$0.81

Transmission

$4.68

$5.31

$0.63

Steering Cyl. (2)

$0.15

$0.40

$0.25

Differential

$1.75

$1.94

$0.19

Torque Converter

$2.03

$2.16

$0.13

Rear Susp. Cyl. (2)

$0.81

$0.93

$0.12

Front Susp. Cyl. (2)

$0.81

none

-

Table 8: Top Problems by Cost opportunity -(US $ / hour)

4.1.8. PIP / PSP Completion Status Definition: A tracking tool used to monitor the status of implementation of factory programs. Description: Although the design is basically fixed, mining equipment manufacturers will on occasion alter the machine based on their experience over time and, as such, employ running changes and improvements to the base machine. These factory programs typically define an applicable machine serial number prefix, a range of machines within that prefix and may be limited to machines within a particular hourmeter range. They may also be deployed on a before or after failure basis. They may also be mandatory or give management some latitude in determining if a particular modification is applicable to his site-specific set of conditions. Programs are typically issued for completion within a twelve-month timeframe. The best tracking tools include the program identification number, dates of issue and termination, scheduled date of execution, program type (priority, before / after failure, safety, containment, unpublished, etc.), and some highly visual, graphical display of the completion status on an individual machine basis. copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

Calculation Methodology: Factory program completion status is calculated as the ratio of programs completed on a machine-by-machine basis relative to the number of programs that are active and applicable at the time under consideration. This ratio should be expressed as a percentage. Programs that are defined as "after failure" should not be included in the calculation. Data Source(s): Factory programs are received on site via the dealer Technical Communications staff and include all of the information necessary to determine applicability and monitor their completion status, i.e. program identification number, dates of issue and termination, and program type. Machine serial number and hourmeter information obtained from the machine history at the site. Benchmarks: Since factors such as parts availability can impact on management's ability to complete a program and in some cases program execution can be delayed to coincide with other related work (which may be a valid decision on the part of management), there is no Benchmark that is applicable to this metric. However, compliance with this discipline is viewed as critical to the success of a project and common sense would dictate that a higher percentage of completion of outstanding programs is desirable. Clearly, no program should be permitted to run beyond its termination date without being addressed unless it is an after failure only program. Usage: Obviously, monitoring the completion status of factory programs enables Project Management to track the execution status of those programs to ensure their timely completion. Less obvious, perhaps, is the fact that management can also use this type of information to determine and verify that programs are available to address those critical issues identified as "Top Problems" in the Pareto analysis. This analysis can also be used as input to the Backlog Management and the Continuous Improvement processes. Interpretation: A low program completion percentage, a number of programs that are approaching their termination date, or the existence of programs that are not planned and scheduled for execution is indicative of poor management of factory programs. In addition, if one or more programs are related to issues on the "Top Problems" summary and are incomplete, management should reprioritize the scheduling and execution of the program such that it is dealt with sooner.

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Action: If the percentage of factory program completion is low, the organization should investigate the following: •

Review all available information to determine if the shortfall is related to Planning & Scheduling of the programs or if it is the result of inadequate execution of the plan.

•

Take appropriate action to ensure that shortcomings in the Planning & Scheduling and/ or execution phases of the cycle are re-emphasized and that timely completion of factory programs receives the attention it deserves.

Has Impact On: •

Costs, ... the inability to execute factory programs may result in failures that include contingent damage that falls outside the scope of the program,

•

MTTR, ... programs that are implemented on a planned basis are inherently more efficient since they derive the benefits of the planning process.

Is Impacted By: •

Poor planning and / or scheduling, ... inadequate prioritization of programs,

•

Failure in execution, ... insufficient resources (personnel, shop bays, tooling, equipment, etc.).

Presentation Format: A spreadsheet analysis similar to the one below provides the most visual display of factory program completion status. Data should be collected, analyzed and reported on a monthly basis then compared month-to-month to trend improvement in this area. Product Improvement/Product Support Programs - May /2000

793B Fleet PS/PI

Percentage Done

Description

When

PI 3366 PI 3370 PS 4223 PS 4955 PS 5288 PS 5308 PS 5478 PS 5488 PS 5513 PS5765

Pending

Install New Software EPTCII needs to be changed Install new exhaus systems elbows Reworking the wastegate Repair the rear engine mounts Rotated rod eye bushing Repair cracks in fabricated wheels Replacing aftercooler core Replace the scavenge oil pump Replacing the steering shafts

Priority Priority Before or after Before or after After failure After failure After failure After failure After failure After failure

Description

When

Replace the Lock Up Valve Spring Install New Software EPTCII needs to be changed Hoist Pump new to be replaced Engine Blocks Need Needle Peened Install new exhaus systems elbows Reworking the wastegate Repair the rear engine mounts Rotated rod eye bushing Replace the scavenge oil pump. Replacing the steering shafts

Priority Priority Priority After Failure. Before or after Before or after Before or after After failure After Failure. After Failure. After failure

Pending Pending Claims are not registered Pending To Be Done at CRC 1.1 times target payload, 10% < 0.9 times target payload, and no loads > 1.2 or < 0.8 times target payload. Action: If payload management practices fall outside the limits of the 10/10/20 policy or are trending in that direction, the equipment manager should investigate as follows: •

Analyze payload management on an individual truck basis to determine if one or more trucks are experiencing problems with the truck payload measurement system. A malfunctioning system will induce errors that can cause the fleet distribution to be skewed or perhaps even bi-modal, which will invalidate the analysis. (Some degree of random variation is to be expected due to the inherent inaccuracies of the system but gross variations are most likely a result of system malfunction or calibration errors).

•

Work with Operations to determine the source of the overloading. Overloading may be the result of variability in material density, the use of loading tools of varying size and bucket capacity, or operator training issues. In addition to TPMS, payload “scoreboards” and other aftermarket systems are available to better define and manage the loading process. If the issues cannot be resolved, the equipment manager should document the problem in writing to the mine in the context of the MARC or other agreement since, in all likelihood customer expectations will not be met.

•

Increase the frequency and quality of structural inspections. Overloading will result in earlier than expected frame and structural damage particularly when combined with rough haul roads and high-speed operation. More thorough inspections will facilitate early detection and repair before failure.

•

Re-evaluate the component management strategy. Overloading has a detrimental impact on component lives (and related costs) requiring increased emphasis on condition monitoring and in some cases a revision of the component replacement plan.

•

Review the condition monitoring plans for machine elements such as tires, brakes and the steering system. Overloading will result in excessive tire wear and failure due to heat separation, accelerated brake wear, and brake and steering system overheating and failure. This may require that trucks are rotated to less severe hauls or perhaps even pulled out of service temporarily until stabilized and acceptable temperatures can be attained.

Has Impact On: •

Production, ... optimized payload management will yield long-term production advantages that result from better availability and higher speeds on grade. That is, any short-term production benefits that may result from overloading will be copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

more than offset by the increase in machine downtime and reduction in loaded travel speeds that result from overloading. •

Maintenance costs, … the cost per operating hour of engines, power train components, structures, the suspension system, dump body and tires as well as that of maintenance manpower and repair facility requirements will increase with overloading.

•

Operating costs, ... fuel consumption and the associated fuel costs per operating hour will increase with overloading.

•

Safety, … overloading can result in a condition in which the machine is operating outside the certification limits of the brake and steering systems.

•

Haul road maintenance, … haul road damage as a result of overloading will increase necessitating additional maintenance.

Is Impacted By: •

Payload measurement system operation, ... any malfunction of the payload measurement system, e.g. system calibration, strut charge, strut sensor operation, etc., on one or more machines will result in erroneous load data that will produce false indications of change in for the fleet.

•

Mine production requirements, ... an availability shortfall or increase in the production demand may result in intentional overloading in the interest of short-term production gains.

•

Operating practices, … backing onto the toe of the cut, load placement in the dump body, and tamping the load with the loader bucket will impact payload measurement system accuracy. (NOTE: The second gear reweigh feature in TPMS addresses this issue for capturing payload data, however this is still an issue when TPMS is used to monitor payload during the loading process).

•

Material density, … normal variations in material density as well as those that result from variability in seasonal precipitation, i.e. material moisture content, will complicate payload management control practices.

•

Truck-loader pass match, … while it may yield short-term production advantages, three to four pass loading will make the task of payload management much more difficult.

•

Bucket fill factor, … muck pile variation that results from blasting practices and/or material loadability as well as normal variations in loader operator skill levels may create problems for payload management.

•

Bucket-dump body capacity, … loader buckets that incorrectly sized to the dump body will result in problems for payload management.

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Performance Metrics for Mobile Mining Equipment

Presentation Format: Payload management data should be collected, analyzed and reported monthly. Trending monthly payload management data over time for a twelve-month period as illustrated by the graph below is the most visual and effective method to define trends in payload management performance. 22%

2.20%

21% 20%

2.00%

19% 1.80%

17% 16%

1.60%

15% 14%

1.40%

13% 12%

1.20%

Management Limit (points below this line meet the "10-10-20" criteria)

11% 10%

1.00%

9% 8%

0.80%

7% 6%

0.60%

5% 4%

0.40%

3% 2%

0.20%

1%

Management Limit (points below this line meet the "10-10-20" criteria)

0%

May-03

Jun-03

Jul-03

Aug-03

Sep-03

Oct-03

Nov-03

Dec-03

Jan-04

0.00%

Feb-04

Mar-04

Apr-04

DATE (Mo-Yr)

Figure 9: Sample OHT Payload Management trend

4.2.3. Haul Cycle Detail Definition: An analysis of the operations on a particular haul road layout for a fleet of Off Highway Trucks that enables the Equipment Manager to isolate the most significant factors affecting overall fleet performance and costs. Description: The application that a piece of equipment is used in has a direct impact on the overall performance of that equipment. Tracking and reporting application severity parameters such as haul cycle details should be a normal practice of the Maintenance Department, specifically the Planning area. Proactive analysis and interpretation of the trended results should be the first indication to management to initiate a more indepth investigation, e.g. modification of operational practices or of the maintenance strategy. The reactive alternative is to wait until the equipment “tells” management that something in the application has changed through premature component failures. As is the case with payload management, factors in the haul cycle that tend to benefit production frequently tend to have detrimental effects on machine performance and operating costs. With this in mind, haul cycle definition should be considered a key performance indicator for assessing application severity for a fleet of equipment and copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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% of Loads > 120% Target

% of Loads > 110% of Target

18%

Performance Metrics for Mobile Mining Equipment

the equipment manager must be diligent in the ongoing evaluation of operating practices through his regular documentation, tracking, and trending of the haul cycle. Calculation Methodology: Haul cycle detail can be quantified using the following methods: •

Average haul cycle distance, … data from the Truck Payload Management System (TPMS) or VIMS-TPMS reports can be used to determine the average haul cycle distance. The average haul cycle distance is the sum of the total empty and loaded travel distances divided by the count of actual loads hauled during the period under consideration.

•

Average haul cycle times, … average empty and loaded travel times are calculated by dividing the total empty and loaded travel times from TPMS or VIMS-TPMS reports and by the count of actual loads hauled during the period under consideration. Average idle time is calculated by dividing the sum of the total empty stop, total loaded stop and total load times by the count of actual loads hauled.

•

Average haul cycle speeds, … average empty and loaded travel speeds are calculated by dividing the total empty and loaded travel distances by the total empty and loaded travel times.

Data Source(s): Haul cycle data is obtained from the Truck Payload Management System (TPMS) or VIMS-TPMS reports. Benchmarks: There are no Benchmarks that are applicable to the operating haul cycle metrics. There are however practical limits that apply to operational parameters such as speeds, grades, and haul distances. The Equipment Manager should use the assumptions made when the component life plan was established as well as the output derived from FPC or other application modeling software as the basis for defining operational targets for the purpose of future comparisons in the event the application goes beyond those boundaries. Usage: Application severity indicators such as haul cycle details enable the equipment manager to gain an understanding of the application of the equipment, to determine when and why changes in the application occur, and to adapt or modify his maintenance strategy, when appropriate, as dictated by changes in the application or operating environment. Equipment health should always be analyzed in conjunction with the elements application severity.

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Interpretation: The performance and operating costs of equipment is affected directly by the operating conditions and application severity that the equipment experiences over the course of its lifetime. The Equipment Manager must ask himself, “How is the equipment being applied?” and be very much aware of the fact that application and operating conditions do change over time. These changes are typically very subtle and occur over time, as such, he must recognize when those changes occur and be prepared to address those changes when they happen. Many of the elements of application severity are due to equipment operations on the haul roads that result in changes in haul cycle distances, cycle times and travel speeds. In general, application severity increases with increases in haul cycle distances, cycle times and travel speeds. There are however exceptions in that slower travel speeds may be the result of an increase in the percentage of time on grade which translates to an increase in application severity and the affect of increases in the percentage of time at idle is a reduction in application severity. As changes in the application are detected they should be reviewed in the context of their potential influence of machine condition and acted upon appropriately. Action: If haul cycle metrics indicate a change in application severity, the equipment manager should act as follows: •

If the application is becoming less severe over time, the Equipment Manager should review the maintenance strategy to determine if increases to the targets defined in the component life plan are justified and feasible. Often times this is not practical since major component life targets need to be large enough to eliminate one rebuild from the machine life cycle. If an analysis of the factors that brought about the reduction in application severity suggests that the decrease is due to operational inefficiency, the Equipment Manager may want to work with Operations to optimize the utilization of the equipment.

•

If the application is becoming more severe over time but the application-based parameters for the haul cycle are still within the assumptions used to establish the component life plan, the Equipment Manager should investigate to determine the cause(s) of the increase in severity. If the trend indicates that the increase in application severity is likely to increase beyond expected limits, he should review the maintenance plan to determine how the increase will impact component lives in anticipation of modifications to the component replacement plan. He may also choose to discuss the change and its impact (on component life expectations and costs) with the mine’s Operations staff in an effort to bring the operation back in line with previous assumptions.

•

If one or more haul cycle-related application parameters have driven application severity beyond the limits of the established component life plan, the Equipment Manager must modify his maintenance strategy and the copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

component replacement plan to accommodate those tougher operating conditions. He must also bring this to the attention of the mine’s Operations group and discuss the implications of the increase on component lives and the resultant maintenance costs. Has Impact On: •

Production, ... application and operational changes that increase haul distances, reduce haul speeds or increase the percentage of idle time typically result in decreased production.

•

Maintenance costs, … the cost per operating hour of engines and power train components as well as that of maintenance manpower and repair facility requirements will increase with increases in haul cycle distance and average speeds. Likewise, a reduction in the percentage of idle time (stopped empty, stopped loaded and load times) for the cycle may also indicate an increase in overall application severity since component idle time is far less likely to do damage to or take life out of most major components.

•

Component operating temperatures, … drive train components will tend to run hotter and reach their equilibrium operating temperatures more quickly as travel speeds and haul distances increase and idle times decrease.

•

Operating costs, ... cost per ton tends to increase with increases in haul cycle distance since fuel consumption and the associated fuel costs per operating hour will increase with haul cycle severity.

•

Tire lives, … tire temperatures and resultant heat-related tire failures increase with speed (and payload). Increases in average travel speeds, longer one-way hauls, and reduced idle (cool-down) time all have the effect of increasing application severity.

Is Impacted By: •

Mine maturation process, ... as mines mature haul cycle distances tend to become longer and pits become deeper (increased vertical lift). As a result, the impact of the application becomes more severe in terms of its influence on fleet performance and costs.

•

Grades, … application severity increases as grades and the percentage of time on grade increase.

•

Haul road maintenance, … substandard haul road surfaces and increases in rolling resistance have adverse affects on travel speeds, production and application severity.

•

Dispatch system, … the addition of a dispatch system should reduce the amount of idle time that the fleet sees. Thus, as dispatch improves operational copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

efficiency, application severity increases since a smaller portion of each operating hour will be spent at idle. Presentation Format: Data for quantifying the haul cycle should be collected, analyzed and reported monthly. Trending the information monthly data over time (twelve-month period) as illustrated by the graph below is the most visual and effective method to detect and define trends in application severity related to the haul cycle. Overlaying the fuel consumption graphic with a plot of the haul cycle detail (empty travel, loaded travel & idle times) is an effective method for determining cause-effect relationships. 10

50

9

45

8

40 Empty Trave l Spe e d

7

35

6

30

5

25

4

20

3

Speed - (km/hr)

Distance - (km)

Cycle Distance

15 Loade d Trave l Spe e d

2

10

1

5

0 May-03

Jun-03

Jul-03

Aug-03

Se p-03

O ct-03

Nov-03

De c-03

Jan-04

Fe b-04

Mar-04

0 Apr-04

Figure 10:Haul Cycle Distance and Speed trends 280

15 14

260

13 Loade d Trave l Time

Time - (mins)

Fue l Rate 240

11 Idle Time 10

220

9

Fuel Rate - (L/hr)

12

8 200

7 Empty Trave l Time 6 5 May-03

Jun-03

Jul-03

Aug-03

Se p-03

O ct-03

Nov-03

De c-03

Jan-04

Fe b-04

Mar-04

180 Apr-04

Figure 11:Haul Cycle Time and Fuel Rate trends

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4.3.

MARC / Customer Satisfaction Metrics

4.3.1. Contractual Availability Definition: The ratio of time that a machine is capable of functioning in the intended operation (available hours) to total calendar hours in the period under consideration, expressed as a percentage. The calculation of available hours is not a pure calculation since the result is amended by downtime hours that are specifically excluded or limited by the terms of the contract. Description: Contracts are written largely to ensure that production equipment is available for operation a sufficient number of hours to enable the mine to meet its production goals at a reasonable, predetermined operating cost. The specific provisions of a contractual availability guarantee vary significantly from site to site, i.e. time that the contractor will be given credit for (available hours), time that the contractor will be held accountable for (contractual downtime), as well as specific exclusions, e.g. tires, dump bodies, welding, etc. are defined in detail in the contract. Furthermore, contracts frequently specify caps or limits on downtime that apply to things such as delays waiting on facilities, repair equipment and or other support infrastructure that the contractor is not expected to provide and has little control over. Because these exclusions and limitations vary so widely from one site to the next, it is not possible to link performance in this area to any kind of Benchmark nor does it make any sense to attempt to make comparisons from one site to the next. Calculation Methodology: As mentioned previously, contractual availability formulae vary significantly from site to site thus there is no unique calculation method that is applicable across the board. Calculations for contractual availability are typically made using the following general form:

Contractual Availability (%) =

Total Calendar Hours - MARC Downtime Hours X 100 (12) Total Calendar Hours

It is worthwhile to note that the determination of MARC Downtime Hours contributes to the lack of standardization as does the use of Total Calendar Hours. Many sites choose to use Scheduled Hours rather than Total Calendar Hours while others exclude Operational Delay Hours from the calculation of Available Hours. Since both of the elements in the equation are subject to individual site interpretation, the results can be highly variable.

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Performance Metrics for Mobile Mining Equipment

Data Source(s): Total calendar hours is equal to the total time in the period to be analyzed, e.g. 8760 hours / year, 720 hours / 30 day month, 168 hours / week, etc. If the available hours calculation involves the combination of operating hours, standby hours, production delay hours and operational delay hours (as it does in many instances), that information can be obtained from the machine service meter reading and information coded within the dispatch system. MARC downtime hours are obtained from the machine workorder history as well as the dispatch system. Dispatch information must be used to account for downtime that is not accompanied by a workorder. It is essential that the machine repair history contain detail sufficient to determine if individual downtime events are excluded from the MARC downtime calculation. Benchmarks: There is no Benchmark that is applicable to the Contractual Availability performance metric. Target performance should be compliance with the provisions defined within the contract or, in the absence of a contract, with customer expectations. Usage: While it is a valid indicator of customer satisfaction, Contractual Availability has very little value as an equipment management tool due primarily to the lack of standardization in calculation methodology. It can be beneficial to monitor Contractual Availability over time in conjunction with the Availability Index to detect trends in performance that can enable the equipment manager to take appropriate preemptive actions. Interpretation: Interpretation of Contractual Availability is very straightforward in that performance trending downward toward or falling below target levels implies that some type of corrective action(s) be taken. Availability Index is the equipment management tool in this case thus the interpretation is common to both metrics. Action: If Contractual Availability is trending toward or falls below contractual guarantee, the equipment manager should investigate as follows: •

Analyze the Availability Index to determine if the decline or shortfall is the result of frequency (MTBS) or duration (MTTR) of machine downtime events.

•

Assess performance on an individual machine basis to determine if the shortfall is the result of overall fleet performance or if it is related to the performance of a small percentage of the machines in the fleet. copyright 2005 Caterpillar Inc. May 2005 - version 1.1

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Performance Metrics for Mobile Mining Equipment

•

Perform a Pareto analysis to define the cause of the shortfall on a component or system basis.

•

Once the causes of the problem have been identified, .he equipment manager is in a position to devise corrective actions or, at minimum containment strategies until corrective actions can be identified.

Has Impact On: •

Customer Satisfaction.

•

Production.

•

MARC financial risks, … falling below the target may require the payment of penalties or guarantees defined in the MARC agreement.

Is Impacted By: •

MTBS & MTTR, … since availability is a direct result of frequency and duration of downtime events.

•

Asset Utilization, … since low utilization sites tend to exhibit higher availability and vice versa.

(Please see contributing factors in the previous sections on MTBS and MTTR.) Presentation Format: Contractual Availability data should be collected, analyzed and reported monthly. If Contractual Availability is in decline or has dropped below guaranteed target levels, it may be necessary to collect and analyze the data on a daily basis in order to track progress of corrective actions and to reconcile performance. Trending monthly Contractual Availability data over a twelve-month period in conjunction with MTBS, MTTR and Availability Index as illustrated by the graph on the following page is the most visual and effective method to define trends in performance.

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40

100%

35

98%

30

96%

25

94%

20

92%

Target Contractual Availability

15

90%

10

88%

5

86%

0 Nov-02

Contractual Availability & Availability Index

MTBS / MTTR - (hours)

Performance Metrics for Mobile Mining Equipment

84% Dec-02

Jan-03

Feb-03

Mar-03

Apr-03

May-03

Jun-03

Jul-03

Aug-03

Sep-03

Oct-03

Month - Year

Figure 12:Contractual Availability trend

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Delay Code Development & Usage

Delay Code Development and Usage: Machine delay codes are used to identify and track machine utilization and to document inefficiencies related to delays for mobile equipment in mining operations. These codes have different usages within the various mine departments. As a result, their definition, function, and usefulness may be diluted or even negated by a lack of understanding of their value and utility within the equipment management system. This document defines delay codes and their usage and provides guidance on how better to structure a delay code system to aid the management of mobile equipment.

Machine Utilization and Delay Tracking: Mines typically use two systems to track machine delays. Production control systems such as Modular Mining, WENCO and MineStar are used by the Operations Department to track production delays when a machine is mechanically available. A second system used by the Maintenance Department addresses work order control and has delay codes built into its structure related to field and work shop operations when a machine is mechanically unavailable. Getting the two systems to produce merged reports is only becoming possible with modern data base systems. Historically, someone in the mine administrative area merges reports from the two systems manually off line. Reconciliation of delay hours is frequently a low priority and management tends to look at each system independently. This approach allows delay coding to exist in parallel and does not force anyone to address coding accuracy and recording processes. As such, much of the knowledge and benefits that could be gained from this effort are lost and insight into problems affecting the operation is minimal. To define delay codes and identify where in the organization they should be captured requires a basic understanding of what happens to a productive hour in typical mining operations. Table 1 below reflects a basic breakdown of available hours to define usage for mobile equipment. Delay code structure should be designed to define machine status with respect to the mine’s management of hours within a structure defined in line with this table.

Table #1

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Delay Code Development & Usage Systems, Coding, and Process Integration: It is unlikely that a mine will throw out its current information management systems and replace them with new state of the art systems. It is simply not economically viable. Therefore, in all likelihood we must live with two systems (production control and work order control) as they are today. To do this, we must establish a relationship between the two systems. In most cases, the production system should take priority to track delay codes relating to top-level machine utilization figures. Code lists 1, 2 & 3 are a suggested list of codes to be used by the production control system to monitor machine status. These codes have been distilled from a mix of code lists observed on working mines throughout the world. You can have more or less codes than the suggested list. The key is to keep them simple, clear and meaningful to your day-to-day operations. Experience has shown that to have too many codes leads to excessive data dilution and discourages use of the system by operators.

A completely separate code structure is needed to monitor delays within the work order control system. Code List 4 provides a suggested list of what those codes should be. Again, it is important to have sufficient codes to clearly identify the reason for any delay but excessive coding will again lead to data dilution and a general lack of usefulness for the system.

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Delay Code Development & Usage

These work order system delay codes should not be confused with machine component codes, which are used to identify those areas of the machine on which you are performing work. The work order system will use this separate coding structure to provide Pareto Analysis on those components causing the most cost, downtime, and delays. The maintenance and repair delay codes are intended to overlay the component / system coding structure to facilitate an understanding of the impact of those delays on repair efficiency.

Report Generation and Nesting of Information: Daily reporting should identify to mine management all key performance indicators which relate to machine availability and utilization. Referring to Table 1, elements within Code Lists 1, 2 & 3 should be managed within the production control system. Alerts or other report flags should be built into the production system reporting structure to raise management’s attention to “out of norm” conditions. The extent of delay needs to be quantified, the source(s) defined and, if it is excessive, a corrective action plan put in place to keep machines in service. By their very nature, the majority of delays within Code List #4 imply unscheduled maintenance and repair activity. Scheduled events should be included but should not necessarily raise any significant red flags to demand action. On the other hand, unscheduled events should ring every alert possible to draw focus on situations requiring management intervention. A report needs to be developed which will provide an analysis of delays recorded within the production system (Code Lists 1, 2 and 3) and those delays recorded in the work order system. Basically, all delay time recorded against Code Lists 1, 2 and 3 in the production control system as well as those recorded in the work order control system (Code List 4) should be analyzed to determine which areas are having the greatest impact on machine downtime (lost production). If the information is not used in this way, it has little value and is likely not worth the effort. January 2005 version 1.0

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Generic Pareto Reference for Large Off Highway Trucks

Generic Pareto Reference for Large Off Highway Trucks: The most successful mining support operations are those that have a clear understanding of the problems and issues they are facing. The identification and quantification of problems by component (e.g. engine, transmission, …), system (e.g. hydraulics, electrical, …) or even process (e.g. PM) facilitates an understanding of the influence each is having on the final outcome enabling management to focus its attention and resources on key areas that will derive the maximum benefit. Unfortunately there are no Benchmarks that are applicable to this kind of measurement. However a collection of guidelines for large Off Highway Trucks in the 785 – 793 size class is available to evaluate actual site performance in terms of MTBS, MTTR and impact on Availability. While not actually benchmarks, the information contained in this reference defines what we believe to be a reasonable level of acceptability for frequency of downtime events (MTBS), duration of downtime events (MTTR) and impact on Availability for each of the major areas on the machine. The data is representative of a site operating at an Availability Index of approximately 90% and is, of course, generic since actual results achieved at any given mine are sitespecific because results of this kind are a function of not only application severity but also of the operating environment, the maintenance the equipment receives and product design shortcomings that are particular to machines either by model or within a given range of serial numbers. The table on the following page provides a baseline that can be used as a reference until individual site experience and history can be documented. Using it to generate and perform a top problems distribution analysis enables Project Management to identify and prioritize critical issues affecting success of the project for investigation and resolution.

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Generic Pareto Reference for Large Off Highway Trucks

Generic Pareto Reference Large OHT's (785-793) Component / System

ref. MTBS (hours)

ref. MTTR (hours)

ref. Availability Impact (%)

Accidents Air System & Starting Air Conditioning Auto Lube System Base Machine Brakes & Brk System Cab / Operator Station Chassis Cooling System Differential Dispatch System Dump Body Electronics & Electrical Engine Filters Final Drives Front Wheels Hoist System Hydraulic System Mirrors miscellaneous PM Steering System Suspension Switches & Sensors Tires & Rims Torque Converter Transmission unknown

6750 2100 6650 6100 4000 1200 450 2850 1300 6950 800 3750 1250 550 2800 2100 6150 1350 1250 850 750 250 1950 3150 1950 550 7250 900 5850

28.00 2.80 0.30 0.90 0.30 6.00 2.60 7.00 5.10 12.90 0.60 6.90 1.90 9.90 1.30 12.20 5.30 3.10 4.90 0.20 2.70 7.70 6.20 9.40 1.90 3.20 5.10 5.30 6.20

0.37% 0.12% 0.00% 0.01% 0.01% 0.45% 0.52% 0.22% 0.35% 0.17% 0.07% 0.16% 0.14% 1.61% 0.04% 0.52% 0.08% 0.21% 0.35% 0.02% 0.32% 2.76% 0.29% 0.27% 0.09% 0.52% 0.06% 0.53% 0.10%

"BASE MACHINE": Includes ladders, hand rails, sheet metal, brackets and other miscellaneous areas of the machine not covered by one of the description codes in the list. CAB / OPERATOR STATION: This description code includes cab, windshield, seat, windows, etc. ELECTRONICS & ELECTRICAL: Includes electrical / electronic components & systems, e.g. lighting, VIMS, wiring harnesses, connectors, etc. ENGINE: Includes all engine and enginerelated components and systems not covered by another description code, e.g basic engine, air intake & exhaust, fuel system, etc. HYDRAULIC SYSTEM: This description code includes all pumps, hydraulic actuators, hoses, lines, oil coolers, tanks, etc not included in another description code. "MISCELLANEOUS": Includes any shutdown event that is not covered by one of the other description codes. NOTE: MTTR = 15.40 hours for a 500 hour PM interval. In other words, if MTBS for PM = 500 hours, MTTR = 15.40 hours. "UNKNOWN": Stoppage is unidentified or data is unavailable. An excessive number of unknown events is an indication of inadequate recordskeeping practices.

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