Analis comparativo de Cargadores de Rueda CAT 950H vs Komatsu WA380z-6

MINING EQUIPMENT MANAGEMENT METRICS

Numerical Benchmarks -- Metrics

Metrics

Calculation / Method

What is needed?

How do we use it?

What Will I Have

Benchmark

1

Mechanical Availability

(Equipment Management Focus)

                        Operated Hours

MA = -------------------------------------------- x 100 (%)

        Operated Hrs. + Service Downtime.

Operated Hours;

Service Downtime Hrs.

Collect & enter: Daily;

Analyze: Monthly;

Monitor/Trend: 12 – 24 months

Production Estimating Aid:

- Can production requirements be met today?

- Is the trend in MA as desired?

92% - new fleet

88% - old fleet

2

Utilization

                   Operated Hours

U  =  -------------------------------- x 100 (%)

                  Total Hours in Period

Operated Hours;

Total Hours in Period

Collect & enter: Daily;

Analyze: Monthly;

Monitor/Trend: 12 – 24 months

Asset Utilization:

- Fleet burden indicator;

- Operation performance indicator.

90%

3

Mean Time Between Stoppages (MTBS)

                        Operated Hours

MTBS  =  --------------------------------

                     Number of Stoppages

Operated Hours;

Stoppage Count

Collect & enter: Daily;

Analyze: Monthly;

Monitor/Trend: 12  months

Machine Reliability & Equipment Management Effectiveness;

Impact of MTBS on Mechanical Availability.

80 hrs. – new fleet

60 hrs. – old fleet

4

Mean Time To Repair

(MTTR)

                       Service Downtime

MTTR = -----------------------------------

                        No. of Stoppages

Service Downtime;

Stoppage Count

Collect & enter: Daily;

Analyze: Monthly;

Trend: 12  months

Service Turnaround/Efficiency;

Machine Serviceability;

Impact of MTTR on Mechanical Availability

3 – 6 hours

5

Maintenance Ratio

(MR)

               Service Man-hours

MR =  ------------------------------

                 Operated Hours

Service Man-hours;

Operated Hours.

Collect & enter: Daily;

Analyze: Monthly;

Trend: Every 5,000 hr increment.

Manpower management tool;

Labor needed;

Service & labor efficiency.

0.2 new fleet

0.3 old fleet

6

Percent of Scheduled Service

(SS)

            Scheduled Service Downtime

SS =  ------------------------------------ x 100%

               Total Service Downtime

Service Downtime with scheduled / unscheduled identification.

Collect & enter: With every WO;

Analyze: Monthly;

Trend: 12  months

Service system effectiveness:

- Who is in control? Maintenance department or the machines?

- Is the system proactive or reactive?

80%

7

Service Accuracy

(SA)

          No. of Scheduled Services within ± 5%

SA =  ----------------------------------------------- x 100%

                 No. of Scheduled Services

Visual Method:

Scatter Diagram of Nominal Interval & ± 5%Limits

Actual Service Intervals (SMU)

Scheduled Service Intervals (SMU)

Collect & enter: With every WO;

Analyze: Monthly;

Trend: 12  months

Planning / Scheduling efficiency

Are Repair Centers following the schedule?

General planning accomplishment

95%

8

Warehouse Parts Fill Level

(PFL)

         Number of Parts Orders Closed at 1st Request

PFL = -------------------------------------------------------

                   Total Number of Parts Orders

Parts Order Count identified as “Closed at 1st Request” and/or “Backorder”.

Collect & enter: With every PO;

Analyze: Monthly;

Trend: 12  months

Parts Management Efficiency Indicator

> 85%

MINING EQUIPMENT MANAGEMENT METRICS

Compliance Benchmarks -- Service Records & Data

Records & Data

What Data & Records Needed?

What Will I Have

Benchmark

1

Commissioning Report

• Date & SMU of the machine start-up;
• Component identification;
• Machine system performance data;

• Custom product & retrofits identification;

• Reference point of machine & component life;
• Component tracking system input;
• Benchmarks for further system performance tests;

• Input for extra parts, tools, literature order;

Summary Report on File

2

Condition Monitoring

• Operator Shift Report

• Production Data;
• Downtime Data;
• Mechanical Condition Records;
• Operating Condition Report.

All suggested methods used;

Effectiveness evaluated through service history analysis

• Fuel & Oil Consumption Data

• Problem Indicator;
• Application Severity Indicator;
• Component Management Aid.

• Oil Sampling Data

• Contamination Indicator;
• Oil Condition Indicator;
• Mechanical Condition Indicator.

• Mechanical Inspection Report (including operating tests)

• Detailed Condition indicator;
• Test Data.

• Onboard Monitoring System (if available)

• Fault Codes;

• System Integrity Report;
• Operating Parameter Analysis.

• Diagnostic Test Reports

• Data for Statistical Analysis and Trending.

3

Work Orders (WO)

• Statistical Data

• Machine & System Identifier;
• Date / Time (In / Out), SMU;
• Labor Data;
• Parts Usage.

All shop services documented in a unique Work Order.

• Technical Data

• Problem Description;
• Problem Resolution;
• Failure Cause;
• Afterservice Test Data.

4

Pareto Analysis

(Top 10 Problems)

Production Data:

• Downtime Count per machine;
• Downtime Count per component (system);
• Downtime Hours per machine & component.

The ability to analyze & prioritize the problems by component, system, machine, fleet:

• Downtime;
• Repair frequency;
• Parts Cost;
• Labor Cost and Time;
• Total Cost;
• Chronological Repair History.

Problem Management Capability:

• Problem Identification;
• Impact & Severity Quantification.

Pareto Analysis reviewed monthly.

Problem management process in operation.

Work Order Data:

• Machine System Identity;
• Date, SMU;
• Labor Applied;
• Parts Consumed.

5

Backlog Management

Work Order Data:

• Machine System Identity;
• Date, SMU;
• Description;
• Priority (Urgency)
• Estimated Labor Needed;

• Estimated Downtime Required;
• Parts Availability & Delivery Status.

Effective Service Backlog Management Capability:

• Efficient use of labor;
• Efficient use of downtime;
• Effective planning for the shop & production supervisors;
• Capability to match the workload to available resources.