Lesson 1Environmental and community data: dust monitoring (PM10/PM2.5), noise levels, truck traffic counts, complaint logs, water management, and tailings/erosion observationsThis part deals with environmental and community data, covering dust, noise, traffic, complaints, water, and tailings observations, to aid compliance, impact evaluation, and open engagement with local communities and Zambian authorities.
Dust (PM10/PM2.5) and weather 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 part discusses drilling and blasting data essential for managing fragmentation, enhancing blasthole accuracy, optimising powder factor, and safeguarding pit wall stability, connecting field data and models to subsequent loading, hauling, and crushing activities.
Drill pattern design and compliance dataBlasthole depth, deviation, and collar accuracyFragmentation size distribution measurementPowder factor optimisation and cost trackingBlast vibration, flyrock, and wall damage dataLesson 3Cost and financial data: unit mining cost breakdown (labour, fuel, maintenance, blasting, contractor costs), cost per ton mined and per ton processed, and cash flow timingThis part outlines cost and financial data for open pit operations, including unit mining cost breakdowns, cost per ton mined and processed, and cash flow timing, relating operational factors to budgets, forecasts, and project economic assessments.
Cost centres and chart of accounts designUnit cost per ton mined and processedFuel, labour, 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 part describes production accounting and sampling data, including grade control sampling, assay variance, moisture measurement, and head grade versus reconciled grade, to guarantee accurate metal accounting and meet regulatory and audit standards.
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, tyre wear, and haul distance distributionsThis part details haulage and cycle time data to examine fleet routes, queueing, loading and dumping times, fuel use, tyre wear, and haul distance distributions, aiding dispatch optimisation and minimising bottlenecks and operating costs.
Cycle time breakdown and time stampsRoute geometry and haul distance profilesFuel consumption by route and payloadTyre wear, TKPH, and road condition dataQueue time, spot time, and dispatch rulesLesson 6Equipment performance data: availability, utilisation, mean time between failures (MTBF), mean time to repair (MTTR), and productivity by unitThis part concentrates on equipment performance data for trucks, shovels, drills, and support units, covering availability, utilisation, MTBF, MTTR, and unit productivity, to enable reliability assessment, maintenance planning, and performance comparison.
Time categories and delay coding standardsAvailability and utilisation 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 part defines key operational metrics for open pit copper-gold mines, including material movement, production tonnage, strip ratio, grade control, and plant feed reconciliation, ensuring uniform reporting and consistency 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 part explains structuring and analysing safety and workforce data to identify risk patterns, handle fatigue, enhance training effectiveness, and match staffing with production needs while following Zambian regulations and company guidelines.
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
