2019 Caterpillar Mining Equipment Management Metrics Document V4.pdf

Caterpillar Confidential: GREEN / WHEREVER THERE’S MINING.
Caterpillar
Mining Equipment Management (MEM)
Performance Metrics
Caterpillar Inc.
Document: 06122019 - 04
Revision Date: June 12th, 2019
Disclaimer: This document is a combination of additions and updates from the 2007
Metrics (KPIs) to assess performance and the 2004 Performance Metrics for Mobile Mining
Equipment.

Caterpillar Confidential Green
Copyright 2019 Caterpillar. All Rights Reserved. 1
Proprietary Material of Caterpillar Inc.
The material contained herein is confidential and is not to be copied or distributed without written permission by owner
TABLE OF CONTENTS
TABLE OF CONTENTS 1
INTRODUCTION 4
I. MEM TOP TIER METRICS 5
1. PHYSICAL AVAILABILITY 5
2. MEAN TIME BETWEEN SHUTDOWNS (MTBS) 7
3. MEAN TIME TO REPAIR (MTTR) 10
4. AVAILABILITY INDEX 12
5. CONTRACTUAL AVAILABILITY 14
6. PERCENTAGE SCHEDULED DOWNTIME 16
7. PERCENTAGE SCHEDULED EVENTS 17
8. MAINTENANCE RATIO 18
9. TOP PROBLEMS SUMMARY 23
10. ASSET UTILIZATION 25
11. UTILIZATION OF AVAILABILITY 26
12. PIP/PSP COMPLETION RATE 27
II. MEM APPLICATION / OPERATIONAL METRICS 28
1. FUEL CONSUMPTION 28
2. PAYLOAD MANAGEMENT 30
3. HAUL CYCLE DETAIL 33
III. MEM M&R PROCESS METRICS 35
1. PREVENTIVE MAINTENANCE 35
1.1. MTBS AFTER PM 35
1.2. UNAVAILABILITY PM 35
1.3. MTTRPM 36
1.4. SERVICE ACCURACY 36
1.5. BACKLOGS EXECUTED DURING PM 37
1.6. BACKLOGS GENERATED DURING PM 37
2. CONDITION MONITORING 38
2.1 MEAN TIME BETWEEN FAILURES (MTBF) 38
2.2 UNAVAILABILITYUNSCHEDULED 38
2.3 FAILURE REDUCTION 38
2.4 CONDITION MONITORING TOTAL SAVINGS 39
2.5 TOTAL BACKLOGS GENERATED 39
2.6 WORKING ON TARGET 39

2.7 BACKLOGS GENERATED BY ORIGIN 39
2.8 DETECTION LEVEL 40
3. BACKLOG MANAGEMENT 41
3.1 TOTAL BACKLOGS PENDING 41
3.2 BACKLOGS PENDING BY MACHINE 41
3.3 TOTAL BACKLOGS GENERATED 41
3.4 TOTAL BACKLOGS EXECUTED 41
3.5 ESTIMATED LABOR TO REPAIR 42
3.6 BACKLOG STATUS SUMMARY 42
3.7 BACKLOGS > 30 DAYS OLD 42
4. PLANNING & SCHEDULING 43
4.1 PERCENTAGE SCHEDULED DOWNTIME 43
4.2 SCHEDULE COMPLIANCE BY HOURS 43
4.3 SCHEDULE COMPLIANCE BY EVENTS 43
4.4 COMPONENTS EXCHANGED (SCHEDULED) 44
4.5 ESTIMATED TIME TO REPAIR PENDING BACKLOGS 44
4.6 ESTIMATED TIME TO REPLACE OVERDUE COMPONENTS 44
4.7 ESTIMATED TIME TO EXECUTE FACTORY PROGRAMS 45
5. PARTS MANAGEMENT 46
5.1 WAREHOUSE SERVICE FILL LEVEL (INSTANTANEOUS) 46
5.2 SERVICE FILL LEVEL (24 HOURS) 46
5.3 UNAVAILABILITY PARTS 46
5.4 EMERGENCY RESPONSE TIME 47
5.5 PARTS INVENTORY ROTATION 47
5.6 EMERGENCY ORDERS 47
5.7 INVENTORY (ITEMS) 47
5.8 INVENTORY (VALUE) 47
6. REPAIR MANAGEMENT 48
6.1 MTTR (SHOP SERVICE) 48
6.2 MTTR (FIELD) 48
6.3 MTTR (SHOP SERVICE / WITHOUT DELAYS) 48
6.4 MTTR (FIELD / WITHOUT DELAYS) 49
6.5 MTBS AFTER REPAIRS 49
6.6 PERCENTAGE REDO (% REDO) 49
6.7 UNAVAILABILITY DELAYS 49
6.8 CONTAMINATION CONTROL (CC) 50
7. COMPONENT MANAGEMENT 52
7.1 COMPONENT LIFE TARGET ACHIEVED 52
7.2 COMPONENTS SCHEDULED 52
7.3 COMPONENTS REPLACEMENT COSTS 52
7.4 UNAVAILABILITY COMPONENT REPLACEMENT 53
7.5 AVAILABILITY INDEX AFTER COMPONENT EXCHANGE 53
7.6 PCR COMPLIANCE 53
7.7 BACKLOGS EXECUTED DURING COMPONENT EXCHANGE 53
8. HUMAN RESOURCES 54
8.1 UNAVAILABILITY PERSONNEL DELAYS 54
8.2 EFFECTIVE MAN-HOURS 54
8.3 PERSONNEL HANDS-ON TO MACHINE RATIO 54

8.4 DIRECT MAINTENANCE PERSONNEL 55
8.5 PERSONNEL TOTAL TO MACHINE RATIO 55
8.6 TOTAL MAINTENANCE PERSONNEL 55
8.7 OVERTIME TO TOTAL EFFECTIVE HOURS RATIO 55
8.8 LABOR HOURS COVERED BY WORKORDERS 56
9.1 TRAINING HOURS/ LABOR HOURS 57
9.2 TRAINING HOURS BY SYSTEM (VS. PROBLEMS) 57
9.3 TRAINING PROGRAM COMPLIANCE 58
9.4 COMPETENCIES REQUIRED 58
9.5 COMPETENCIES SATISFIED 58
9.6 PERSONNEL SKILLS INVENTORY COMPLETION 58
10. CONTINUOUS IMPROVEMENT 59
10.1 TOP PROBLEMS WITH C.I. PROJECTS 59
10.2 ACTIVE C.I. PROJECTS 59
10.3 C.I. PROJECTS CLOSED DURING PERIOD 60
10.4 AVERAGE C.I. PROJECT EXECUTION TIME 60
10.5 C.I. PROJECTS OPENED DURING PERIOD 60
10.6 C.I. PROJECT REALIZED VALUE 60
10.7 C.I. PROJECT DELIVERED VALUE BY CATEGORY 60
IV. TIME USAGE MODEL 62
V. GLOSSARY OF TERMS: 65
VI. RAINBOW GRAPH 67
VII. MEASURING MTBS VS MTBF 70
VIII.IMPACT OF UTILIZATION ON PHYSICAL AVAILABILITY 73
IX. GENERIC PARETO REFERENCE: LARGE MINING TRUCKS 76

Introduction
The primary goal and responsibility of the maintenance department is to maintain
equipment in optimum condition via problem or failure avoidance. One of the key tools of
the maintenance department is performance measurement. In order to be effective,
performance measurements should serve (3) objectives:
• An accurate picture of performance relative to established targets and/or global
benchmarks.
• A prediction or projection of future performance’s expectations.
• A picture of the overall effectiveness of the Maintenance and Repair Organization and
processes
The most valuable performance measures assist us in identifying weak areas within the
organization, poor maintenance practices, and other improvement opportunities. Therefore,
we should look at performance critically via an honest assessment, enabling us to correct
small issues before they become major problems. The metrics included in this document
help us to identify and understand on-site performance related to product health, and
maintenance & repair processes performance in support of that product. Performance
Metrics enable insight into application severity, operating practices, contract guarantees &
commitments, and contract financial health.
“Performance Metric” is a term used to describe the outcome of any process used to collect,
analyze, interpret and present quantitative data. It is 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 and projected future behavior.
“Benchmark” is another term frequently used to describe performance. Benchmark is
defined as a world-class performance standard relative to a specific performance metric. A
Benchmark represents and quantifies "world-class performance or best practice" of an
operation or of specific functions within that operation according to a specified performance
metric. A benchmark is determined by and represents actual, documented, sustainable
performance over time relative to some performance metric.
Caterpillar has invested significant time and resources to identify and develop performance
metrics that quantify and trend product and project health. The Global Mining team has
documented actual mining equipment performance in many areas and is confident
representing some results as Benchmarks.
Caterpillar has concentrated its effort on mining loading, hauling fleets, and most
benchmarks in this document pertain to them. Regarding other mining equipment, best
estimates for benchmarks are included where sufficient data was available.

I. MEM Top Tier Metrics
The following measures are “Top Tier” performance metrics or Key Performance Indicators
(KPI’s) that enable management to quantify, assess, and monitor product health and site
performance.
To accurately calculate, understand, interpret, and take corrective action to improve top tier
metrics, it is imperative for each mine site to clearly define and document their “time usage
model”. It’s also imperative that all relevant parties agree to the defined time usage model.
The time usage model is used to define categories for available & non-available time,
stoppage codes, production & operational delay codes, and accurately define departmental
responsibility for each category (i.e. production department or maintenance department).
For additional information, please refer to Section IV titled “Time Usage Model”.
1. Physical Availability
Definition:
The amount of time that an equipment is capable of being used by the production
department divided by the total time in the period, expressed as a percentage.
Calculation Methodology:
Physical Availability =
Total Hours − Downtime Hours
Total Hours
.
(%)
*NOTE: “Total Hours” are typically based on either calendar or scheduled hours depending
upon the calculation methodology defined in the agreement or customer expectations.
Description:
The production department determines their target for production hours based on the
production plan and goals for the upcoming period (typically a year or month). This is then
converted from “hours” to “availability” that the operations department desires for their
production plan. This goal is then communicated to the maintenance department.
Physical Availability is the measure of time that a machine is “available” to be used by the
production department (i.e. not down for maintenance).
Data Sources:
Total calendar hours and machine downtime hours are obtained from machine workorder
history and/or the Fleet Management System (FMS) i.e. FMS MineStar Fleet system. FMS
information must be used to account for events that are not accompanied by a workorder.

Benchmarks & Targets:
Physical Availability benchmarks vary significantly by machine model, size, age, design
“maturity”, and design complexity. Physical Availability for mechanical Large Mining Trucks
in the 785 – 797 size class is well documented.
Our best data indicates that normal benchmarks are in the 88-92% range.
Machine/Model Availability
LMT: from 785 to 797 88 – 92%
LWL: 992-993-994 88 – 92%
LTTT: D10-D11 88 – 92%
LMG: from 16 to 24 88 – 92%
Table 1: Physical Availability Benchmarks for Large Mining Machines.

2. Mean Time Between Shutdowns (MTBS)
Definition:
The average operating time between all machine stoppages (planned & unplanned). This is
the average frequency of equipment downtime events, expressed in hours.
MTBS =
Operating Hours
Total Shutdowns
(hours)
Description:
MTBS is a measure combining the effects of inherent machine reliability and the
effectiveness of the maintenance management organization to influence results through
equipment stoppage avoidance.
Data Sources:
Operating hours are obtained from machine service meter unit (SMU) reading. Note that:
• Hours obtained from Fleet Management Systems (FMS) frequently do not directly
correlate with machine SMU hours due to coding of production delays and other
factors.
• 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 FMS may track and code
production delay hours separate from operating hours hence they must be acquired
from FMS.
Stoppage event count could be obtained from machine workorder history and/or FMS.
FMS information must be used to account for stoppage events that are not accompanied by
a workorder.
MTBS benchmarks vary significantly by machine model, size, age, and design “maturity” and
design complexity. MTBS for mechanical Large Mining Trucks in the 785 – 797 size class is
very well documented. Depending on machine models, the benchmark for a new truck fleet
can be in the range of 60-90 hours while that of a “mature” fleet (one that has undergone its
first round of major component replacements and rebuild) should be close to “as new
performance”.

Since benchmarks represent documented, “best in-class” performance sustainable over time,
we are frequently asked to assess MTBS through a range of results. The following table
represents our best data in this area.
Table 2: MTBS Benchmarks for mechanical driven LMT.
Benchmarks for mechanical mining trucks in the 769-777 size class are less well-known,
although it is believed their MTBS will be 10-20% higher than trucks in the 785-793 size.
Benchmarks for electric Large Mining Trucks (794,795,796,798) have not yet been studied
thoroughly since they have a smaller global population and have yet to accumulate sufficient
field hours and data to document performance.
Similarly, benchmarks for other large mining equipment are difficult to assess, since their
performance is not typically well documented. However, indications are that once MTBS data
is collected, analyzed and validated, the results will fall into the following ranges:
MTBS
Assessments/Characteristics
Machine Range
785-789 793 797
Excellent; high % of scheduled
downtime; Maintenance Mgmt.,
organization is highly proactive
> 65 hours > 60 hours > 55 hours
Acceptable; majority of downtime is
scheduled; substantial emphasis on
Equipment Mgmt.
55 - 65 hours 50 - 60 hours 45 - 55 hours
Marginal; approx. half of all downtime
is scheduled; Maintenance Mgmt.
discipline is not fully functional
Fair; < 40% downtime is scheduled;
minimal effort on Equipment Mgmt.
Poor; only PM's are scheduled;
Equipment Mgmt. organization is
purely reactive
< 35 hours < 30 hours < 25 hours

Machine / Model MTBS
LTTT: D10 / D11 55 to 75 hours
LWL: from 992 to 994 55 to 75 hours
LMG: 16 MG 95 to 105 hours
LMG: 24 MG 55 to 75 hours
HEX: 5000
HMS: from 6015 to 6030
55 to 75 hours
HMS: from 6040 to 6090 45 to 65 hours
Table 3: MTBS guidelines for Mining machines.

3. Mean Time To Repair (MTTR)
Definition:
The average downtime for machine stoppages. This is the average duration of equipment
downtime events (planned + unplanned), expressed in hours.
MTTR =
Total Downtime Hours
Number of Shutdowns
(hours)
Description:
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
efficiency of the maintenance management organization to deliver rapid execution of
repairs.
Data Sources:
Downtime hours are obtained from machine workorder history and Fleet Management
System. FMS information must be used to account for downtime that is not accompanied by
a workorder. Maintenance stoppages often have repair delays. Therefore:
• MTTR should include repair delay time in the downtime history calculation.
• If delay times are known, MTTR should be calculated both with and without delays.
Shutdown count could be obtained from machine workorder history and FMS. Once again,
FMS information must be used to account for shutdown events that are not accompanied by
a workorder.
MTTR benchmarks vary somewhat by machine model, size, and design complexity but to a
much lesser extent than MTBS. Machine age is the primary driver of MTTR. MTTR for
mechanical Large Mining Trucks in the 785 – 797 size class is very well documented. The
following table represents our best data in this area.
Machine/Model MTTR
785-789-793 3-6 hours
797 4-7 hours
Table 4: MTTR Benchmarks for Mechanical LMT.

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.
Benchmarks for mechanical mining trucks in 769-777 size class are less well-known;
although it is believed that their MTTR will be 10-20% lower compared to trucks in 785-793
size class.
Similarly, benchmarks for other large mining equipment are difficult to assess, since their
performance is not typically 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 truck fleets.
Note: As a reminder, poor availability is primarily driven by two factors: repair frequency
(MTBS), and repair duration (MTTR). When mine sites suffer from poor availability, a
common mistake of maintenance departments is to target reducing MTTR (faster repair
times) and stoppage frequency (MTBS) is often overlooked. In general, the primary focus
area should be to concentrate on improving MTBS (improving machine reliability) striving
toward benchmark or target levels. Once MTBS performance is reasonable, additional
efforts can then focus on reducing MTTR. For additional information, please refer to Section
VI titled “Rainbow Graph”.

4. Availability Index
Definition:
The ratio of MTBS (average shutdown frequency) to the sum of MTBS and MTTR (average
shutdown duration), expressed as a percentage.
Availability Index is a variation of the physical availability equation and eliminates any
differences in utilization by using machine operating hours, downtime hours, and the
quantity of stoppages. For additional information, please refer to Section VI titled “Rainbow
Graph” and Section VIII titled “Impact of Utilization on Physical Availability”.
Availability Index =
MTBS
MTBS + MTTR
X 100 (%)
Description:
Availability is the result of the frequency and duration of downtime events (shutdowns).
Because of the mathematical relationship between MTBS, MTTR, and Availability Index, the
result shows which of the other two factors had the greatest influence upon that availability.
This allows management to react appropriately to changes in the Availability Index by
focusing efforts and resources on the frequency (MTBS) or duration (MTTR) of downtime
events.
Data Sources:
Since Availability Index is derived from MTBS and MTTR, the data sources for those two
metrics are applicable here as well.
Availability Index benchmarks vary significantly by machine model, size, age, and design
“maturity” and complexity. Availability Index for mechanical Large Mining Trucks in the 785
– 797 size class is very well documented. The following table represents our best data in this
area.
Machine/ Model Availability Index
785 - 797 86 – 90%
Table 5: Availability Index Benchmarks for Mechanical LMT.
Benchmarks for trucks in the 769-777 size range are less well-known although the
Availability Index will be somewhat higher (possibly 2 to 3%).

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 trucks fleets. For larger machines, e.g. 24 MG and 5000 series
HEX, availability index is anticipated to be 3 to 4% lower while for smaller machines, e.g.
16H, availability index is likely to be 1 or 2% higher.
Note: Availability Index is a valuable metric to use to determine where the maintenance
team should focus improvement efforts. The generic process steps are:
• Calculate MTBS, MTTR, and Availability Index (for each machine model).
• Compare each metric against defined benchmarks / targets.
• Questions to ask:
o Is current performance near or at benchmarks / targets?
o Is there opportunity to improve?
o Is Availability Index meeting expectations or not?
o Are MTBS and MTTR meeting or near benchmarks / targets?
• Suggested actions if:
o Availability Index is within benchmark range and MTBS & MTTR are near
benchmark levels → No action needed.
o Availability Index is below expectations, what metric is driving poor
availability, MTBS or MTTR? Once defined, target corrective actions to
improve that metric.
o Availability Index is within benchmark range but either MTBS is below
benchmark levels or MTTR is above benchmark levels, define corrective
actions to improve that metric.
• Reminders:
o MTBS = average stoppage frequency.
o MTTR = average stoppage duration.
o If analysis shows that MTBS is below target and requires improvement, site
improvement plans should target the top issues by occurrence.
o If analysis shows that MTTR is above target and requires improvement, site
improvement plans should target the top issues by downtime.
For additional information, please refer to Section VI titled “Rainbow Graph”.

5. Contractual Availability
Definition:
The ratio of time that a machine is capable of functioning in the intended operation (available
hours) to total hours in the period, 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.
Contractual Availability =
Total Hours∗
− MARC Downtime Hours
Total Hours∗
.
X 100 (%)
*NOTE: “Total Hours” are typically based on either calendar or scheduled hours depending
upon the calculation methodology defined in the agreement or customer expectations.
Description:
Contracts are written to ensure that production equipment is available for operation for a
sufficient number of hours for the mine’s production goals. They target reasonable,
predetermined operating costs. The specific provisions of a contractual availability
guarantee vary significantly from site to site. Some examples of contract differences include,
but are not limited to the following:
• Time the contractor will be given credit for (available hours);
• Time the contractor will be held accountable for (contractual downtime);
• Other specific exclusions such as tires, dump bodies, welding, GET, undercarriage,
accident damages, etc.
Contracts frequently specify exclusions on downtime that apply to areas outside of a
contractor’s span of control 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 vary so widely from one contract 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. Target performance should
be to comply with the provisions defined within the contract.
Data Sources:
“Total hours” is 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, stand-by
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 FMS.
MARC (Maintenance and Repair Contract) downtime hours are obtained from the machine
workorder history as well as the Fleet Management System. FMS 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.
There is no benchmark that is applicable to the Contractual Availability performance metric.
Target performance should be to comply with the provisions defined within the contract.

6. Percentage Scheduled Downtime
Definition:
The percentage of total “maintenance & repair” downtime hours performed in a given period
that have been planned and scheduled.
% Scheduled Downtime =
Scheduled Downtime Hours
X 100 (%)
Description:
A high percentage of unscheduled downtime incidents results in very inefficient use of
resources and excessive costs. Personnel are frequently shuffled from one job to another
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 globally has shown that average downtime for unplanned
/ unscheduled work is up to eight times greater than the downtime for planned / scheduled
activities.
Data Sources:
Downtime hours are obtained from machine workorder history and Fleet Management
system. FMS 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” to track the number
of downtime hours that are scheduled.
Scheduled Downtime Hours for mechanical Large Mining Trucks in the 785 – 797 size class
is very well documented. Scheduled Downtime Hours for electric drive trucks is expected to
fall within a similar performance range.
Mines with highly effective equipment management processes in place are consistently able
to execute 75% of its overall 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.

7. Percentage Scheduled Events
Definition:
The percentage of total “maintenance & repair” shutdown events performed in a given
period that have been planned and scheduled.
% Scheduled Events =
Scheduled Shutdowns
Total Shutdowns
X 100 (%)
Description:
A high percentage of unscheduled shutdowns results in very inefficient use of resources and
excessive costs as personnel are frequently shuffled from job to another. Facilities and
manpower requirements need to be sufficiently large to accommodate large swings in the
quantity of machines down for repairs.
Data Sources:
Downtime events should be obtained from machine workorder history and Fleet
Management System. FMS information must be used to account for events that are not
accompanied by a workorder.
Individual workorders should be coded as “scheduled” or “unscheduled to track the number
of shutdown events that are scheduled.
Scheduled Events for mechanical Large Mining Trucks is well documented. Although %
Scheduled Events for electric drive trucks and other mining machines (electric rope shovels,
hydraulic mining shovels, drills) is not well known, it is expected to fall within a similar
performance range.
Mines with highly effective equipment management processes in place are consistently able
to execute about 55% of their overall Maintenance & Repair stoppage events on a scheduled
basis. We believe that this criterion holds true for other mining equipment as well.

8. Maintenance Ratio
Definition:
Maintenance Ratio is a dimensionless ratio of maintenance and repair man-hours to machine
operating hours.
Maintenance RatioCharged =
Maintenance & Repair ManHours
Operating Hours
Description:
Maintenance Ratio is an indication of the amount of effort required to keep equipment in
service, labor efficiency, and the workforce effectiveness. Maintenance Ratio can be
calculated as either “charged” or “direct”. “Charged” or Direct” Maintenance Ratio considers
only work order manhours (direct labor). Examples of labor hours that are not included in
this calculation are repair shop, Component Rebuild Center, etc. “Overall” Maintenance Ratio
includes all the elements of “charged” Maintenance Ratio plus staff, supervision and idle
time.
Data Sources:
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, inspection
and diagnostic time. Labor inefficiencies such as delays or wait time for bay space, parts,
tooling, literature, repair support equipment, decision making, etc. should be included in this
calculation. Charged Maintenance Ratio would typically exclude time associated with lunch
breaks, meetings, smoke breaks, toilet breaks, etc. as they are not direct labor hours.
Operating hours are obtained from machine service meter reading and should include
production delay hours. Please note that hours obtained from FMS (Fleet Management
Systems) frequently do not agree with machine SMR (Service Meter Reading) due to coding
of production delays, etc.
Maintenance Ratio benchmarks vary significantly by machine model, machine relative size,
age and design “maturity” and complexity. Maintenance Ratio for large Off-Highway Trucks
in the 785 – 797 size class is very well documented.
For mechanical Large Mining Trucks (785-793), 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 man- hours/ operating hour.

Benchmarks represent documented, best-in-class performance sustainable over time.
Caterpillar is frequently asked to assess performance through a range of results. The
following (table 5) represents our best data in this area for mechanical Large Mining Trucks.
Maintenance Ratio
Assessments/ Characteristics
Machine Range
785-793 797
Excellent; high % of scheduled downtime; Equipment
Mgmt., organization is highly proactive
0.30-0.35 0.45-0.50
Acceptable; majority of downtime is scheduled;
substantial emphasis on Equipment Mgmt.
0.35-0.40 0.50-0.55
Marginal; approx. half of all downtime is scheduled;
Equipment Mgmt. discipline is not fully functional
0.40-0.50 0.55-0.65
Fair; < 40% downtime is scheduled; minimal effort on
Equipment Mgmt.
0.50-0.60 0.65-0.75
Poor; only PM's are scheduled; Equipment Mgmt.
organization is purely reactive
>0.60 >0.75
Table 5: Maintenance Ratio Benchmarks for mechanical LMT.
Benchmarks for mechanical mining trucks in the 769-777 size range are less well-known
although it is believed their Maintenance Ratio will be slightly lower than that of the 785-
793 size class.
Similarly, benchmarks for other large mining equipment are very well-known and
documented. Refer to table 6 below for our best data in this area for Hydraulic Mining
Shovels in the 6030 to 6090 size range.

Maintenance Ratio
Machine Range
6030 - 6090
0.32 to 0.48
0.48 to 0.54
0.54 to 0.59
Fair; < 40% downtime is scheduled; minimal effort
on Equipment Mgmt.
0.59 to 0.66
> 0.66
Table 6: Maintenance Ratio Benchmarks for HMS.
The following (table 7) represents our best data in the benchmark area for the 7495 Electric
Rope Shovels.
Maintenance Ratio
Machine Range
7495
0.40 to 0.51
0.51 to 0.55
0.55 to 0.60
Fair; < 40% downtime is scheduled; minimal effort on
Equipment Mgmt.
0.60 to 0.74
> 0.74
Table 7: Maintenance Ratio Benchmarks for ERS.
The tables below are the results of a Labor Analysis using Caterpillar MARC Centerline
BUILDER files. The tables can be used to determine manpower needs to support the
Caterpillar fleet. A detailed review of the assumptions made in this analysis is required by
the CAT dealer and customer site maintenance department. The information below provides
benchmarks for onsite labor to compare existing fleet Maintenance Ratios with fleet
availability.

Machine/ Model Maintenance Ratio
14 MG 0.15
16 MG 0.17
18 MG 0.18
24 MG 0.26
Table 8: Maintenance Ratio Benchmarks for Motor Graders.
824 WD 0.14
834 WD 0.15
844 WD 0.16
854 WD 0.20
Table 9: Maintenance Ratio Benchmarks for Wheel Dozers.
MD 6420 0.14
MD 6540 0.15
MD 6640 0.16
Table 10: Maintenance Ratio Benchmarks for Drills.
Table 11: Maintenance Ratio s for Track-Type Dozers.
D8 0.16
D9 0.17
D10 0.20
D 11 0.23

988 0.26
990 0.28
991 0.30
992 0.31
993 0.47
994 0.49
Table 12: Maintenance Ratio Benchmarks for Wheel Loaders.

9. Top Problems Summary
Description:
The distribution of problems affecting a fleet of equipment ranked by MTBS, MTTR, Impact
on Availability and costs.
For the purpose of the following calculations, Impact on Availability is synonymous with the
term “Unavailability”. ( Impact on Availability (System) = Unavailability (System) )
MTBS (System) =
Operating Hours
Number of Shutdowns (System)
(hours)
MTTR (System) =
Downtime Hours (System)
Number of Shutdowns (System)
(hours)
Unavailability (System) = (1 − Availability(Total)) X
Downtime Hrs (System)
Total Downtime Hrs (Machine)
(%)
Unavailability(System) =
DT Hrs (System)
Total Calendar Hrs (Period)
(%)
Cost per Hour(System) =
Cost (System)
Operating Hours
(USD per Hour)
Description:
All mining support operations have limited resources. The most successful operations
clearly understand their problems and therefore can establish priorities to focus their efforts
and allocate the appropriate resources on continuous improvement strategies.
Identifying and quantifying top problems by component, system, and/or process facilitates
the understanding of variables affecting the success of a mining operation. With this
information, the maintenance management organization should be able to recognize the key
issues on site and efficiently apply the necessary resources to improve.
• Components: Engine, transmission, final drives, differential, etc.
• Systems: Hydraulics, electrical, air conditioning, etc.
• Processes: Preventive Maintenance, Repair Management, Condition Monitoring, etc.
• Mining Success Factors: stoppage frequency (MTBS), shutdown duration (MTTR),
impact on Availability and Costs.

Data Sources:
Operating hours are obtained from machine service meter unit reading. Please note that
hours obtained from FMS often do not directly correlate with machine SMU due to coding of
production delays, etc.
Shutdown count and downtime hours are obtained from machine workorder history and
Fleet Management System. FMS information must be used for shutdown events and
downtime hours that are not accounted for by a workorder. Shutdown count must be
determined for the entire machine and also for individual major component and machine
system. This information is needed to assess the contribution of each component/ system
and to calculate Availability Index.
Please note the following:
• 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.
• Downtime must be determined individually for each component, machine system and
entire machine to assess the contribution. In case there are two or more maintenance
activities performed at the same time (or with a significant overlap), the leading cause
for downtime should be flagged as the reason for stoppage.
• Maintenance management must know the total cost to support and maintain each of
the machine systems and components to manage contract profitability. At a
minimum, it is vital to know the breakdown for repairs and rebuilds costs of each
major machine component. Most recordkeeping systems do a poor job of
documenting costs.
There are no applicable benchmarks for this metric. However, Caterpillar has developed
generic reference guidelines for mechanical Large Mining Trucks in the 785 – 793 size class.
These can be used to evaluate MTBS, MTTR and impact on Availability. This reference defines
reasonable targets for frequency of downtime events (MTBS), duration of downtime events
(MTTR), and impact on Availability for each machine system and component.
The data represents a site achieving a 90% Availability Index. The actual results are site-
specific due to factors such as application severity, environmental conditions, maintenance
and repair processes, and machine product issues.
The “Generic Pareto Reference – Large Mining Trucks” included in Section IV can be used as
a baseline until maintenance management has sufficient site-specific experience and history
to generate their own reference tables.
Apart from equipment management, other factors such as labor rates, transportation costs,
import duties, taxes, etc. influence costs. As such, it is impossible to define benchmarks that
are universally applicable to any given machine model. Caterpillar recommends projecting
budgetary cost and component lives to define cost per hour, then comparing to actual cost.

10. 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.
Asset Utilization =
Operating Hours
Total Calendar Hours
X 100 (%)
Description:
How effectively the Operations Department schedules equipment and efficiently utilizes it
has significant implications for Maintenance. If machines are scheduled for use 24 hours a
day, 7 days a week, Maintenance should work with Operations to find windows of
opportunity to perform maintenance and repairs without increasing downtime. These
opportunities typically occur during scheduled shutdowns but may also come during
operational delays (shift change, lunch breaks, refueling) or production delays (blasting).
In all circumstances, Operations and Maintenance need to recognize that they are working
together toward common goals of high availability, good machine reliability and the lowest
possible cost per unit of production.
Data Sources:
Operating hours are obtained from machine service meter reading and from Fleet
Management System should include production delay hours. Note, hours obtained from FMS
do not directly correlate with machine SMU hours due to coding of production delays, etc.
Note that hours taken from machine SMU will be higher than those taken from FMS,
oftentimes by as much as 10 percent.
Total calendar hours is the total time in the period to be analyzed, e.g. 8760 hours / year, 720
hours / 30-day month, 168 hours / week, etc.
Asset Utilization for mechanical Large Mining Trucks in the 785 – 793 size class is well
documented. Ultra-class 797 trucks and electric Large Mining Trucks (794,795,796, 798)
would be expected to fall within similar performance range.
Mines with highly effective equipment management processes can achieve Asset Utilization
of 90% (over 6500 operating hours per year). We believe this Benchmark is valid for other
production mining equipment; however, the Benchmark for less utilized, non-production
equipment, although unknown, may be significantly less.

11. Utilization of Availability
Definition:
The proportion of time that a machine is operating (operating hours) divided by the available
time in the period, expressed as a percentage.
Utilization of Availability =
Operating Hours
Available Hours
X 100 (%)
Description:
How effectively the Operations Department schedules equipment and efficiently utilizes it
has significant implications for Maintenance. If machines are scheduled for use 24 hours a
day, 7 days a week, Maintenance should work with Operations to find windows of
opportunity to perform maintenance and repairs without increasing downtime. These
opportunities typically occur during scheduled shutdowns but may also come during
operational delays (shift change, lunch breaks, refueling) or production delays (blasting).
In all circumstances, Operations and Maintenance need to recognize that they are working
together toward common goals of high availability, good machine reliability and the lowest
possible cost per unit of production.
Data Sources:
Operating hours are obtained from machine service meter unit (SMU) reading and should
include production delay hours. Note that hours obtained from FMS frequently do not
directly correlate with machine SMU hours due to coding of production delays, etc. Note that
hours taken from machine SMU will be higher than those taken from FMS, oftentimes by as
much as 10 percent.
Available hours is the available time in the period to be analyzed (i.e. not down for
maintenance or maintenance delays). A simple equation for available hours is total calendar
hours minus downtime hours.
Utilization of Availability for mechanical Large Mining Trucks in the 785 – 793 size class is
well documented. Ultra-class 797 trucks and electric Large Mining Trucks (794,795,796,
798) would be expected to fall within similar performance range.
A reasonable Benchmark measure for Utilization of Availability is 90% (over 6500 operating
hours per year). We believe this Benchmark is valid for other production mining equipment,
however Benchmarks for less utilized, non-production equipment, although unknown, may
be significantly less.

12. PIP/PSP Completion Rate
Definition:
A tracking tool used to monitor the status of implementation of factory programs.
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.
Tracking service support programs is important because of machine failure risk. As
previously discussed, machine stoppages impact availability and maintenance organizations
struggle with efficiently using manpower and resources during unscheduled shutdowns.
Programs defined as "after failure only" should not be included in the calculation. The
support programs include:
• PI1xxxx: Safety
• PI3xxxx: Priority
• PS4xxxx: Before or After Failure.
Data Sources:
Factory programs should be received on site through the dealer Technical Communications
network and include all necessary information to determine applicability. The site and
dealer teams should monitor the completion status using program identification number,
machines affected, dates of issue and termination. Machine serial number and hour meter
information are obtained from the machine history at the site.
Key factors such as parts availability can impact management's ability to complete a
program. 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), therefore, there is no
applicable global Benchmark. However, compliance is critical to project success and
common-sense dictates that a higher completion percentage 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.
Guidelines for PIP support programs should fall within the targets for Caterpillar Service
Operations functions, namely:
• PI1xxxx: 90% completion within 90 days of issue
• PI3xxxx: 90% completion within 180 days of issue
• PS4xxxx: To be determined by site
• PS5xxxx: To be determined by site

II. MEM Application / Operational Metrics
The application which mining equipment is used is somewhat fixed for a specific property
although mines do change over time, typically becoming more severe (deeper, steeper,
longer hauls, etc.). Environmental conditions such as altitude, ambient temperature, and
precipitation are relatively fixed and it’s up to the mine operation to decide how to manage
these conditions.
Mining operations have some control in machine selection and machine application, but
many challenges cannot be eliminated and have to be managed. Mining engineering and
Operations departments can establish criteria for haul road design and maintenance and
many factors can significantly influence equipment performance, depending on
design/planning, adherence, and management. Considerations for haul road management
are:
• Mine planning (road layout, width, gradient, traffic patterns, haul distances)
• Mining operations (payload management, speed limits, operator behavior &
training)
1. Fuel Consumption
Definition:
The fuel consumption (average engine fuel burn rate) for a fleet of equipment expressed in
volume (gallons or liters) per hour.
Description:
Machine application has a direct impact on the overall performance of that equipment. Given
that applications change with respect to haul road grades (grades become steeper or
shallower), time spent on grade (increasing pit depth), haul distances (typically become
longer), etc., it would be expected to see variation in application severity over time. These
variations will be reflected in changes in fuel consumption, particularly for LMT.
Engine life is a function of the cumulative amount of fuel burned over its lifetime than simply
by operating hours. Therefore, the average fuel burn rate (or engine load factor) should be
considered as a key performance indicator to assess the impact in engine life due to changes
in application severity for the operation of a fleet of equipment.
Maintenance must recognize the impact of changing operating conditions and modify
maintenance practices and maintenance management strategy accordingly (e.g. component
replacement plans). If application severity increases, equipment will experience accelerated
wear resulting in shortened component lives (in operating hours) and increased costs.
Ongoing evaluation of mine operations via tracking, trending, and reporting fuel burn rate
should be a normal practice of any Maintenance Department.

Fuel Consumption =
Total Fuel Consumed
Machine Service Meter Unit
Data Source(s):
Total fuel consumption during the period can be obtained from VIMS, the ECM or, on
machines not equipped with VIMS, from accurate/verified fuel addition records.
Benchmarks and Targets:
Since there is a correlation between fuel rate and application severity, there is no applicable
Benchmark. However, target fuel rate based on historical data and/or levels generated from
application modeling software should be known, understood, and the actual fuel rate relative
to that target monitored over time as an indication of changes in applicationseverity.

2. Payload Management
Definition:
Payload management is an analysis of payload distribution for a fleet of Large Mining Trucks
expressed in terms of Caterpillar’s 10/10/20 truck overload policy.
Description:
Machine application conditions have a direct impact on equipment performance. Payload
management is the extent to which haul trucks are operated within safe working conditions
and design parameters and is important when assessing application conditions. Increasing
production demands have driven the mining industry to utilize haul trucks and loading tools
of larger size and capacity. Furthermore, production studies found that optimizing truck-
loader pass match, frequently to three to four pass loading, will yield production advantages,
by reducing the time that the haul truck sits under the loading tool.
Factors such as normal variations in material density, moisture content, material blast
fragmentation, bucket size, loader operator skill level, and material carry-back/debris add
to variability thus complicating the task of payload management. Payload management
should be considered a key performance indicator for assessing application conditions for
machine fleets. Equipment managers should be diligent in continuously evaluating a mine’s
payload management practices through regular documentation, tracking, and trending of
payload data.
Payload management can be quantified using one of the following methods:
• Actual count analysis: Data from the Truck Payload Management System (TPMS) or
VIMS-TPMS reports can be used to count the actual number of loads within each
range defined by the 10/10/20 policy and compared for compliance with the
specified guidelines.
• Statistical analysis: Data from TPMS or VIMS-TPMS reports can also be used to
perform a statistical analysis for loads within each range defined by the 10/10/20
policy to compare with specified guidelines. The analysis assumes that the data is
represented by a normal distribution (bell-shaped curve). Before beginning, users
should view a frequency distribution to verify the data being modeled is indeed
normally distributed. Normal variations and operating practices on a mine can cause
the data to follow a bi-modal, skewed or other distribution. If the data is not “normal”,
the model predicted by the statistical analysis will be invalid.
The cut-off limit is an important consideration when performing payload management
analysis using the statistical approach. Ignoring loads that are less than 50% of the target

payload will reflect the actual shape of the distribution curve. Eliminating zero and very
small loads is valid since the main goal is to define the upper limits of the distribution curve.
Data Source(s):
The specifications for gross machine weights can be obtained from the Caterpillar
Performance Handbook, machine specification sheets, and information contained in the
factory documentation for the 10/10/20 overload policy.
Empty machine weights published by the factory represent values of machines as leaving the
factory, therefore actual operating empty machine weight is best obtained from scale data.
Factors such as body design, tires, optional equipment, and carry-back or other debris
accumulation can have a significant effect on the accuracy of published estimates.
Payload data is obtained from the Truck Payload Management System (TPMS) or VIMS-TPMS
reports.
Benchmarks:
There is no Benchmark that is applicable to the payload management metric. Target
performance should be to comply with the CAT 10/10/20 policy (Figure 1), or as per any
other documented/agreed payload policies.
Figure 1: Payload Distribution
The policy stipulates that:
• “No more than 10 percent of payloads may exceed 110% of the target payload”.
• “No single payload shall ever exceed the maximum allowable payload, typically 120%
of the target payload”.

• “The mean of the payloads shall not exceed the target payload, hence no more than
50 percent of payloads may exceed the target payload.”
• “The rated capacity of the tires should always be considered in any evaluation”.
Additional comments include:
• Loads exceeding 110% of target payload will have an adverse effect on machine
durability.
• Payloads higher than 120% of target payload exceed the safe working design
parameters that the machine is certified [braking, steering, and rollover protection
system (ROPS)].
Although no benchmark applies to this metric, a different benchmark was found to apply to
the standard deviation of payloads in a given population. This level of variation among
payloads, combined with a reasonably well-matched bucket to body size capacity is most
likely to result in optimum payload management performance.

3. Haul Cycle Detail
Definition:
An analysis of the operations on a particular haul road layout for a fleet of Large Mining
Trucks that enables the Equipment Manager to isolate the most significant haul road
attributes and defects affecting overall fleet performance and costs.
Description:
Machine application conditions have a direct impact to its resulting equipment performance.
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 in-depth 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 have detrimental effects on machine performance and operating
costs. Therefore, haul cycle definition should be considered a key performance indicator for
assessing application severity for a fleet of equipment and the equipment manager must be
diligent in the ongoing evaluation of operating practices through his regular documentation,
tracking, and trending of the haul cycle.
Haul cycle detail can be quantified using the following methods:
• Average haul cycle distance: Data from Truck Payload Management Systems (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 cycle times are calculated by dividing the total travel
times (empty + loaded) from TPMS or VIMS-TPMS reports and by the count of actual
loads hauled during the period considered. Average idle time is calculated by dividing
the sum of the total stop times (stopped empty + stopped loaded) and total load times by
the count of actual loads hauled.
• Average haul cycle speeds: Average speeds (empty + loaded) are calculated by dividing
the total travel distances (empty + loaded) by the total travel times (empty + loaded).
Data Source(s):
Haul cycle data is obtained from the Truck Payload Management System (TPMS) or VIMS-
TPMS reports.

Benchmarks:
There are no applicable Benchmarks for haul cycle metrics. There are however practical
limits that apply to operational parameters such as speeds, grades and roads. The Equipment
Management team 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.

III. MEM M&R Process Metrics
The main goal of the maintenance organization is to preserve the primary function of
equipment through the machine’s entire productive life. It must establish all needed
routines and processes to minimize equipment downtime and the resulting production
losses. Maintenance events must be efficiently and effectively managed and executed,
triggering all necessary processes used by the maintenance team to support the goal of
“repair before failure.” This will maximize equipment availability at the lowest cost.
Effective maintenance organizations understand the need of well-established processes,
supported by appropriate resources, and executed by skilled and well-trained personnel.
The study of and partnership with these organizations has built a solid understanding of the
processes and techniques that are effective, as well as those that must be identified in order
to correct or avoid them.
The following sections will describe the Maintenance & Repair process metrics that
Caterpillar recommends for each M&R discipline.
1. Preventive Maintenance
The following metrics are indicators of the performance and / or contributions of the
Preventive Maintenance process to the end results of the project, i.e. equipment reliability
and availability.
1.1. MTBS AFTER PM
MTBSAfter PM =
Total Operating Hours to First Stop
Number of PM Services
(Hours)
The average operating hours to the first stop after each PM service is a valid indication of PM
quality and effectiveness. The Benchmark (best in class) for Large Mining Trucks is 105
hours; a realistic target is 2 to 3 times the overall MTBS. Tracking and trending this metric
monthly offers a reasonable representation of PM quality and effectiveness. It is important
to compare MTBS after PM with PM duration. A clear positive correlation between the two
should be observed, such as the more PM duration, the longer resulting MTBS after PM.
1.2. Unavailability PM
UnavailabilitySystem = (1 − Availability) X
PM Downtime Hours
.
X 100 (%)

Unavailability due to Preventive Maintenance quantifies the impact of PM on availability.
There is no Benchmark associated with this metric, but a reasonably valid target is in the
range of 2.75 to 3.25% for mechanical Large Mining Trucks in the 785 – 793 size class.
The result of this measure should be taken in context with MTBS after PM. If unavailability
due to PM is below the range and MTBS after PM is low, it is a safe assumption that
insufficient time and effort is being placed on PM. Conversely, if unavailability due to PM is
above the range and MTBS after PM is high, one can assume that the site is placing substantial
emphasis on the value of PM.
1.3. MTTRPM
MTTRPM =
Total PM Downtime Hours
Number of PM Services
(Hours)
The average downtime hours dedicated to PM is an indication of PM efficiency. There is no
Benchmark associated with this measure, but a reasonable target is in the 7.75 to 8.5-hour
range for Large Mining Trucks on a 250-hour PM service interval. The target for an
equipment that is on a 500-hour PM service interval is higher, i.e. up to double that of the
250-hour interval. If the average PM downtime is within the range, and MTBS after PM is
low, it is safe to assume that insufficient time and effort is placed on PM.
Conversely, if average PM downtime is above the range and MTBS after PM is high, one can
assume that the site is placing substantial emphasis on the value of PM. Additionally, one
should consider the impact of efficiency factors such as facilities, tooling, training, planning
& scheduling, etc. when assessing MTTR PM.
1.4. Service Accuracy
A measurement of Preventive Maintenance execution timeliness based on a statistical
calculation that predicts the probability that the next PM service will occur within the
recommended range (+/- 25 hours of target interval). The calculation is based upon past
performance and assumes that PM intervals are normally distributed about the mean. The
Benchmark for Service Accuracy is 95% but an aggressive target that will yield excellent
results is 90%.
Notes / Comments:
• The Caterpillar equation of service accuracy is NOT the count of PM services within
range divided by the total quantity of PMs. Using this equation, there is no impact on
results if the PM was out-of-range by one hour or by 100 hours.
• The Caterpillar calculation uses mean and standard deviation. Therefore, if actual
PMs intervals have a lot of “scatter” in the data points, the Service Accuracy result will
be negatively impacted due to high standard deviation. Caterpillar’s method will yield
lower results if PM was out-of-range by 100 hours vs out-of-range by one hour.

1.5. Backlogs Executed During PM
Backlogs executed during Preventive Maintenance is a good indication of how well the
organization is using the “window of opportunity” presented by PM to maintain the
equipment at a standard that will enhance product reliably. There is no Benchmark or target
for this measure.
1.6. Backlogs Generated During PM
The number of defects identified and entered into the Backlog Management system during
the execution of Preventive Maintenance. Since this measure is a direct function of the
number of machines being monitored as well as their condition, no Benchmarks or targets
are applicable. Backlogs generated during PM quantifies the use of the “window of
opportunity” presented during the PM shutdown for defect detection (an element of
Condition Monitoring).

2. Condition Monitoring
The following metrics are indicators of the performance and / or contributions of the
Condition Monitoring process to the end results of the project.
2.1 Mean Time Between Failures (MTBF)
MTBF =
Operating Hours
Number of Failures
(Hours)
The average operating time between equipment failures (unplanned stoppages); the inverse
of failure frequency, expressed in hours. Other expressions for failures are unscheduled
stoppages or breakdowns. Failures may be the result of technical product issues, i.e.
equipment unreliability, or due to maintenance & repair neglect, i.e. equipment management
ineffectiveness in the area of problem avoidance. We have not established a Benchmark for
MTBF and do not have sufficient confidence at this time to provide a reasonable target.
(Please see the Glossary for our definition of equipment Failure.)
2.2 UnavailabilityUnscheduled
UnavailabilityU/S = (1 − Availability) X
Unscheduled Downtime
Total Downtime
.
X 100 (%)
Unavailability due to unscheduled downtime quantifies the impact of unscheduled events on
availability. There is no Benchmark associated with this metric, but a reasonably valid target
is < 2% for mechanical Large Mining Trucks in the 785 – 793 size class. If unavailability due
to unscheduled downtime is significantly higher than 2%, it is reasonable to assume that
gaps exist in the detect-plan-execute cycle therefore improvements to the Condition
Monitoring, Planning & Scheduling and/or repair execution areas will be necessary.
Increasing unavailability due to unscheduled downtime is a valid predictor of pending
problems and may very well predict future shortages of manpower andfacilities.
2.3 Failure Reduction
FR =
Unscheduled Hrs (6 months RA) − Unscheduled Hrs (last month)
Unscheduled Hours (6 month rolling average)
X 100 (%)
Failure Reduction is a means of quantifying the impact of Condition Monitoring in its efforts
toward failure/problem avoidance. Since unscheduled events are inherently more difficult
and inefficient to deal with in terms of the time required to make unscheduled (unplanned)
repairs, Failure Reduction should be the primary focus of Condition Monitoring activities.
Because the opportunity to improve in this area is highly dependent upon the amount of
unscheduled downtime taking place at the site, there is no Benchmark or target for Failure

Reduction. In any event, the result should be positive indicating a decline in the percentage
of unscheduled downtime.
2.4 Condition Monitoring Total Savings
CM Total Savings = CM Cost Savings − CM Program Cost (US $)
Condition Monitoring Total Savings defines the “Value Proposition” for Condition
Monitoring. In other words, the total savings generated by Condition Monitoring (cost of
after-failure repairs – cost of preventive, before-failure repairs) must be greater than the
cost of implementation and execution of the Condition Monitoring program. There is no
Benchmark for this metric, but the target should be a positive value (net savings as a result
of Condition Monitoring).
2.5 Total Backlogs Generated
Backlogs Generated Total = Total Backlogs Generated in the Period
The number of defects identified and entered into the Backlog Management system during a
specified period (typically one month). This metric assesses the Condition Monitoring effort
and the ability to successfully detect potential issues. Since this measure is a direct function
of the number of machines being monitored, there is no Benchmark or target.
2.6 Working On Target
Working on Target = % Backlogs on Problem List
The percentage of Backlogs generated which address issues that appear on the “Top 10”
historical problem list. The result yields the % of Condition Monitoring actions that are “On
Target” relative to the key issues affecting site performance. There is no Benchmark or target
for this metric however, if all issues on the problem list are not producing Backlogs, the
Condition Monitoring effort may be misdirected.
2.7 Backlogs Generated By Origin
Backlogs GeneratedBy Origin = Backlogs Generated in the Period by area of origin
Backlogs Generated by Origin identifies which areas that are or are not contributing to
efforts by Condition Monitoring in failure detection. There is no Benchmark for Backlogs
Generated by Origin, however, if the quantity of Backlogs generated by operators, inspectors,
the PM crew, the shop crew, etc. is low, additional emphasis should be placed on the
offending party(s) to encourage their participation in the defect detection process.
Conversely, if the percentage of “shop-found” defects is disproportionately high, the other
areas must be encouraged to increase their involvement since “shop found” defects are
typically far less efficiently executed due to the inability to plan the workload. There are no
Benchmarks or targets related to this metric.

2.8 Detection Level
Potential Failure Detection (PFD) =
Recorded Backlogs
Total Defects Pending
X 100 (%)
This metric is based on a comparison between the number of Backlogs recorded in the
system and the defects that can be detected in an inspection of a randomly selected sample
of machines (10% of total fleet at minimum). Although inspections are limited to visual
inspection, the Potential Failure Detection level can be used to assess the capability and
thoroughness of Condition Monitoring routines.
This metric is typically used:
• By highly trained technical personnel (CM Fleet Analysts, Inspections
Supervisors/Team Leaders, Maintenance managers, or other Mining Equipment
Management Subject Matter Experts).
• As an ongoing means of Condition Monitoring and process compliance. In most cases,
detection level would be measured when a machine is down for an extended period
of time i.e. PM, other major/minor repairs etc. Short stoppages such as refueling, fluid
level checks, shift changes, operator breaks, etc. are not conducive for this type of
evaluation.
• During Site Assessments or M&R Gap Analysis. During these evaluations, the
assessment teams would typically measure detection level to determine the
robustness of M&R Processes, specifically machine inspections.
• When sites are experiencing low availability and/or low reliability (MTBS and MTBF).
In these instances, sites are usually found to perform well-below expected detection
level.
As a reminder, robust M&R processes and effective cost management are heavily reliant on
a repair-before-failure maintenance strategy. Therefore, low detection level (significantly
below 80%) impairs the maintenance organization from proactively ordering parts,
planning resources, scheduling and completing repairs.

3. Backlog Management
The following metrics are used to evaluate the ability of the Backlog Management process to
prioritize, control and manage problems identified through Condition Monitoring such that
they do not result in unnecessary downtime.
3.1 Total Backlogs Pending
The total number of defects identified by Condition Monitoring and pending in the Backlog
Management process. An indication of the pending workload and risk for failure. Since this
number is dependent upon the size of the fleet being managed, there is no Benchmark or
target for this metric.
3.2 Backlogs Pending by Machine
The total number of defects per machine identified by Condition Monitoring and pending in
the Backlog Management process. There is no Benchmark for this metric, however a
reasonable target is that there should be no more than five pending Backlog repairs per
machine.
3.3 Total Backlogs Generated
Backlogs Generated Total = Total Backlogs Generated in the Period
The number of defects identified and entered into the Backlog Management system during a
specified period (typically one month). This metric assesses the Condition Monitoring effort
in and ability to successfully detect potential problems before failure. Since this measure is
a direct function of the number of machines being monitored, there is no Benchmark or
target. Backlog generation should be viewed in the context of % scheduled downtime and, if
the percentage of scheduled downtime is low, the total number of Backlogs generated should
be correspondingly high.
3.4 Total Backlogs Executed
Backlogs Executed Total = Total Backlogs Executed in the Period
The number of Backlog repairs performed during a specified period (typically one month).
This metric evaluates the ability of the maintenance organization to react appropriately to
correct defects identified through the Condition Monitoring process. Since this measure is
related to the number of Backlogs in the system, there is no Benchmark or target, however,
there should be a balance between the number of Backlogs generated and executed.

3.5 Estimated Labor To Repair
The total estimated repair labor man-hours required to execute all pending Backlogs that
have been generated. This metric is an indication of severity of the Backlog workload and
the potential availability lost if manpower resources are insufficient to accomplish the task
at hand. There is no Benchmark for this metric, but a reasonable target is that the total
estimated repair labor man-hours required to clean up the Backlog list should be < 5% of
available man-hours labor for the month.
3.6 Backlog Status Summary
The Backlog Status Summary defines the number of pending Backlogs that are waiting for
planning (“Red phase”), waiting for parts / resources (“Blue phase”), and waiting to be
executed (“Green phase”). There is no Benchmark or target for the Backlog Status Summary
however this metric analyzed to identify any weak area(s) in the detect-plan -execute cycle
that may be delaying the Backlog repair execution process.
3.7 Backlogs > 30 Days Old
Measured from the date the Backlog was generated, this metric assesses the quality and
timeliness of the response of the Backlog Management system in its ability to respond
proactively to eliminate potential problems. It is important to note that Backlogs are
potential failures, thus Backlog age is an indication of the risk of failure that a site is under.
There is no Benchmark for this measure; however, an aggressive target is that no Backlogs
are greater than 30 days old.

4. Planning & Scheduling
The following metrics are used to evaluate how well the Planning and Scheduling process is
organized and functioning to ensure that planned activities can be accomplished both
efficiently and effectively and that they do not result in unnecessary downtime.
4.1 Percentage Scheduled Downtime
% Scheduled Downtime =
Scheduled Downtime Hours
X 100 (%)
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.
The Benchmark for percentage of scheduled downtime is 80% of maintenance and repair
downtime activity is executed on a scheduled basis. A reasonably aggressive target for most
sites is 60%.
4.2 Schedule Compliance By Hours
Schedule Compliance By Hours =
Scheduled PM & Repair Hours Executed
PM & Repair Hours Scheduled
X 100 (%)
Schedule Compliance (by hours) is the ratio of scheduled Preventive Maintenance and Repair
downtime hours actually performed to the Preventive Maintenance and Repair downtime
hours scheduled. There is no Benchmark for this metric, but the target should be in the range
of 90 to 100%. If the result is consistently 100%, it may be an indication that the schedule is
too conservative (does not provide sufficient “stretch”. Conversely, if the result is
consistently low, it could mean that the schedule is too ambitious, the workforce is
inefficient, or that the amount of unscheduled downtime during the period was such that it
interfered with work that had been previously scheduled.
4.3 Schedule Compliance By Events
Schedule Compliance By Events =
Scheduled PM & Repair Hours Executed
PM & Repair Events Scheduled
X 100 (%)
Schedule Compliance (by events) is the ratio of scheduled Preventive Maintenance and
Repair events actually performed to the Preventive Maintenance and Repair events

scheduled. There is no Benchmark for this metric, but the target should be in the range of
90 to 100%. Just as was the case for Schedule Compliance by hours, if the result is
consistently 100%, it may be an indication that the schedule is too conservative
(does not provide sufficient “stretch”). Conversely, if the result is consistently low, it could
mean that the schedule is too ambitious, the workforce is inefficient, or that the amount of
unscheduled downtime during the period was such that it interfered with work that had
been previously scheduled.
4.4 Components Exchanged (Scheduled)
𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐄𝐱𝐜𝐡𝐚𝐧𝐠𝐞𝐝 =
PCR′
s Scheduled & Executed
PCR′s Executed
X 100 (%)
Components Exchanged is the ratio of component replacements scheduled and actually
replaced to components replacements scheduled. There is no Benchmark for this metric, but
the target should be 100%.
4.5 Estimated Time To Repair Pending Backlogs
The total estimated repair downtime hours required to execute all of the pending Backlogs
that have been generated. At the time a repair request is entered (Backlog generated), the
Estimated Time to Repair (ETTR) must be identified to permit effective planning of required
corrective actions. This metric is an indication of extent of the Backlog workload and the
potential availability lost if resources are insufficient to accomplish the task at hand. There
is no Benchmark or target for this metric; it serves as a tool for the planning process to enable
if to “manage” availability by scheduling work in such a way that availability goal can be met.
4.6 Estimated Time To Replace Overdue Components
The total estimated repair downtime hours required to replace all overdue components.
Standard jobs for component replacement will forecast the estimated time to replace each
component. This metric is an indication of extent of the component replacement workload
and the potential availability lost if resources are insufficient to accomplish those tasks.
There is no Benchmark for this metric, but a reasonable target is that the downtime required
for component replacement should not result in more than 2% unavailability. It is important
to note here that since fleets tend to come due for component replacement in “batches”, thus
this metric is highly variable and must be looked at over the long-term … 12-24 months. Just
as with the ETTR for Backlogs, this measure serves as a tool for the Planning process to
enable it to “manage” availability by scheduling work in such a way that the availability goal
can be met.

4.7 Estimated Time To Execute Factory Programs
The total estimated repair downtime hours required to perform all overdue factory
programs, i.e. PIP & PSP’s. The program will typically define the estimated time to execute
each program. This metric is an indication of extent of the program execution workload and
the potential availability lost if resources are insufficient to accomplish those tasks.
There is no Benchmark for this metric but, if one assumes that program execution is
relatively current, a reasonable target is that the downtime required for component
replacement should not result in more than 1% unavailability. It is important to note that
since programs are generated to cover fleets of equipment, those programs tend to come in
“batches”, thus this metric is highly variable and must be looked at over the long-term … 6-
12 months. Just as with the ETTR for Backlogs and component replacement, this measure
serves as a tool for the Planning process to enable it to “manage” availability by scheduling
work in such a way that the availability goal can be met.

5. Parts Management
The following metrics are used to determine how well maintenance activities are supported
by the parts inventory and evaluate the relationship between the Parts, Planning &
Scheduling and Maintenance Departments in their efforts to avoid unnecessary parts-related
downtime.
5.1 Warehouse Service Fill Level (instantaneous)
Service Fill Level (Instantaneous) =
Parts Orders Closed at 1st
Request
Total Parts Orders
X 100 (%)
Instantaneous Service Fill Level is a parts management efficiency indicator that quantifies
the percentage of individual parts requests entered against the on-site parts warehouse for
repairs (including Backlog parts requests) and filled / closed at the first call. A reflection of
the level of satisfaction of on-site parts warehouse performance. The Benchmark for
Instantaneous Service Fill Level is 95%. An aggressive target is > 90%.
5.2 Service Fill Level (24 hours)
Service Fill Level (After 24 Hours) =
Parts Orders Closed in 1st
24 Hours
Total Parts Orders
X 100 (%)
Service Fill Level after 24 hours is a parts management efficiency indicator that quantifies
the percentage of individual parts requests entered against the on-site parts warehouse for
repairs (including Backlog parts requests) and filled/ closed in the first 24 hours after the
first call. A reflection of the level of satisfaction of on-site parts warehouse performance. We
do not have sufficient data to define a Benchmark, but an aggressive target is 100%.
5.3 Unavailability Parts
UnavailabilityPD = (1 − Availability) X
Parts Delay Downtime
Total Downtime
X 100 (%)
Unavailability due to parts delays quantifies the impact of parts delay events on availability.
There is no Benchmark associated with this metric, but a reasonable target is < .5%.
If unavailability (downtime) due to parts delays is significantly higher than .5%, it may
signify potential problems with inventory quality / quality and/or a higher than normal
percentage of unplanned downtime, i.e. the inability of the maintenance organization to
detect problems in advance of failure and plan & schedule the work and associated resources
accordingly.
If parts inventory quality / quality is found to be an issue, it may be due either to the fact that
the maintenance organization is not doing a good job of defining the parts inventory support

requirements to the Parts Department or that the Parts Department is not delivering on its
obligation to support the site with the required parts.
5.4 Emergency Response Time
Emergency Response Time quantifies the average response time (in days) to satisfy parts
requests that cannot be filled instantaneously. This parameter works and should be analyzed
in conjunction with Instantaneous Service Fill Level. There is no Benchmark or target for this
metric.
5.5 Parts Inventory Rotation
Parts Inventory Rotation is defined as the annual turnover of parts held in the on-site parts
warehouse. No Benchmark is available for this parameter. Defining a realistic target for this
metric is highly dependent upon site logistics of the specific operation including
transportation, the capacity and design of the parts warehouse, the remoteness of the site,
costs associated with carrying the inventory and the specific requirements of the site in
terms of any availability guarantees that may be in place.
5.6 Emergency Orders
Emergency Orders quantifies the percentage of parts orders that are placed against the
system on an emergency basis, i.e. “panic mode”. The percentage of Emergency Orders is
another method of analyzing the extent to which the maintenance organization is behaving
pro-actively and control of the fleet. There is no Benchmark or target for this metric.
5.7 Inventory (Items)
This metric quantifies the number of individual line items maintained on- site in the parts
inventory. Because this is proportional to the size of the fleet being supported, there is no
Benchmark or target for this metric. Trending inventory levels over a 6 to 12-month period
and relating the trend to fleet performance results such as MTBS, MTTR and % of scheduled
work, is one way of determining the impact of parts support on the overall site performance.
5.8 Inventory (value)
This metric quantifies the number of individual line items maintained on- site in the parts
inventory. Because this is proportional to the size of the fleet being supported, there is no
Benchmark or target for this metric. Trending inventory levels over a 6 to 12-month period
and relating the trend to fleet performance results such as MTBS, MTTR and % of scheduled
work, is one way of determining the impact of parts support on the overall site performance.

6. Repair Management
The following Repair Management metrics are indicators of the adequacy of the maintenance
personnel, facilities, tooling and support equipment and how well those resources are
organized and managed to perform efficient & effective repairs while contributing to
efficiency, cost and availability objectives.
6.1 MTTR (Shop Service)
MTTRShop =
Total Shop Repair Downtime
Number of Shop Repairs
(Hours)
The average downtime hours (including delays) required to execute shop repairs. There is
no Benchmark or target associated with this measure. Actual results will vary significantly
based upon the nature of the repair, whether it is scheduled or unscheduled and the extent
to which repairs are grouped for optimum efficiency.
6.2 MTTR (Field)
MTTRField =
Total Field Repair Downtime
Number of Field Repairs
(Hours)
The average downtime hours (including delays & response time) required to execute field
repairs. There is no Benchmark associated with this measure, but a reasonable target is <1
hour. It should be noted here that the field service crew is the “first line of defense” for the
maintenance organization and, as such, their attention should be focused on rapid remedial
actions and fast, decisive decision-making. Consequently, if the field service crew determines
that a repair can be better performed in the shop environment, they should release it to the
shop unless those resources are unavailable. Thus, the target for MTTRfield is relatively low,
<1 hour, and unless the machine is not mobile, higher quality and more efficient repairs can
be performed in the shop.
6.3 MTTR (Shop Service / Without Delays)
MTTRShop =
Total Shop Repair Downtime
Number of Shop Repairs
(Hours)
The average downtime hours (excluding delays) required to execute shop repairs. There is
no Benchmark or target associated with this measure. This measure enables site
management to assess the impact of delays on shop repair execution and, if delays are found
to be a problem, take corrective actions accordingly.

6.4 MTTR (Field / Without Delays)
MTTRField =
Total Field Repair Downtime
Number of Field Repairs
(Hours)
The average downtime hours (excluding delays & response time) required to execute field
repairs. There is no Benchmark associated with this measure, but a reasonable target is <.5
hour. This measure enables site management to assess the impact of delays on field repair
execution and, if delays or response time are found to be problems, take the corresponding
corrective actions.
6.5 MTBS After Repairs
MTBS After Repairs =
Total Operating Hours to First Stop
Number of Repairs
(Hours)
The average operating hours to the first stop after each repair is an indication of repair
quality and effectiveness. There is no Benchmark available for this metric. A realistic target
depends upon the level of current performance, i.e. if repair quality is an issue (technicians
are creating more problems than they are correcting), the target should be much higher than
if repair quality is relatively good.
6.6 Percentage Redo (% Redo)
Redo =
∑ Repair Redo ManHours
∑ Repair ManHours
(%)
Percentage Redo is a measure of repair effectiveness. It is the proportion or percentage of
man-hours labor dedicated to redo (correcting repair errors) to the total number of man-
hours labor dedicated to all repairs.
There is no Benchmark for this metric, but a reasonable target is that redo should be < 2%
of total repair man-hours.
6.7 Unavailability Delays
UnavailabilityD = (1 − Availability) X
Delay Downtime
Total Downtime
X 100 (%)
Unavailability due to delay downtime quantifies the impact of delays associated with shop
and field repairs on availability. There is no Benchmark associated with this metric, but a
reasonable target is < 1%. If unavailability (downtime) due to delays is significantly higher
than 1%, it may be related to inadequate manpower, excessive field response times, poor

communications to the field service crew, excessive workload, and/or a higher than normal
amount of unscheduled work.
6.8 Contamination Control (CC)
Contamination Control is a reliability and durability initiative aimed at providing superior
value to Caterpillar’s customers. This superior value can be realized through the avoidance
of component failures and consequent longer component and product lives, as well as
optimized productivity throughout the product’s life cycle.
The “Star” contamination control rating scheme is a valid indication of the organizations
ability to provide and maintain a working environment and work procedures that promote
and support high quality repair execution. The Benchmark is “5 Star”.
To aid compliance with Global CC Guidelines, Regions, Districts, Service Operations Teams,
and Dealers, there needs to be clear governance serving a number of purposes:
• A clear goal of dealers in owning, being accountable and self-responsible for their
Contamination Control culture and compliance performance.
• A Sustainable process for dealers and districts.
• A framework to follow CC guidelines and establish minimum requirements.
Applicable Facilities:
• All dealer branded fixed and mobile facilities are subject to CC requirements.
• Exceptions are to be agreed with the local Caterpillar District Office (District).
The following represent the required and minimum standards to comply to the Global
Contamination Control Guidelines and Governance:
• 3 Star or greater standard at all dealer facilities, with a recommendation that
Component Rebuild Centres (CRC) / Specialisation Areas be rated at 4 or 5 Star.
• Star Ratings for individual dealer facilities may be awarded by any Caterpillar
accreditedCC assessor (dealer or Caterpillar employed).
• All dealer facilities, and customer enrolled facilities, must have minimally an annual
CC audit result recorded in the Caterpillar CC Web Reporting System. The
recommendation isaudits are conducted at least monthly.
• Application of an initial 3, 4, or 5 Star rating to a customer facility should be attended
byan accredited Caterpillar employed assessor.
• Any additional requirements or exceptions are at the District’s discretion.
Below are the assessor requirements to be met to reach CC compliance standards:
• All dealers are to have a minimum of one Caterpillar accredited CC Assessor. More
accredited assessors may be required to maintain a reasonable workload (e.g. one
assessor per branch / store).

2019 Caterpillar Mining Equipment Management Metrics Document V4.pdf

2019 Caterpillar Mining Equipment Management Metrics Document V4.pdf

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