Lesson 1Environmental and community data: dust monitoring (PM10/PM2.5), noise levels, truck traffic counts, complaint logs, water management, and tailings/erosion observationsThis section addresses environmental and community data, including dust, noise, traffic, complaints, water, and tailings observations, to support compliance, impact assessment, and transparent engagement with nearby communities and Brazilian authorities.
Dust (PM10/PM2.5) and meteorological dataNoise mapping and blasting impact recordsTruck traffic counts and road safety dataCommunity complaint logs and responsesWater balance, quality, and tailings checksLesson 2Drilling and blasting metrics: fragmentation, blasthole accuracy, powder factor, and pit wall stability indicatorsThis section covers drilling and blasting data needed to control fragmentation, improve blasthole accuracy, optimize powder factor, and protect pit wall stability, linking field measurements and models to downstream loading, hauling, and crushing performance.
Drill pattern design and compliance dataBlasthole depth, deviation, and collar accuracyFragmentation size distribution measurementPowder factor optimization and cost trackingBlast vibration, flyrock, and wall damage dataLesson 3Cost and financial data: unit mining cost breakdown (labor, fuel, maintenance, blasting, contractor costs), cost per ton mined and per ton processed, and cash flow timingThis section details cost and financial data for open pit operations, including unit mining cost breakdowns, cost per ton mined and processed, and cash flow timing, linking operational drivers to budgets, forecasts, and project economic evaluations.
Cost centers and chart of accounts designUnit cost per ton mined and processedFuel, labor, and maintenance cost driversBlasting and contractor cost trackingCash flow timing and variance analysisLesson 4Production accounting and sampling: grade control sampling, assay variance, moisture measurement, and head grade vs reconciled gradeThis section explains production accounting and sampling data, covering grade control sampling, assay variance, moisture measurement, and head grade versus reconciled grade, to ensure reliable metal accounting and support regulatory and audit requirements.
Sampling protocols and QA/QC controlsAssay variance, bias, and precision checksMoisture content and tonnage correctionsHead grade versus reconciled grade gapsMetal accounting balance and reportingLesson 5Haulage and cycle time analysis: fleet routes, empty/load times, fuel consumption, tire wear, and haul distance distributionsThis section details haulage and cycle time data structures to analyze fleet routes, queueing, loading and dumping times, fuel burn, tire wear, and haul distance distributions, supporting dispatch optimization and reduction of bottlenecks and operating costs.
Cycle time breakdown and time stampsRoute geometry and haul distance profilesFuel consumption by route and payloadTire wear, TKPH, and road condition dataQueue time, spot time, and dispatch rulesLesson 6Equipment performance data: availability, utilization, mean time between failures (MTBF), mean time to repair (MTTR), and productivity by unitThis section focuses on equipment performance data for trucks, shovels, drills, and support units, including availability, utilization, MTBF, MTTR, and unit productivity, enabling reliability analysis, maintenance planning, and performance benchmarking.
Time categories and delay coding standardsAvailability and utilization calculationsMTBF and MTTR data collection methodsComponent failure modes and historiesUnit productivity and benchmark dashboardsLesson 7Key operational metrics: material movement, production tonnage, strip ratio, grade control, and plant feed reconciliationThis section defines key operational metrics for open pit copper‑gold mines, including material movement, production tonnage, strip ratio, grade control, and plant feed reconciliation, ensuring consistent reporting and alignment with mine plans and budgets.
Material movement and mining rate trackingOre, waste, and stockpile tonnage dataStrip ratio monitoring and pit phase dataGrade control model versus mined gradesMine-to-mill plant feed reconciliationLesson 8Safety and workforce data: incident reports (TRIF/TRIFR), near misses, absenteeism, overtime, training records, and fatigue indicatorsThis section explains how to structure and analyze safety and workforce data to detect risk patterns, manage fatigue, improve training effectiveness, and align staffing levels with production demands while complying with Brazilian regulations and company standards.
TRIF/TRIFR and severity rate trackingNear miss capture and root cause analysisAbsenteeism, overtime, and shift patternsFatigue risk indicators and mitigation plansSafety training records and competency gaps
