1"use strict";(self.webpackChunkfrontend=self.webpackChunkfrontend||[]).push([[1951],{1951:e=>{e.exports=JSON.parse('[{"case_id":"M001","title":"Predictive maintenance for haul trucks","summary":"Machine learning models predict component failures and optimize maintenance intervals for haul trucks, reducing downtime and costs.","company":"BHP","confidential":"No","location":"Australia","region":"APAC","site_type":"Surface (iron ore)","year":"2023","category_main":"Maintenance","use_case":"Failure Prediction","data_inputs":"Telemetry, CMMS/work orders, sensor data (vibration/temp/pressure)","equipment":"Haul trucks","commodity":"Iron ore","situation":"The operation relied on scheduled maintenance that often triggered downtime even when equipment was still healthy. At the same time, unexpected failures continued to occur because maintenance did not reflect true asset condition. This created both lost production and inefficient use of maintenance resources.","solution":"The company implemented risk-based predictive models trained on historical failure modes and real-time telemetry. These models produced health scores and recommended actions that the maintenance COE monitored through dashboards and KPIs. This shift enabled maintenance to align more closely with measured equipment risk.","results_text":"The approach led to $5.5M in savings at one site by reducing avoidable work and lowering the number of unexpected outages. Maintenance intervals were extended safely, and equipment availability improved through earlier risk detection.","roi_type":"Dollars","roi_value":"$5.5M","vendors_tools":"In-house models; data platform","source_url":"https://www.bestpractice.ai/ai-case-study-best-practice/bhp_saves_%245.5m_by_predicting_mining_truck_maintenance_requirements_with_machine_learning","image_tag":"predictive maintenance, haul trucks","headline_website":"How BHP achieved $5.5M in annual savings through predictive maintenance for haul trucks","subheader_website":"ML models trained on failure modes and telemetry prioritized risk-based maintenance. That cut unplanned failures and safely extended intervals.","image_url":"/assets/cases/M001.jpg","slug":"how-bhp-achieved-55m-in-annual-savings-through-predictive-maintenance-for-haul-trucks"},{"case_id":"M003","title":"Operator fatigue & distraction monitoring (DSS)","summary":"In-cab AI camera monitors eyelids and head pose to detect fatigue and distraction, alerting operators in real time.","company":"Multiple (e.g., PanAust, Central American mine)","confidential":"No","location":"Global","region":"Global","site_type":"Surface & underground mobile equipment","year":"2023","category_main":"Safety","use_case":"Fatigue Monitoring","data_inputs":"In-cab camera, eye-closure metrics, head pose","equipment":"Haul trucks and mobile equipment","commodity":"Multi-commodity","situation":"Fatigue had become a recurring safety risk, with operators showing signs of drowsiness during critical tasks. Traditional monitoring methods were reactive and did not provide adequate early warning. These conditions increased the probability of serious incidents.","solution":"AI-based fatigue detection cameras and predictive analytics were integrated into site workflows to flag risky behavior. The system assesses facial and behavioral indicators and provides escalating alerts to operators and supervisors. Combined with predictive tools, it gives teams earlier visibility of fatigue patterns.","results_text":"The deployment led to a 50% reduction in fatigue-related camera alarms, showing measurable improvement in operator alertness. Sites also reported stronger fatigue management practices supported by continuous data.","roi_type":"Quality","roi_value":"50% reduction","vendors_tools":"Caterpillar MineStar Detect DSS; Fatigue Science Readi","source_url":"https://www.nsc.org/getmedia/7d0ff926-06c1-4570-80f7-6a0f0518d5ec/techhub-readi-case-study.pdf","image_tag":"fatigue monitoring","headline_website":"How leading mining companies reduced fatigue incidents through AI-based operator monitoring","subheader_website":"In-cab cameras track eyelids and head pose to flag fatigue and distraction for timely supervisor action. Sites reported ~50% fewer fatigue alarms.","image_url":"/assets/cases/M003.jpg","slug":"how-leading-mining-companies-reduced-fatigue-incidents-through-ai-based-operator-monitoring"},{"case_id":"M004","title":"Collision avoidance across mine fleet","summary":"GNSS-based CAS provides 360 operator awareness and traffic alerts to reduce vehicle collisions.","company":"Cerrejn (Hexagon Mining)","confidential":"No","location":"Colombia","region":"LATAM","site_type":"Open-pit coal mine","year":"2022","category_main":"Safety","use_case":"Collision Avoidance","data_inputs":"GNSS, vehicle telemetry, proximity sensors","equipment":"Light/Heavy vehicles, haul trucks","commodity":"Coal","situation":"Mobile equipment frequently operated in close proximity, creating constant risk of dangerous interactions. Human awareness alone could not reliably detect all hazards, especially in complex or congested areas. This resulted in near misses and inconsistent compliance with traffic rules.","solution":"A site-wide collision-avoidance system (CAS) was deployed, including both random and preventative monitoring of stop-sign compliance. The system used onboard sensors and location data to warn operators of unsafe interactions. Supervisors gained visibility into site-wide compliance patterns.","results_text":"The operation reported reduced accidents and higher compliance, as well as improved situational awareness for operators. CAS alerts increased adherence to traffic protocols and contributed to safer fleet movement.","roi_type":"Qualitative","roi_value":"Reduced accidents and improved compliance;
1 enhanced situational awareness.","vendors_tools":"Hexagon HxGN MineProtect CAS","source_url":"https://hexagon.com/resources/customer-success-stories/using-data-in-mining-operations-tackle-risks-prevent-accidents","image_tag":"collision avoidance","headline_website":"How Cerrejn (Hexagon Mining) enhanced fleet safety through mine-wide collision avoidance","subheader_website":"GNSS-based CAS provides 360 awareness and event alerts across light and heavy vehicles. Sites saw fewer interactions and better compliance.","image_url":"/assets/cases/M004.jpg","slug":"how-cerrejn-hexagon-mining-enhanced-fleet-safety-through-mine-wide-collision-avoidance"},{"case_id":"M005","title":"Ventilation-on-Demand (VoD) automation","summary":"AI/logic-driven ventilation adjusts airflow based on tracking of people and machines to reduce energy use.","company":"Fosterville Gold Mine / ABB","confidential":"No","location":"Victoria, Australia","region":"APAC","site_type":"Underground gold mine","year":"2025","category_main":"Energy & Environment","use_case":"Ventilation Optimization","data_inputs":"RFID/RTLS for people & equipment, sensors (airflow, gas, temp)","equipment":"Ventilation fans & controls","commodity":"Gold","situation":"Continuous ventilation consumed a large portion of energy costs, especially during periods of low activity. Fixed airflow settings also struggled to maintain good air quality as conditions changed underground. The site required more adaptive airflow control to lower costs while meeting safety standards.","solution":"A Level 4 Ventilation-on-Demand system was implemented with automated, zone-based airflow control triggered by real-time tracking. The system modulates ventilation based on personnel and equipment presence. This allows airflow to be delivered only where and when it is needed.","results_text":"The operation achieved significant energy savings while maintaining or improving air quality. It also became the first Level 4 VoD implementation in Australia, demonstrating advanced ventilation automation in a live mining environment.","roi_type":"Qualitative","roi_value":"Significant energy savings and improved air quality; first Level 4 VoD in Australia.","vendors_tools":"ABB VoD; mine tracking system","source_url":"https://www.ausimm.com/bulletin/bulletin-articles/case-study--ventilation-on-demand-at-fosterville-mine-extract/","image_tag":"ventilation on demand","headline_website":"How Fosterville Gold Mine / ABB cut ventilation energy costs through Level-4 VoD automation","subheader_website":"Tracking and sensors tune airflow by zone as people and machines move. This lowers energy while maintaining safe air quality underground.","image_url":"/assets/cases/M005.jpg","slug":"how-fosterville-gold-mine-abb-cut-ventilation-energy-costs-through-level-4-vod-automation"},{"case_id":"M006","title":"AI froth vision for flotation optimization","summary":"Computer vision measures bubble size, velocity and stability to optimize flotation control and recovery.","company":"Metso (deployments at various concentrators)","confidential":"No","location":"Global","region":"Global","site_type":"Processing plant (flotation)","year":"2022","category_main":"Processing","use_case":"Flotation Optimization","data_inputs":"Froth camera video, process tags (airflow, reagents, levels)","equipment":"Flotation cells","commodity":"Copper, gold, others","situation":"Operators relied on manual interpretation of froth appearance, leading to inconsistent flotation control. Variability in human judgment made it difficult to maintain optimal recovery and grade. These limitations reduced process stability and throughput.","solution":"AI-based vision analytics were integrated with advanced control strategies to interpret froth behavior in real time. The system quantifies key froth parameters and feeds them into automated adjustments. This reduces dependency on subjective manual observation.","results_text":"The site achieved more stable froth conditions, leading to higher recovery and improved grade consistency. Real-time diagnostics also enabled faster responses to changing flotation conditions.","roi_type":"Qualitative","roi_value":"Stabilized froth, higher recovery/grade; real-time diagnostics.","vendors_tools":"Metso FrothSense+","source_url":"https://www.metso.com/portfolio/frothsense/","image_tag":"froth vision","headline_website":"How Metso improved flotation recovery through AI froth-vision optimization","subheader_website":"Computer vision quantifies bubble size, velocity and stability, feeding advanced control. Operators stabilize froth faster and hold targets longer.","image_url":"/assets/cases/M006.jpg","slug":"how-metso-improved-flotation-recovery-through-ai-froth-vision-optimization"},{"case_id":"M007","title":"Predictive monitoring of tailings storage facilities","summary":"AI models flag anomalies and risk in tailings dam behavior using IoT and remote sensing data.","company":"Various (e.g., VROC)","confidential":"No","location":"Global","region":"Global","site_type":"Tailings storage facility","year":"2021","category_main":"ESG & Risk","use_case":"Tailings Monitoring","data_inputs":"Piezometers, inclinometers, weather, InSAR, imagery","equipment":"Sensors & analytics platform","commodity":"Multi-commodity","situation":"Equipment failures often occurred without sufficient warning, exposing the operation to production losses. Traditional inspections provided limited lead time to act. The site required a system that could detect anomalies far earlier.","solution":"Predictive analytics were applied across multi-sensor data streams, including vibration, temperature, and pressure. Dashboards and alarm thresholds were configured to highlight emerging risks. Teams received clear indicators when asset behavior deviated from expected baselines.","results_text":"The system enabled earlier anomaly detection, allowing teams to respond before failure. This improved risk management and helped prevent unplanned downtime.","roi_type":"Qualitative","roi_value":"Earlier anomaly detection; enhanced risk management.","vendors_tools":"VROC platform","source_url":"https://vroc.ai/ai-for-predictive-monitoring-of-tailings-storage-facilities/","image_tag":"tailings monitoring","headline_website":"How leading miners strengthened tailings safety through predictive monitoring","subheader_website":"Multi-sensor data with AI detects behavioral anomalies early. Teams get clearer risk signals and time to intervene.","image_url":"/assets/cases/M007.jpg","slug":"how-leading-miners-strengthened-tailings-safety-through-predictive-monitoring"},{"case_id":"M009","title":"ML-driven mineral exploration targeting","summary":"Machine learning integrates geochem, geophysics & remote sensing to prioritize drill targets for critical minerals.","company":"KoBold Metals","confidential":"No","location":"Global (e.g., Zambia, Quebec)","region":"Global","site_type":"Exploration","year":"2024","category_main":"Exploration","use_case":"Target Generation","data_inputs":"Geochem, geophysics, hyperspectral, EM inversions, maps","equipment":"Exploration IT stack","commodity":"Copper, nickel, cobalt, lithium","situation":"Early-stage exploration involved high uncertainty, with large areas requiring costly drilling. Traditional methods struggled to prioritize targets effectively and often produced low hit rates. The company needed a data-driven way to focus exploration spending.","solution":"ML models were trained on multi-modal datasets - geochemistry, geophysics, geology - to predict prospectivity across the region. The system generated ranked targets and highlighted areas most likely to contain mineralization.","results_text":"The approach accelerated discoveries and improved drilling hit rates compared with baseline methods. While exact financial outcomes were not disclosed, the project demonstrated meaningful value creation by reducing exploration waste and focusing drilling on higher-probability targets.","roi_type":"","roi_value":"","vendors_tools":"Internal ML stack; Coiled; Neptune.ai","source_url":"https://www.wsj.com/tech/ai/kobold-metals-ai-copper-lithium-caad58da","image_tag":"exploration targeting","headline_website":"How KoBold Metals accelerated discoveries through ML-driven exploration targeting","subheader_website":"Models fuse geochem, geophysics and remote sensing to rank prospectivity. Drilling focuses on higher-probability zones, reducing time and cost.","image_url":"/assets/cases/M009.jpg","slug":"how-kobold-metals-accelerated-discoveries-through-ml-driven-exploration-targeting"},{"case_id":"M010","title":"Prospectivity modelling for porphyry Cu","summary":"Combined ML and expert features to map mineral potential in the Chilean Andes.","company":"BHP (with SRK & DeepIQ)","confidential":"No","location":"Chile (northern Andes)","region":"LATAM","site_type":"Exploration","year":"2021","category_main":"Exploration","use_case":"Prospectivity Analysis","data_inputs":"Regional geoscience datasets, structural geology, geochem, geophysics","equipment":"Data science & GIS platforms","commodity":"Copper","situation":"Exploration teams aimed to i
1dentify new porphyry copper targets in a mature and heavily explored terrain. Traditional interpretation had limited ability to reveal subtle or deeply buried signatures. Better target discrimination was required.","solution":"A hybrid workflow combining ML and geoscience modeling was developed to evaluate geological, geophysical, and structural indicators. The models scored and ranked prospective zones for follow-up exploration.","results_text":"The approach produced improved target ranking and more confident area selection, allowing exploration teams to focus on the most promising zones.","roi_type":"Qualitative","roi_value":"Improved target ranking and area selection.","vendors_tools":"SRK; DeepIQ","source_url":"https://www.srk.com/en/projects/machine-learning-in-prospectivity-analysis","image_tag":"prospectivity modelling","headline_website":"How BHP (with SRK / DeepIQ) improved porphyry targeting through hybrid prospectivity modelling","subheader_website":"ML plus expert geological features rank targets across the Andes. Teams narrowed search areas and prioritized drilling intelligently.","image_url":"/assets/cases/M010.jpg","slug":"how-bhp-with-srk-deepiq-improved-porphyry-targeting-through-hybrid-prospectivity-modelling"},{"case_id":"M011","title":"AI-targeted drilling (GoldSpot)","summary":"Data-driven models identify high-priority drill targets, informing drilling and intercepts.","company":"GoldSpot Discoveries (now EarthLabs)","confidential":"No","location":"Canada","region":"","site_type":"Exploration data platform","year":"2020","category_main":"Exploration","use_case":"Target Generation","data_inputs":"Geological maps, geochem, geophysics, remote sensing","equipment":"Exploration data platform","commodity":"Gold","situation":"Drilling campaigns risked wasting resources on low-prospectivity areas due to limited geological visibility. Traditional mapping and early data could not sufficiently narrow down targets. This increased cost and reduced discovery efficiency.","solution":"ML prospectivity models were trained to highlight promising zones based on structural and geochemical patterns. Outputs guided geologists toward areas with higher predicted mineralization.","results_text":"The models led to successful drilling intercepts within predicted zones, showing direct correlation between ML predictions and mineralization results.","roi_type":"Qualitative","roi_value":"Reported successful intercepts within predicted zones.","vendors_tools":"GoldSpot platform","source_url":"https://finance.yahoo.com/news/goldspot-discoveries-ai-targeted-drilling-160700618.html","image_tag":"targeted drilling","headline_website":"How GoldSpot Discoveries (now EarthLabs) focused drilling through AI-targeted target generation","subheader_website":"Data-driven prospectivity models highlighted structures and zones. Drilling intersected predicted mineralization more often.","image_url":"/assets/cases/M011.jpg","slug":"how-goldspot-discoveries-now-earthlabs-focused-drilling-through-ai-targeted-target-generation"},{"case_id":"M012","title":"Autonomous blasthole drilling (Pit Viper)","summary":"Semi- to fully autonomous drilling of blast rows with automatic tramming between holes.","company":"Boliden Aitik / Epiroc","confidential":"No","location":"Sweden","region":"EMEA","site_type":"Open-pit copper mine","year":"2025","category_main":"Drilling & Blasting","use_case":"Autonomous Drilling","data_inputs":"MWD data, positioning, machine telemetry","equipment":"Epiroc Pit Viper drills","commodity":"Copper","situation":"Drilling performance was highly dependent on operator skill, leading to inconsistent hole quality and variable utilization. These inconsistencies created downstream impacts on blasting and production.","solution":"Row-by-row autonomous drilling was introduced, supported by remote oversight. The system standardizes drilling patterns and ensures consistent execution independent of operator variability.","results_text":"The shift resulted in higher utilization and more consistent hole quality. Sites also reported notable safety benefits due to reduced operator exposure.","roi_type":"Qualitative","roi_value":"Higher utilization and consistent hole quality; safety benefits.","vendors_tools":"Epiroc autonomy suite","source_url":"https://www.epiroc.com/en-mk/customer-stories/2020/aitik-automated-bit-by-bit","image_tag":"autonomous drilling","headline_website":"How Boliden Aitik / Epiroc improved drilling productivity through autonomous blasthole drilling","subheader_website":"Row-by-row autonomy standardizes quality and reduces exp
1osure. Sites report higher utilization and consistent holes.","image_url":"/assets/cases/M012.jpg","slug":"how-boliden-aitik-epiroc-improved-drilling-productivity-through-autonomous-blasthole-drilling"},{"case_id":"M013","title":"AI-guided drill bit changer underground","summary":"AI identifies worn bits and performs automatic changes, reducing downtime and improving drill quality.","company":"Sandvik Mining and Rock Solutions","confidential":"No","location":"Finland (test mine)","region":"EMEA","site_type":"Underground development drill","year":"2022","category_main":"Drilling & Blasting","use_case":"Tool Wear Management","data_inputs":"Vision/AI on bit condition, drilling telemetry","equipment":"AutoMine Concept Underground Drill","commodity":"Base metals (underground)","situation":"Frequent bit wear created stoppages and exposed workers to manual handling risks during bit changes. These delays slowed drilling cycles and increased operational hazard.","solution":"An AI-guided automatic drill bit changer was deployed and integrated with iSURE/Deswik for full workflow visibility. The system automates bit changes and reduces hands-on involvement.","results_text":"The operation experienced reduced manual handling, improved safety, and higher drilling productivity due to fewer interruptions.","roi_type":"Qualitative","roi_value":"Reduced manual handling; improved productivity and safety.","vendors_tools":"Sandvik AutoMine; iSURE/Deswik integration","source_url":"https://www.mining.sandvik/en/news-and-media/news-archive/2022/09/sandvik-develops-vision-for-mining-automation-with-automine-concept-underground-drill/","image_tag":"bit changer, underground","headline_website":"How Sandvik Mining and Rock Solutions reduced stoppages through AI-guided drill bit changing","subheader_website":"Vision models spot wear and trigger automatic bit swaps. That removes manual handling risk and keeps drilling on-plan.","image_url":"/assets/cases/M013.jpg","slug":"how-sandvik-mining-and-rock-solutions-reduced-stoppages-through-ai-guided-drill-bit-changing"},{"case_id":"M014","title":"Rock mass classification from MWD data","summary":"ML models classify geotechnical categories from drill MWD signals to optimize blasting and design.","company":"Research/Industry","confidential":"No","location":"Global","region":"Global","site_type":"Drilling operations","year":"2025","category_main":"Drilling & Blasting","use_case":"Rock Characterization","data_inputs":"MWD pressure, torque, RPM, penetration rate","equipment":"Rotary blast hole drill rigs","commodity":"Multiple","situation":"Geotechnical characterization relied on limited sampling and manual interpretation, making it difficult to evaluate rock properties at scale. This created uncertainty in blast design and contributed to overbreak or underbreak.","solution":"ML models were trained on MWD data to predict rock strength and geological units along drill holes. The results provided continuous profiles used to design more accurate blasts.","results_text":"The enhanced visibility enabled better blast design and reduced overbreak and underbreak, improving downstream fragmentation quality.","roi_type":"Qualitative","roi_value":"Better blast design and reduced overbreak/underbreak.","vendors_tools":"ML toolkits; data platform","source_url":"https://www.mdpi.com/2076-3263/15/3/93","image_tag":"MWD analytics","headline_website":"How research teams improved blast design through rock-mass classification from MWD","subheader_website":"Supervised ML turns pressure/torque/RPM into geotech classes. Better inputs cut overbreak/underbreak and downstream losses.","image_url":"/assets/cases/M014.jpg","slug":"how-research-teams-improved-blast-design-through-rock-mass-classification-from-mwd"},{"case_id":"M015","title":"AI-based control for mine ventilation","summary":"Neuro-fuzzy/ML controllers predict gas concentration and optimize airflow distribution underground.","company":"Academic (various)","confidential":"No","location":"Global","region":"Global","site_type":"Underground mine ventilation","year":"2024","category_main":"Energy & Environment","use_case":"Process Control Optimization","data_inputs":"Gas sensors (CH4/CO2), airflow/pressure, equipment tracking","equipment":"Fans, VFDs, air doors","commodity":"Coal & metals","situation":"Static ventilation settings wasted energy because airflow did not adjust to changing underground activity. This approach was costly and s
1ometimes insufficient for emerging safety needs.","solution":"AI controllers were developed to modulate ventilation dynamically based on sensor and location data. The system ensures airflow is delivered where activity is occurring.","results_text":"Sites achieved material energy savings while maintaining stronger safety margins. Ventilation efficiency improved as airflow better matched operational demand.","roi_type":"Qualitative","roi_value":"Energy savings up to significant percentages; improved safety margins.","vendors_tools":"AI controllers (academic)","source_url":"https://www.sciencedirect.com/science/article/abs/pii/S0952197624002069","image_tag":"ventilation control","headline_website":"How academic teams lowered ventilation energy through AI-based process control","subheader_website":"Controllers predict gas levels and pre-emptively adjust fans and doors. Energy drops while safety margins stay intact.","image_url":"/assets/cases/M015.jpg","slug":"how-academic-teams-lowered-ventilation-energy-through-ai-based-process-control"},{"case_id":"M016","title":"Video-based froth prediction for control","summary":"Deep learning predicts future froth states to proactively adjust setpoints.","company":"Academic/Industry","confidential":"No","location":"Global","region":"Global","site_type":"Processing (flotation)","year":"2024","category_main":"Processing","use_case":"Advanced Control","data_inputs":"Froth video sequences; process tags","equipment":"Flotation cells","commodity":"Phosphate/metals","situation":"Delayed froth response made it difficult for operators to adjust the flotation circuit in time to prevent instability. These lags reduced recovery and required constant manual attention.","solution":"A spatiotemporal deep-learning model was developed to forecast froth motion and stability. Predictions were fed into control actions to pre-empt fluctuations in the circuit.","results_text":"The approach produced more stable flotation performance and improved recovery by anticipating froth behavior before it became unstable.","roi_type":"Qualitative","roi_value":"More stable operation and improved recovery.","vendors_tools":"DL models","source_url":"https://www.sciencedirect.com/science/article/abs/pii/S026322412400321X","image_tag":"froth prediction","headline_website":"How academic/industry teams stabilized flotation through video-based froth prediction","subheader_website":"Deep learning forecasts froth states seconds ahead, letting APC tune setpoints earlier. Operations become steadier with higher recovery.","image_url":"/assets/cases/M016.jpg","slug":"how-academicindustry-teams-stabilized-flotation-through-video-based-froth-prediction"},{"case_id":"M017","title":"Plant-wide optimization with intelligent instruments","summary":"AI-enabled sensors (Sense series) provide real-time analytics for grinding & flotation to improve stability.","company":"Metso","confidential":"No","location":"Global","region":"Global","site_type":"Concentrator","year":"2022","category_main":"Processing","use_case":"Stabilization & Optimization","data_inputs":"Cameras, acoustic sensors, process signals","equipment":"Grinding mills, flotation","commodity":"Base & precious metals","situation":"Manual adjustments to flotation resulted in variability and lost recovery. Operators struggled to maintain stable performance due to rapidly changing conditions.","solution":"Instrumented vision and analytics were integrated with APC systems to automate the control of key flotation parameters. The solution responds dynamically to visual and process indicators.","results_text":"The plant achieved higher throughput and recovery along with reduced variability. Automated adjustments improved consistency throughout the circuit.","roi_type":"Qualitative","roi_value":"Higher throughput and recovery; reduced variability.","vendors_tools":"Metso Outotec Sense series","source_url":"https://www.metso.com/corporate/media/news/2022/5/intelligent-instruments-for-optimizing-mining-operations-introducing-metso-outotec-sense-series/","image_tag":"intelligent instruments","headline_website":"How Metso increased plant stability through intelligent instruments and analytics","subheader_website":"Sense-series sensors and vision feed APC in grinding and flotation. Less variability means more throughput and recovery.","image_url":"/assets/cases/M017.jpg","slug":"how-metso-increased-plant-stability-through-intelligent-instruments-and-analytics"},{"case_id":"M019","title":"Risk-based predictive maintenance transition","summary":"Shift from scheduled to risk-based predictive maintenance across critical components.","company":"BHP","confidential":"No","location":"Australia","region":"APAC","site_type":"Surface & underground","year":"2023","category_main":"Maintenance","use_case":"Reliability Optimization","data_inputs":"Telemetry, failure modes, work orders","equipment":"Mobile & fixed plant","commodity":"Multi-commodity","situation":"Preventive maintenance was performed too frequently, while critical failures still occurred unexpectedly. This imbalance increased downtime and inefficient use of labor.","solution":"Risk-based monitoring models were introduced to identify failure modes and assess asset health in real time. Maintenance schedules were adjusted dynamically based on risk levels.","results_text":"The system enabled longer PM intervals and fewer unexpected outages, improving equipment availability and reducing unnecessary maintenance work.","roi_type":"Qualitative","roi_value":"Extended PM intervals; fewer outages.","vendors_tools":"In-house; data platform","source_url":"https://www.miningmonthly.com/management/news-articles/1461592/ai-machine-learning-help-bhps-wheels","image_tag":"reliability optimization","headline_website":"How BHP reduced outages through risk-based predictive maintenance","subheader_website":"Models focus attention on high-risk failure modes, extending PM intervals. The result is fewer unexpected stoppages.","image_url":"/assets/cases/M019.jpg","slug":"how-bhp-reduced-outages-through-risk-based-predictive-maintenance"},{"case_id":"M020","title":"Automated loaders and trucks underground","summary":"AutoMine-enabled loader & truck fleet reduces exp
1osure and boosts productivity.","company":"Glencore George Fisher Mine / Sandvik","confidential":"No","location":"Queensland, Australia","region":"APAC","site_type":"Underground base metals mine","year":"2024","category_main":"Operations","use_case":"Autonomous Haulage (Underground)","data_inputs":"Localization, LiDAR, telemetry","equipment":"Toro LH loaders, TH trucks","commodity":"Zinc-lead-silver","situation":"Manual loading and hauling in hazardous stopes exposed workers to significant risk and limited productivity. Operations often paused due to safety restrictions.","solution":"The mine deployed AutoMine tele-remote systems in phases, transitioning toward full automation. The system reduced operator exposure and enabled equipment to operate continuously.","results_text":"Operations reported strong safety improvements, continuous production in previously restricted areas, and ROI comparable to other AutoMine deployments.","roi_type":"Qualitative","roi_value":"Safety improvement; continuous operation; ROI reported in similar deployments.","vendors_tools":"Sandvik AutoMine","source_url":"https://discoveryalert.com.au/news/sandvik-glencore-automation-partnership-2025/","image_tag":"underground autonomy","headline_website":"How Glencore George Fisher Mine boosted productivity through autonomous underground loaders/trucks","subheader_website":"AutoMine phases from tele-remote to full automation across stopes. Continuous operation improves safety and utilization.","image_url":"/assets/cases/M020.jpg","slug":"how-glencore-george-fisher-mine-boosted-productivity-through-autonomous-underground-loaderstrucks"},{"case_id":"M021","title":"Asset health monitoring for haul trucks","summary":"Predictive maintenance app suite monitors engines, hydraulics, and drive systems with anomaly detection.","company":"XMPro (various mines)","confidential":"No","location":"Global","region":"Global","site_type":"Open-pit mining","year":"2024","category_main":"Maintenance","use_case":"Condition Monitoring","data_inputs":"CAN bus, vibration, temperature, pressure","equipment":"Haul trucks","commodity":"Multiple","situation":"The operation faced recurring unplanned maintenance activities that reduced equipment availability and increased operational costs. Failures often went undetected until late in the cycle, causing reactive interventions and unnecessary downtime. A structured way to anticipate issues and prioritize work was required.","solution":"The site introduced composite health scoring models that integrate multiple sensor inputs and equipment histories. These models continuously evaluate asset condition, while alerting systems recommend targeted maintenance actions to address emerging issues before escalation. This created a more proactive and data-driven maintenance workflow.","results_text":"The implementation led to higher equipment availability and lower maintenance costs through earlier detection of degrading conditions. Resources were allocated more efficiently, and fewer interventions were reactive.","roi_type":"Qualitative","roi_value":"Higher availability and lower maintenance costs.","vendors_tools":"XMPro","source_url":"https://xmpro.com/solutions-library/asset-performance-management%2Cmining%2Cpredictive-maintenance%2Cuse-cases/predictive-maintenance-asset-health-monitoring-for-haul-trucks-in-the-mining-industry/","image_tag":"haul truck health","headline_website":"How XMPro (various mines) raised truck availability through asset health monitoring","subheader_website":"Condition indicators and anomaly scores surface engine, hydraulic and drive issues early. Maintenance shifts from reactive to planned.","image_url":"/assets/cases/M021.jpg","slug":"how-xmpro-various-mines-raised-truck-availability-through-asset-health-monitoring"},{"case_id":"M022","title":"AI predictive maintenance for mining fleets","summary":"Modeling critical components & failure modes to anticipate breakdowns and schedule interventions.","company":"Razor Labs (examples at IMARC)","confidential":"No","location":"Global","region":"Global","site_type":"Open-pit fleets","year":"2024","category_main":"Maintenance","use_case":"Failure Prediction","data_inputs":"Telemetry, work orders, environmental","equipment":"Haul trucks, shovels","commodity":"Multiple","situation":"Fleet utilization suffered because unexpected failures frequently removed trucks from service with little warning. These failures disrupted production schedules and increased the cost of maintenance interventions. The mine needed a way to forecast equipment health before breakdowns occurred.","solution":"Machine-learning models were trained on historical failure data and real-time telemetry to estimate remaining useful life and identify components at elevated risk. These models were integrated into dashboards that maintenance teams used to track health trends and plan interventions. The workflow enabled earlier, more informed decision-making.","results_text":"The approach improved fleet utilization by reducing catastrophic failures and enabling planned rather than reactive maintenance. Operating continuity increased because issues were addressed before they escalated.","roi_type":"Qualitative","roi_value":"Improved utilization; fewer catastrophic failures.","vendors_tools":"Razor Labs","source_url":"https://www.razor-labs.com/revolutionizing-mining-operations-with-ai-preidictive-maintenance-imarc2024/","image_tag":"predictive maintenance fleets","headline_website":"How Razor Labs (IMARC examples) improved fleet utilization through predictive maintenance","subheader_website":"RUL and failure-risk models guide interventions on trucks and shovels. Fewer catastrophic failures keep fleets moving.","image_url":"/assets/cases/M022.jpg","slug":"how-razor-labs-imarc-examples-improved-fleet-utilization-through-predictive-maintenance"}
1,{"case_id":"M023","title":"Eye-tracking fatigue monitoring at scale","summary":"Eye-tracking tech detects microsleeps and distraction; integrated with Cat mining trucks.","company":"Seeing Machines / Caterpillar","confidential":"No","location":"Australia & global","region":"Global","site_type":"Mobile equipment","year":"2013","category_main":"Safety","use_case":"Fatigue Monitoring","data_inputs":"Eye/eyelid tracking, head pose","equipment":"Mining trucks & vehicles","commodity":"Multiple","situation":"Fatigue-related incidents were a significant contributor to safety events across the site. Operators often worked long shifts in demanding environments, increasing the likelihood of reduced alertness. The mine needed a reliable way to detect and mitigate fatigue risks in real time.","solution":"Camera-based fatigue detection was deployed alongside site-level analytics to monitor operator behavior and identify signs of fatigue early. Alerts were integrated into existing safety workflows so that supervisors could intervene promptly. Data insights helped refine rosters and work practices.","results_text":"The site achieved a 70\u201390% reduction in fatigue events, representing a substantial improvement in operator safety. Real-time alerts provided early intervention opportunities and supported improved shift planning.","roi_type":"Quality","roi_value":"90% reduction","vendors_tools":"Seeing Machines; Cat DSS","source_url":"https://www.wired.com/story/eye-tracking-mining-system","image_tag":"eye-tracking safety","headline_website":"How Seeing Machines / Caterpillar cut fatigue events by up to 90% through eye-tracking","subheader_website":"Camera analytics detect microsleeps and distraction in real time. Sites reported 7090% reductions in fatigue-related events.","image_url":"/assets/cases/M023.jpg","slug":"how-seeing-machines-caterpillar-cut-fatigue-events-by-up-to-90-through-eye-tracking"},{"case_id":"M026","title":"Automated fragmentation measurement with AI vision (FRAGTrack)","summary":"AI vision quantifies post-blast fragmentation to optimize blasting and downstream crushing performance.","company":"Multiple mines (Orica deployments)","confidential":"No","location":"Global","region":"Global","site_type":"Open pit / Underground drill & blast, crusher","year":"2022","category_main":"Processing","use_case":"Fragmentation Analytics","data_inputs":"High-speed images at shovel/crusher, conveyor; production tags","equipment":"Shovels, crushers, conveyors, cameras","commodity":"Multi-commodity","situation":"Fragmentation assessment was done manually and infrequently, often delayed by sampling constraints. This created gaps in understanding of blast performance and limited the ability to optimize downstream processes. A continuous measurement approach was needed.","solution":"Automated AI-based vision systems were installed at shovels and crushers to capture particle-size distributions truck-by-truck. Data was logged and visualized to support drill-and-blast and processing teams. This provided continuous, high-resolution fragmentation insight.","results_text":"Continuous monitoring improved throughput and energy efficiency by enabling faster adjustment of blasting and crushing parameters. Real-time PSD tracking enhanced decision-making and reduced oversize issues.","roi_type":"Qualitative","roi_value":"Truck-by-truck PSD and continuous tracking improve throughput and energy efficiency.","vendors_tools":"Orica FRAGTrack; FRAGTrack Crusher","source_url":"https://www.orica.com/news-media/2022/orica-expands-its-fragmentation-monitoring-solution-with-fragtrack-crusher","image_tag":"fragmentation, computer vision","headline_website":"How multiple mines (Orica) lifted throughput through automated fragmentation analytics","subheader_website":"FRAGTrack measures PSD at the shovel/crusher to close the loop with blasting. Continuous feedback reduces energy per tonne.","image_url":"/assets/cases/M026.jpg","slug":"how-multiple-mines-orica-lifted-throughput-through-automated-fragmentation-analytics"},{"case_id":"M027","title":"Blast movement modelling with OREPro3D","summary":"3D blast movement modelling predicts ore displacement to reduce dilution and ore loss.","company":"St Barbara (Touquoy)","confidential":"No","location":"Nova Scotia, Canada","region":"","site_type":"Open-pit gold mine","year":"2023","category_main":"Drilling & Blasting","use_case":"Blast Movement Modelling","data_inputs":"Blast design, burden/spacing, timing; pre/post-blast surveys","equipment":"Blasthole drills, survey, OREPro3D software","commodity":"Gold","situation":"Blast movement often caused ore/waste misclassification, contributing to dilution and lower head grades. Traditional approaches lacked the precision to predict how material shifts post-blast. A more accurate representation of blast movement was required.","solution":"3D modeling tools were used to interpret movement vectors and update digging polygons after blasting activities. These updated polygons allowed loading teams to dig according to the actual post-blast distribution, improving spatial accuracy.","results_text":"The mine achieved approximately 7% grade improvement and 6.5% dilution reduction over 12 months across 33 blasts. These outcomes demonstrate the benefit of adjusting digging boundaries based on real blast movement.","roi_type":"Quality","roi_value":"7%","vendors_tools":"Orica OREPro3D","source_url":"https://www.orica.com/ArticleDocuments/2605/Case%20Study_OREPro_Blast%20movement%20modelling%20with%20OREPro_Touquoy%20Mine_%20English_ODS2025.pdf.aspx","image_tag":"blast movement, ore polygons","headline_website":"How St Barbara (Touquoy) increased grade by ~7% through 3D blast-movement modelling","subheader_website":"OREPro3D predicts ore displacement and updates dig lines. Less dilution and better classification improve feed quality.","image_url":"/assets/cases/M027.jpg","slug":"how-st-barbara-touquoy-increased-grade-by-7-through-3d-blast-movement-modelling"},{"case_id":"M028","title":"Reducing dilution at Santa Luz with OREPro3D","summary":"Predictive blast movement modelling to control feed variability and reduce dilution.","company":"SLD
1M (Santa Luz)","confidential":"No","location":"Bahia, Brazil","region":"LATAM","site_type":"Open-pit gold mine","year":"2024","category_main":"Drilling & Blasting","use_case":"Blast Movement Modelling","data_inputs":"Pre/post-blast survey, timing, design parameters","equipment":"OREPro3D software","commodity":"Gold","situation":"A carbonaceous orebody introduced complex feed variability, increasing dilution and making grade control more difficult. Traditional classification methods struggled to differentiate material types. A better way to characterize post-blast material was required.","solution":"Machine-learning models were applied to post-blast data to classify carbonaceous and non-carbonaceous zones more accurately. Updated polygons guided equipment operators in routing material more effectively. This reduced misclassification and improved downstream feed consistency.","results_text":"Classification accuracy improved, leading to more stable plant feed and better dilution control. The operation gained confidence in routing decisions supported by data-driven geological interpretation.","roi_type":"Qualitative","roi_value":"Improved classification confidence and control of dilution.","vendors_tools":"Orica OREPro3D","source_url":"https://www.orica.com/ArticleDocuments/2605/Increasing%20control%20of%20ore%20dilution%20with%20OREPro%203D_%20Case%20Study.pdf.aspx","image_tag":"blast movement, dilution control","headline_website":"How SLDM (Santa Luz) improved dilution control through predictive blast-movement modelling","subheader_website":"Pre/post-blast modelling sharpens polygon decisions. Teams route material with more confidence and less variability.","image_url":"/assets/cases/M028.jpg","slug":"how-sldm-santa-luz-improved-dilution-control-through-predictive-blast-movement-modelling"},{"case_id":"M029","title":"Shovel-based ore sorting to correct blast shift","summary":"Real-time XRF at shovel bucket improves ore/waste routing, reducing energy and water per tonne.","company":"Copper Mountain Mining / MineSense","confidential":"No","location":"British Columbia, Canada","region":"","site_type":"Open-pit copper mine","year":"2021","category_main":"Processing","use_case":"Pre-concentration / Ore Sorting","data_inputs":"XRF readings, fleet dispatch, grade control, shovel telemetry","equipment":"MineSense ShovelSense","commodity":"Copper","situation":"Blast shift and feed variability were causing inconsistent plant performance and increasing energy usage. Bucket-level decisions made without real-time grade sensing often sent the wrong material to the wrong destination. The mine needed a real-time ore routing solution.","solution":"Bucket-mounted sensing systems were deployed to estimate grade during loading. These real-time measurements enabled operators to reroute ore and waste more accurately. The system reduced reliance on static dig plans and made routing decisions more dynamic.","results_text":"The approach lowered energy and water intensity while improving routing accuracy, which stabilized plant performance. Efficiency improvements were seen across both the loading and processing stages.","roi_type":"Qualitative","roi_value":"Lower energy & water intensity; improved ore routing.","vendors_tools":"MineSense ShovelSense","source_url":"https://minesense.com/wp-content/uploads/2024/03/MineSense-Copper-Mountain-Case-Study-2-2.pdf","image_tag":"ore sorting, XRF, shovel","headline_website":"How Copper Mountain Mining reduced energy and water per tonne through shovel-based ore sorting","subheader_website":"XRF in the bucket redirects ore/waste in real time. Better routing lifts head grade and reduces plant load.","image_url":"/assets/cases/M029.jpg","slug":"how-copper-mountain-mining-reduced-energy-and-water-per-tonne-through-shovel-based-ore-sorting"},{"case_id":"M030","title":"Sensor-based ore sorting boosts gold recovery","summary":"AI-enabled sensor sorting removes waste, increases head grade, and reduces processing costs.","company":"Mina Esperanza de Caravel / TOMRA","confidential":"No","location":"Arequipa, Peru","regi
1on":"LATAM","site_type":"Gold operation","year":"2023","category_main":"Processing","use_case":"Ore Sorting / Pre-concentration","data_inputs":"Sensor data (XRT/optical), belt feed, grade control","equipment":"TOMRA sorters","commodity":"Gold","situation":"High waste content and inconsistent ore feed increased processing costs and lowered mill efficiency. Sending unnecessary waste to the mill reduced throughput and consumed additional energy. A method was required to detect and reject waste early in the flow.","solution":"Sensor-based sorting systems were installed to assess material before milling, allowing waste to be rejected upstream. This ensured that higher-grade ore reached the processing plant and reduced unnecessary load on the mill. The approach improved the overall feed profile.","results_text":"The system delivered higher-grade material to the mill and lowered operating costs, consistent with outcomes reported in comparable case studies. Waste rejection before processing provided both operational and financial benefits.","roi_type":"Qualitative","roi_value":"Higher grade to mill and lower costs (per case study).","vendors_tools":"TOMRA Mining","source_url":"https://www.tomra.com/mining/media-center/customer-stories/mina-esperanza-de-caraveli","image_tag":"ore sorting, sensor-based","headline_website":"How Mina Esperanza de Caravel lowered processing cost through sensor-based ore sorting","subheader_website":"Pre-concentration strips waste before milling. That raises grade to mill and trims costs.","image_url":"/assets/cases/M030.jpg","slug":"how-mina-esperanza-de-caravel-lowered-processing-cost-through-sensor-based-ore-sorting"},{"case_id":"M031","title":"Predictive maintenance on rotating equipment","summary":"Online condition monitoring and analytics reduced production losses by $7.8M.","company":"Vale / ACOEM","confidential":"No","location":"Brazil","region":"LATAM","site_type":"Processing plant (rotating equipment)","year":"2023","category_main":"Maintenance","use_case":"Condition Monitoring","data_inputs":"Vibration, temperature, process load","equipment":"Critical rotating equipment (MV-x)","commodity":"Iron ore / nickel","situation":"The operation experienced repeated unplanned downtime due to faults that were not detected early enough. Manual inspections and standard monitoring tools often failed to catch issues before they escalated. These failures disrupted production and increased maintenance costs.","solution":"The site implemented online condition monitoring combined with predictive analytics to continuously analyze equipment behavior. The system used sensor data to identify anomalies and provided early alerts to operators. This proactive approach allowed teams to address issues before failures occurred.","results_text":"The solution helped avoid $7.8M in production loss, as documented in the case study. Earlier warnings reduced severe failures and contributed to more stable and predictable equipment performance.","roi_type":"Dollars","roi_value":"$7.8M","vendors_tools":"ACOEM MV-x","source_url":"https://www.acoem.com/en/case-studies/mining-reliability-how-acoems-mv-x-helped-vale-save-7.8-mus/","image_tag":"predictive maintenance, vibration","headline_website":"How Vale avoided $7.8M in losses through predictive maintenance on rotating equipment","subheader_website":"Always-on vibration and process analytics flag emerging faults. Early fixes prevent breakdowns and production loss.","image_url":"/assets/cases/M031.jpg","slug":"how-vale-avoided-78m-in-losses-through-predictive-maintenance-on-rotating-equipment"},{"case_id":"M032","title":"Autonomous haulage with AHS","summary":"Komatsu AHS trucks operate autonomously with safety and productivity gains at scale.","company":"Multiple operators / Komatsu","confidential":"No","location":"Global (notably Australia, Americas)","region":"Global","site_type":"Open-pit haulage","year":"2024","category_main":"Operations","use_case":"Autonomous Haulage (Surface)","data_inputs":"GNSS, radar/LiDAR, dispatch data","equipment":"Komatsu AHS haul trucks","commodity":"Iron ore, coal, copper","situation":"Human-operated haulage was limited by safety constraints, shift changes, and inconsistent operating patterns. These factors reduced truck utilization and created variability in haul cycle times. More consistent performance was needed to meet production targets.","solution":"An autonomous haulage system was integrated
1with fleet management and central control. The system coordinated truck movement, optimized routing, and removed the need for constant operator input.","results_text":"Autonomous trucks achieved milestones exceeding 100,000 autonomous hours each, demonstrating reliable long-duration operation. The transition improved consistency, reduced variability, and delivered strong safety benefits.","roi_type":"Qualitative","roi_value":"Fleet milestones incl. trucks exceeding 100k autonomous hours each.","vendors_tools":"Komatsu AHS","source_url":"https://www.komatsu.com/en-us/newsroom/2024/komatsu-achieves-major-autonomous-milestones","image_tag":"autonomous trucks, AHS","headline_website":"How multiple operators increased haulage consistency through Komatsu autonomous haulage","subheader_website":"AHS integrates GNSS, radar and dispatch for 24/7 cycles. Fleets accumulate 100k+ autonomous hours with fewer interactions.","image_url":"/assets/cases/M032.jpg","slug":"how-multiple-operators-increased-haulage-consistency-through-komatsu-autonomous-haulage"},{"case_id":"M033","title":"Digital twins to optimize rail & plant operations","summary":"Predictive modelling via digital twins supports optimization and decision-making.","company":"Anglo American / Wabtec","confidential":"No","location":"South Africa (rail); global plants","region":"EMEA","site_type":"Rail & processing","year":"2020","category_main":"Logistics","use_case":"Digital Twin Optimization","data_inputs":"Sensors, asset telemetry, operational data","equipment":"Rail systems; plant equipment","commodity":"Iron ore / coal","situation":"Logistics and plant throughput were constrained by complex interactions across the processing chain. Teams lacked visibility into how operational changes would impact overall system performance. This made optimization difficult.","solution":"Digital twin models were developed to simulate different operating scenarios, allowing engineers to test adjustments before implementing them in the real plant. The models incorporated real-time data and predictive capabilities.","results_text":"The system identified efficiency and throughput improvement opportunities, helping the operation refine bottlenecks and understand impacts before changes were made.","roi_type":"Qualitative","roi_value":"Potential savings and efficiency improvements reported.","vendors_tools":"Wabtec Digital Twin","source_url":"https://mine.nridigital.com/mine_jul20/mining_digital_twin","image_tag":"digital twin, rail logistics","headline_website":"How Anglo American / Wabtec improved rail and plant throughput through digital twins","subheader_website":"Predictive models test scenarios across logistics and processing. Planners choose higher-yield options with lower risk.","image_url":"/assets/cases/M033.jpg","slug":"how-anglo-american-wabtec-improved-rail-and-plant-throughput-through-digital-twins"},{"case_id":"M037","title":"IoT & analytics to reduce crusher downtime","summary":"Azure-based analytics monitor crushed ore bin (COB) sensors to avoid blockages and downtime.","company":"Newcrest (Cadia Valley) / Microsoft & Insight","confidential":"No","location":"NSW, Australia","region":"APAC","site_type":"Underground block cave / processing","year":"2019","category_main":"Processing","use_case":"Condition Monitoring","data_inputs":"COB sensor data, equipment telemetry","equipment":"Crushers, conveyors","commodity":"Gold-copper","situation":"Crushed ore bins experienced intermittent sensor failures that were difficult to detect early. These failures sometimes caused major delays and unplanned downtime. The operation needed a more reliable way to forecast sensor issues.","solution":"Cloud-based IoT monitoring and analytics were used to evaluate sensor health continuously. The system flagged degrading performance trends and provided alerts ahead of critical failure.","results_text":"The solution prevented thousands of minutes of downtime, as documented in the case study. Earlier detection allowed maintenance teams to intervene before sensors failed completely.","roi_type":"Qualitative","roi_value":"Addressed thousands of minutes of downtime per case study.","vendors_tools":"Microsoft Azure IoT; Insight","source_url":"https://news.microsoft.com/en-au/features/newcrest-minings-ai-and-iot-transformation-3-month-roi/","image_tag":"iot crusher analytics","headline_website":"How Newcrest (Cadia Valley) cut crusher downtime through IoT analytics","subheader_website":"Cloud models monitor COB sensors to catch failures early. Thousands of minutes of downtime were addressed.","image_url":"/assets/cases/M037.jpg","slug":"how-newcrest-cadia-valley-cut-crusher-downtime-through-iot-analytics"},{"case_id":"M038","title":"Ventilation on Demand reduces energy and improves safety","summary":"Automated ventilation system adjusts airflow by activity, saving energy and improving air quality.","company":"Glencore Kidd Mine","confidential":"No","location":"Ontario, Canada","region":"","site_type":"Underground copper-zinc mine","year":"2017","category_main":"Energy & Environment","use_case":"Ventilation Optimization","data_inputs":"RTLS tracking, gas sensors, airflow/pressure","equipment":"Fans, VOD controls","commodity":"Copper-Zinc","situation":"Ventilation consumed a significant portion of the mine\u2019s energy budget, while safety requirements demanded precise airflow control. Static ventilation settings were inefficient and sometimes inadequate.","solution":"A Ventilation-on-Demand (VoD) system was implemented using tracking data and sensors to regulate airflow dynamically. The system adjusted ventilation based on location and activity levels.","results_text":"The mine achieved notable energy s
1avings and improved air quality. Case studies highlighted enhanced safety while reducing ventilation-related operating costs.","roi_type":"Qualitative","roi_value":"Case study notes significant savings and safety benefits.","vendors_tools":"Maestro Digital Mine (VOD)","source_url":"https://www.ieso.ca/-/media/files/ieso/document-library/iap/process-and-systems/6046-ieso-iap-glencore-casestudy-02.pdf","image_tag":"vod energy safety","headline_website":"How Glencore Kidd Mine saved energy through Ventilation on Demand","subheader_website":"RTLS and gas sensors trigger airflow changes by level and zone. Mines report significant savings and better air quality.","image_url":"/assets/cases/M038.jpg","slug":"how-glencore-kidd-mine-saved-energy-through-ventilation-on-demand"},{"case_id":"M039","title":"Autonomous & remote operations at Gudai-Darri","summary":"Mine designed around autonomous haulage and remote operations for safety and productivity.","company":"Rio Tinto","confidential":"No","location":"Pilbara, Western Australia","region":"APAC","site_type":"Open-pit iron ore mine","year":"2022","category_main":"Operations","use_case":"Autonomous Operations","data_inputs":"Dispatch, sensors, remote ops telemetry","equipment":"Autonomous haul trucks, drills","commodity":"Iron ore","situation":"Running operations on-site required constant personnel presence in hazardous environments and limited the ability to scale central oversight. The mine sought to reduce on-site workload and transition toward remote operation.","solution":"A mine-wide autonomy program was introduced alongside remote operations center (ROC) integration. Key processes and equipment were automated and monitored centrally.","results_text":"The initiative led to improved safety and continuous operation, reducing the need for workers to be physically present in high-risk areas. Productivity increased as operations became less dependent on shift constraints.","roi_type":"Qualitative","roi_value":"Improved safety and continuous operations (context case).","vendors_tools":"Multiple OEMs; ROC stack","source_url":"https://www.rocketdna.com/case-studies/xbot-deployment-to-rio-tinto-gudai-darri-mine-site","image_tag":"autonomous operations","headline_website":"How Rio Tinto scaled safe productivity through autonomous & remote operations at Gudai-Darri","subheader_website":"Mine design around autonomy plus ROC integration reduces onsite exposure. Operations run more consistently around the clock.","image_url":"/assets/cases/M039.jpg","slug":"how-rio-tinto-scaled-safe-productivity-through-autonomous-remote-operations-at-gudai-darri"},{"case_id":"M040","title":"AI in crushing & grinding operations","summary":"AI-driven control improves precision and performance in comminution.","company":"Industry overview","confidential":"No","location":"Global","region":"Global","site_type":"Comminution circuits","year":"2025","category_main":"Processing","use_case":"Advanced Process Control","data_inputs":"Power draw, pressure, flow, size distribution","equipment":"Crushers, SAG/Ball mills","commodity":"Multiple","situation":"Manual setpoint tuning in processing plants led to variability in throughput and inefficient energy use. Operators struggled to adjust setpoints quickly enough to maintain optimal performance.","solution":"AI and ML models were deployed to optimize and stabilize process setpoints in real time. The system analyzed plant behavior and recommended or automated adjustments to maintain ideal operating conditions.","results_text":"The site achieved higher throughput and lower specific energy consumption, driven by more stable and consistent setpoint control. Operators reported smoother operation across key circuits.","roi_type":"Qualitative","roi_value":"Higher throughput and lower specific energy.","vendors_tools":"APC with ML","source_url":"https://www.mining-technology.com/features/ai-crushing-and-grinding/","image_tag":"ai comminution control","headline_website":"How industry operators increased comminution throughput through AI-enhanced APC","subheader_website":"Models optimize mill and crusher setpoints in real time. Variability falls and specific energy improves.","image_url":"/assets/cases/M040.jpg","slug":"how-industry-operators-increased-comminution-throughput-through-ai-enhanced-apc"},{"case_id":"M041","title":"Predictive analytics to reduce lost production time","summary":"AI models reduce lost production time across multiple sites.","company":"Rio Tinto","confidential":"No","location":"Global","region":"Global","site_type":"Multiple","year":"2024","category_main":"Operations","use_case":"Downtime Analytics","data_inputs":"Operational, maintenance and sensor data","equipment":"Mobile & fixed plant","commodity":"Multiple","situation":"The operation was losing significant production time due to equipment and process events that were not detected early enough. Traditional monitoring approaches could not identify the root causes of downtime quickly or consistently. This created avoidable performance losses across multiple circuits.","solution":"Predictive analytics were deployed at scale to track equipment behavior, process stability, and early indicators of failure. Models analyzed data across fleets and plant systems to surface leading indicators of production loss.","results_text":"According to case data, the approach enabled up to a 94% reduction in lost production time, showing substantial improvement in o
1perational continuity and event prevention.","roi_type":"Quality","roi_value":"94% reduction","vendors_tools":"In-house; analytics platforms","source_url":"https://www.imd.org/entity-profile/rio-tinto-ai-maturity/","image_tag":"predictive analytics uptime","headline_website":"How Rio Tinto reduced lost production time by up to 94% through predictive analytics","subheader_website":"Cross-site models anticipate downtime drivers and trigger prevention. Teams recover hours that previously disappeared.","image_url":"/assets/cases/M041.jpg","slug":"how-rio-tinto-reduced-lost-production-time-by-up-to-94-through-predictive-analytics"},{"case_id":"M045","title":"Automated fragmentation analytics (product overview)","summary":"Computer vision captures and reports fragmentation for blast optimisation and downstream impact.","company":"Orica","confidential":"No","location":"Global","region":"Global","site_type":"Drill & blast / crusher","year":"2022","category_main":"Processing","use_case":"Fragmentation Analytics","data_inputs":"Image streams; PSD analytics","equipment":"FRAGTrack cameras","commodity":"Multiple","situation":"Blasting teams lacked continuous, high-quality fragmentation feedback, making it difficult to adjust designs and achieve consistent downstream performance. Manual sampling was slow and limited in scope.","solution":"An AI-enabled fragmentation measurement tool was installed to provide continuous PSD analysis directly after blasts or at key material handling points. The system enabled faster assessment of blast outcomes.","results_text":"Operators gained quicker insights for continuous improvement, allowing adjustments to drill-and-blast strategies to improve fragmentation quality.","roi_type":"Qualitative","roi_value":"Faster insights for continuous improvement.","vendors_tools":"Orica FRAGTrack","source_url":"https://www.orica.com/products-services/blasting/blastiq/technologies/fragtrack/fragtrack","image_tag":"fragtrack overview","headline_website":"How Orica sped up blast-to-plant improvement through automated fragmentation analytics","subheader_website":"Computer vision measures PSD continuously and links to QA/QC. Faster feedback helps dial in designs.","image_url":"/assets/cases/M045.jpg","slug":"how-orica-sped-up-blast-to-plant-improvement-through-automated-fragmentation-analytics"},{"case_id":"M049","title":"Ventilation on demand with IoT & analytics","summary":"VOD systems use sensors and analytics to deliver air on demand, saving energy and improving safety.","company":"Industry overview","confidential":"No","location":"Global","region":"Global","site_type":"Underground mines","year":"2024","category_main":"Energy & Environment","use_case":"Ventilation Optimization","data_inputs":"Tracking, gas, airflow sensors","equipment":"Fans, controllers, analytics","commodity":"Multiple","situation":"Ventilation systems consumed high levels of energy and needed to maintain strict air-quality requirements. Traditional fixed ventilation patterns were inefficient and did not align with real-time operational activity.","solution":"A demand-based control system was deployed using sensors and analytics to adjust airflow continuously. Airflow was delivered only to areas where activity triggered ventilation needs.","results_text":"The system produced lower energy use and a safer work environment, improving airflow distribution based on actual underground conditions.","roi_type":"Qualitative","roi_value":"Lower energy use; safer work environment.","vendors_tools":"Various (Maestro, ABB, Epiroc)","source_url":"https://mine.nridigital.com/mine_australia_feb24/case-studies-iot-_mining","image_tag":"vod overview","headline_website":"How industry mines cut ventilation energy through demand-based control","subheader_website":"Tracking and sensors match air to activity in real time. Energy falls while air quality standards are maintained.","image_url":"/assets/cases/M049.jpg","slug":"how-industry-mines-cut-ventilation-energy-through-demand-based-control"},{"case_id":"A001","title":"AI-agent for procurement automation","summary":"Demonstrates how AI can streamline the purchasing process from request to order placement, reducing time and improving accuracy.","company":"Aplied.ai","aplied_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Mining Equipment Manufacturer","year":"2025","category_main":"Supply Chain","use_case":"Procurement Automation","data_inputs":"ERP data, supplier catalogues, purchase order history, approval workflows","equipment":"ERP and procurement platforms","commodity":"Spare parts, consumables, industrial
1components","situation":"Procurement teams spend significant time handling routine purchase requests. The manual validation of specifications, price checks, and approval workflows slows down operations.","solution":"An AI agent connects to ERP and supplier databases, interpreting chat or voice requests. It identifies the correct materials, compares suppliers, and automates purchase order creation according to approval rules.","results_text":"Request-to-order time reduced by up to 60 percent. Procurement accuracy increased, freeing staff for strategic sourcing.","roi_type":"Quality","roi_value":"60% reduction","vendors_tools":"Aplied.ai, SAP Integration","source_url":"","image_tag":"procurement automation","headline_website":"How procurement teams could automate purchase requests using an AI-agent","subheader_website":"AI agent interprets requests, validates specs, and creates orders automatically, cutting process time by 60%.","image_url":"/assets/cases/A001.jpg","slug":"how-procurement-teams-could-automate-purchase-requests-using-an-ai-agent"},{"case_id":"A002","title":"AI-driven customer segmentation for growth","summary":"Shows how AI can help identify high-value customer segments and optimize commercial focus.","company":"Aplied.ai","aplied_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Mining OEM","year":"2025","category_main":"Commercial","use_case":"Customer Segmentation","data_inputs":"CRM data, revenue reports, churn data, market signals","equipment":"CRM and ERP systems","commodity":"Equipment, spare parts, services","situation":"Commercial teams rely on static spreadsheets and limited historical data, causing inefficient targeting and missed opportunities.","solution":"AI models analyze customer volume, revenue, churn risk, and product mix to cluster customers. Strategies adjust dynamically per segment.","results_text":"Higher sales efficie
1ncy and margin performance. Teams gained clearer visibility into growth opportunities.","roi_type":"Qualitative","roi_value":"Increased sales efficiency and margin improvement","vendors_tools":"Aplied.ai, Salesforce","source_url":"","image_tag":"customer segmentation","headline_website":"How commercial teams could drive targeted growth using AI-driven customer segmentation","subheader_website":"AI clusters customers by value and behavior, enabling dynamic strategies for each segment.","image_url":"/assets/cases/A002.jpg","slug":"how-commercial-teams-could-drive-targeted-growth-using-ai-driven-customer-segmentation"},{"case_id":"A003","title":"AI-driven dynamic pricing optimization","summary":"Demonstrates how AI can optimize pricing decisions based on demand, margin, and market conditions.","company":"Aplied.ai","aplied_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Industrial Equipment Supplier","year":"2025","category_main":"Commercial","use_case":"Dynamic Pricing","data_inputs":"Sales transactions, market signals, inventory levels, customer segments","equipment":"ERP and pricing tools","commodity":"Equipment, parts, consumables","situation":"Pricing is manual and inconsistent, leading to margin leakage and poor demand balancing.","solution":"AI models analyze sales history, demand patterns, and customer behavior to recommend optimal prices per region or segment. Supports what-if simulations.","results_text":"Margin improvement of 2 to 5 percent and faster pricing cycles.","roi_type":"Quality","roi_value":"2-5% margin improvement","vendors_tools":"Aplied.ai, in-house pricing engine","source_url":"","image_tag":"dynamic pricing","headline_website":"How pricing teams could balance demand and profitability using AI-driven dynamic pricing","subheader_website":"AI recommends optimal prices by segment and region, improving margins 2-5% while balancing demand.","image_url":"/assets/cases/A003.jpg","slug":"how-pricing-teams-could-balance-demand-and-profitability-using-ai-driven-dynamic-pricing"},{"case_id":"A004","title":"CustomGPT for internal knowledge activation","summary":"Shows how AI can unlock internal knowledge through a domain-trained GPT model.","company":"Aplied.ai","aplied_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Global Mining Operator","year":"2025","category_main":"Knowledge Management","use_case":"Knowledge Activation","data_inputs":"Documents, manuals, reports, SharePoint data","equipment":"IT systems, knowledge bases","commodity":"Applies across departments","situation":"Critical knowledge lives in documents and SharePoint, making it slow to find answers.","solution":"A private GPT chatbot trained on internal documents provides instant answers, summaries, and reference extraction.","results_text":"Onboarding time reduced by 40 percent. Knowledge access improved across departments.","roi_type":"Quality","roi_value":"40% reduction","vendors_tools":"Aplied.ai, open-source LLM","source_url":"","image_tag":"knowledge management GPT","headline_website":"How knowledge workers could activate internal expertise using a CustomGPT","subheader_website":"Private GPT trained on company documents provides instant answers and cuts onboarding time 40%.","image_url":"/assets/cases/A004.jpg","slug":"how-knowledge-workers-could-activate-internal-expertise-using-a-customgpt"},{"case_id":"A005","title":"AI opportunity discovery for leadership","summary":"Demonstrates how structured AI discovery helps prioritize scalable opportunities.","company":"Aplied.ai","aplied_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Global Mining Organization","year":"2025","category_main":"Strategy","use_case":"AI Opportunity Discovery","data_inputs":"Workshop insights, KPIs, data assessments","equipment":"Cross-functional tools, data warehouses","commodity":"All business units","situation":"Companies struggle to prioritize AI initiatives without a framework.","solution":"Workshops, stakeholder interviews, and data readiness assessments identify high-impact opportunities and feasibility.","results_text":"Executives gained a clear roadmap with 8 to 10 high-value opportunities.","roi_type":"Qualitative","roi_value":"Clear AI investment roadmap","vendors_tools":"Aplied.ai, consulting frameworks","
1source_url":"","image_tag":"AI strategy leadership","headline_website":"How leadership teams could identify high-impact AI opportunities","subheader_website":"Discovery workshops and data assessments deliver a prioritized AI roadmap with 8-10 opportunities.","image_url":"/assets/cases/A005.jpg","slug":"how-leadership-teams-could-identify-high-impact-ai-opportunities"},{"case_id":"A006","title":"Structured change management for AI adoption","summary":"Shows how AI upskilling and change enablement programs improve adoption.","company":"Aplied.ai","aplied_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Mining OEM","year":"2025","category_main":"HR","use_case":"Change Management","data_inputs":"Employee surveys, usage analytics, training completion","equipment":"Learning platforms, collaboration tools","commodity":"Applies across organization","situation":"AI pilots fail to scale because employees lack training and confidence.","solution":"AI change management programs with role-based learning paths, communication plans, and internal champions.","results_text":"AI maturity increased by 60 percent within a year. Employees use AI tools more proactively.","roi_type":"Quality","roi_value":"60% increase","vendors_tools":"Aplied.ai, LearnUpon, Microsoft 365","source_url":"","image_tag":"change management AI adoption","headline_website":"How HR teams could scale AI adoption through structured change management","subheader_website":"Role-based training and internal champions drive 60% increase in AI maturity within one year.","image_url":"/assets/cases/A006.jpg","slug":"how-hr-teams-could-scale-ai-adoption-through-structured-change-management"},{"case_id":"I001","title":"AI-powered oil diagnostics for gearbox failure prevention","summary":"Explores how AI-based oil diagnostics can predict gearbox failures before they occur.","company":"Mid-tier Integrated Mining Operator","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Integrated Mining Operation","year":"2025","category_main":"Maintenance","use_case":"Predictive Maintenance","data_inputs":"Oil lab data, vibration telemetry, temperature sensors","equipment":"Gearboxes, pumps, conveyors","commodity":"Multi-commodity","situation":"Critical gearboxes and pumps often fail unexpectedly. Manual oil analysis misses early wear patterns because data from labs and field sensors are not connected. Maintenance teams collect data but lack insights, causing reactive intervention.","solution":"AI combines oil analysis and telemetry into a predictive system. Models detect subtle shifts in viscosity, temperature, and vibration weeks before failure. Teams receive clear guidance on which assets to service and when.","results_text":"Mines can reduce unplanned downtime by 15\u201325% and extend component life.","roi_type":"Quality","roi_value":"15-25% reduction","vendors_tools":"Aplied.ai","source_url":"","image_tag":"oil diagnostics predictive maintenance","headline_website":"How maintenance teams could prevent gearbox failures with AI-powered oil diagnostics","subheader_website":"AI combines oil analysis and telemetry to detect wear patterns weeks before failure, reducing downtime 15-25%.","image_url":"/assets/cases/I001.jpg","slug":"how-maintenance-teams-could-prevent-gearbox-failures-with-ai-powered-oil-diagnostics"},{"case_id":"I002","title":"AI pattern recognition for near-miss risk prediction","summary":"Shows how AI can help identify and prevent safety incidents.","company":"Mid-tier Open Pit Gold Operator","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Open pit gold mine","year":"2025","category_main":"Safety","use_case":"Risk Prediction","data_inputs":"Incident logs, weather data, shift records","equipment":"Haul trucks, loaders","commodity":"Gold","situation":"Safety teams track incidents after they happen. Hidden patterns often go unnoticed until accidents occur.","solution":"AI integrates weather, fatigue, and shift data to flag patterns tied to near misses. The system warns supervisors before risk levels increase.","results_text":"Incident frequency drops by 10\u201320%. Supervisors gain proactive visibility.","roi_type":"Quality","roi_value":"10-20% reduction","vendors_tools":"Aplied.ai","source_url":"","image_tag":"safety risk prediction AI","headline_website":"How safety teams could predict near-miss risks using AI pattern recognition","subheader_website":"AI integrates weather, fatigue, and shift data to warn supervisors before risk escalates, cutting incidents 10-20%.","image_url":"/assets/cases/I002.jpg","slug":"how-safety-teams-could-predict-near-miss-risks-using-ai-pattern-recognition"}
1,{"case_id":"I003","title":"AI-driven energy analytics for cost reduction","summary":"Shows how AI reduces energy intensity and fuel waste.","company":"Open Pit Gold and Copper Operator","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Open pit gold/copper mine","year":"2025","category_main":"Energy & Environment","use_case":"Energy Optimization","data_inputs":"Fuel telemetry, dispatch data, power meters","equipment":"Haul trucks, generators, conveyors","commodity":"Gold, Copper","situation":"Fuel and power can account for 30% of operating costs. Inefficient routes and idle time increase waste.","solution":"AI analyzes dispatch and power data to detect inefficiencies and suggest improvements such as route optimization or load balancing.","results_text":"Operators achieve 5\u201310% energy cost reduction and measurable CO\u2082 savings.","roi_type":"Quality","roi_value":"5-10% reduction","vendors_tools":"Aplied.ai","source_url":"","image_tag":"energy optimization AI","headline_website":"How operations teams could cut diesel and power costs using AI-driven energy analytics","subheader_website":"AI detects route and load inefficiencies to deliver 5-10% energy cost reduction and CO\u2082 savings.","image_url":"/assets/cases/I003.jpg","slug":"how-operations-teams-could-cut-diesel-and-power-costs-using-ai-driven-energy-analytics"},{"case_id":"I004","title":"AI fleet optimization for haul cycle efficiency","summary":"Shows how AI optimizes haul routes to reduce energy use.","company":"Mid-tier Open Pit Iron Ore Producer","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Open pit iron ore mine","year":"2025","category_main":"Operations","use_case":"Fleet Optimization","data_inputs":"Dispatch data, GPS telemetry, fuel usage","equipment":"Haul trucks, shovels","commodity":"Iron ore","situation":"Inefficient routes and idle trucks increase fuel consumption.","solution":"AI analyzes cycle times, congestion, and speed patterns to assign optimal routes.","results_text":"Cycle time improves, and fuel use drops by 5\u20138%.","roi_type":"Quality","roi_value":"5-8% reduction","vendors_tools":"Aplied.ai","source_url":"","image_tag":"fleet optimization dispatch","headline_website":"How dispatchers could reduce haul cycle times and fuel burn with AI fleet optimization","subheader_website":"AI assigns optimal routes based on congestion and speed patterns, cutting fuel use by 5-8%.","image_url":"/assets/cases/I004.jpg","slug":"how-dispatchers-could-reduce-haul-cycle-times-and-fuel-burn-with-ai-fleet-optimization"},{"case_id":"I005","title":"Natural language AI for risk forecasting","summary":"Shows how AI extracts risk indicators from documentation.","company":"Global Mining Group","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Corporate / Multi-site","year":"2025","category_main":"Strategy","use_case":"Risk Management","data_inputs":"Financial reports, permitting documents, risk logs","equipment":"Office systems","commodity":"Multi-commodity","situation":"Teams process thousands of pages of text manually, causing delays.","solution":"Natural language models analyze documents to surface risk factors and forecast delays.","results_text":"Review cycles shorten. Risk visibility improves. Decision-making accelerates.","roi_type":"Qualitative","roi_value":"Faster review cycles and improved risk visibility","vendors_tools":"Aplied.ai","source_url":"","image_tag":"NLP risk forecasting","headline_website":"How finance and permitting teams could forecast risks using natural language AI","subheader_website":"NLP models analyze documents to surface risk factors and accelerate decision-making.","image_url":"/assets/cases/I005.jpg","slug":"how-finance-and-permitting-teams-could-forecast-risks-using-natural-language-ai"},{"case_id":"I006","title":"AI simulation for metal recovery optimization","summary":"Explains how AI simulation enhances recovery and plant stability.","company":"Mid-tier Base Metals Producer","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Processing plant","year":"2025","category_main":"Processing","use_case":"Recovery Optimization","data_inputs":"Process historian data, pH data, particle size","equipment":"Mills, flotation cells","commodity":"Copper, Nickel","situation":"Variability in concentrators leads to inconsistent recovery.","solution":"AI simulates outcomes and suggests adjustments for grinding and flotation.","results_text":"Recovery improves by 1\u20133%. Throughput stabilizes.","roi_type":"Quality","roi_value":"1-3% improvement","vendors_tools":"Aplied.ai","source_url":"","image_tag":"concentrator optimization AI","headline_website":"How concentrator teams could increase metal recovery with AI simulation","subheader_website":"AI simulates grinding and flotation adjustments to improve recovery by 1-3% and stabilize throughput.","image_url":"/assets/cases/I006.jpg","slug":"how-concentrator-teams-could-increase-metal-recovery-with-ai-simulation"},{"case_id":"I007","title":"AI pipelines for automated ESG reporting","summary":"Shows how AI integrates ESG data across systems to streamline reporting.","company":"Large Multi-commodity Mining Group","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Corporate / Multi-site","year":"2025","category_main":"ESG","use_case":"ESG Reporting","data_inputs":"Energy data, HR data, emissions records","equipment":"IT systems, environmental sensors","commodity":"Multi-commodity","situation":"ESG data sits in spreadsheets, sensors, and reports. Reporting becomes slow and error-prone.","solution":"AI validates, formats, and consolidates data to auto-generate compliance-ready reports.","results_text":"Reporting time reduces by 40\u201360%. Accuracy improves.","roi_type":"Quality","roi_value":"40-60% reduction","vendors_tools":"Aplied.ai","source_url":"","image_tag":"ESG reporting automation","headline_website":"How mine operators could unify ESG data and automate reporting with AI pipelines","subheader_website":"AI validates and consolidates ESG data to auto-generate reports, cutting time by 40-60%.","image_url":"/assets/cases/I007.jpg","slug":"how-mine-operators-could-unify-esg-data-and-automate-reporting-with-ai-pipelines"},{"case_id":"I008","title":"AI-automated weekly and monthly reporting","summary":"Shows how AI automates weekly and monthly reporting.","company":"Global Multi-site Mining Operator","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Multi-site operation","year":"2025","category_main":"Operations","use_case":"Reporting Automation","data_inputs":"ERP data, production KPIs, maintenance records","equipment":"Reporting systems","commodity":"Multi-commodity","situation":"Supervisors spend hours compiling production, energy, and safety data into spreadsheets.","solution":"AI connects to ERP, fleet, and SCADA systems and generates reports automatically.","results_text":"Leaders save 10\u201315 hours weekly. KPIs become more consistent.","roi_type":"Quality","roi_value":"10-15 hours s
1aved weekly","vendors_tools":"Aplied.ai","source_url":"","image_tag":"automated reporting AI","headline_website":"How mine management could save hundreds of hours by automating weekly reports with AI","subheader_website":"AI auto-generates reports from ERP and SCADA, saving leaders 10-15 hours weekly.","image_url":"/assets/cases/I008.jpg","slug":"how-mine-management-could-save-hundreds-of-hours-by-automating-weekly-reports-with-ai"},{"case_id":"I009","title":"AI-based shift scheduling for productivity","summary":"Explains how AI improves shift plan adherence.","company":"Mid-tier Underground Nickel Mine","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Underground nickel mine","year":"2025","category_main":"Operations","use_case":"Workforce Optimization","data_inputs":"Crew rosters, shift data, equipment telemetry","equipment":"LHDs, trucks, drills","commodity":"Nickel","situation":"Manual scheduling overlooks constraints like travel time and equipment availability.","solution":"AI generates optimal shift rosters and learns from historical data to improve efficiency.","results_text":"Productivity increases by 5\u201310%.","roi_type":"Quality","roi_value":"5-10% improvement","vendors_tools":"Aplied.ai","source_url":"","image_tag":"shift scheduling AI","headline_website":"How production planners could improve shift productivity with AI-based scheduling","subheader_website":"AI generates optimal shift rosters that account for constraints, improving productivity by 5-10%.","image_url":"/assets/cases/I009.jpg","slug":"how-production-planners-could-improve-shift-productivity-with-ai-based-scheduling"},{"case_id":"I010","title":"AI predictive analytics for spare parts demand","summary":"Shows how predictive analytics improves inventory readiness.","company":"Mid-tier Underground Copper-Gold Operator","inspiration_case":true,"confidential":"No","location":"Global","region":"Global","site_type":"Underground copper-gold mine","year":"2025","category_main":"Supply Chain","use_case":"Inventory Optimization","data_inputs":"Consumption history, maintenance logs, supplier lead times","equipment":"Spare parts, components","commodity":"Copper, Gold","situation":"Planners struggle to match spare parts with demand.","solution":"AI forecasts usage, failure rates, and lead times.","results_text":"Excess inventory reduces by 20\u201330%.","roi_type":"Quality","roi_value":"20-30% reduction","vendors_tools":"Aplied.ai","source_url":"","image_tag":"spare parts forecasting AI","headline_website":"How supply chain teams could predict spare parts demand months ahead with AI","subheader_website":"AI forecasts usage and failure rates to reduce excess inventory by 20-30%.","image_url":"/assets/cases/I010.jpg","slug":"how-supply-chain-teams-could-predict-spare-parts-demand-months-ahead-with-ai"},{"case_id":"M050","title":"Autonomous haul trucks at Rio Tinto\'s Pilbara mines","summary":"AI-guided autonomous haul trucks operate consistently and reduce idle time across the pit, delivering significant cost savings and safety improvements.","company":"Rio Tinto","confidential":"No","location":"Pilbara, Western Australia","region":"APAC","site_type":"Surface (iron ore)","year":"2018","category_main":"Production","use_case":"Haul route optimisation & autonomous haulage","data_inputs":"GPS positioning, truck telemetry, pit mapping, production schedules","equipment":"Autonomous haul trucks","commodity":"Iron ore","situation":"The operation struggled with cycle-time variability and rising haulage costs due to pit expansion, driver fatigue, and shift changes causing delays. Manual operations created inconsistent haul cycles and queueing at loading points.","solution":"Rio Tinto deployed Komatsu\'s Autonomous Haulage System (AHS) which smoothed haul cycles, reduced queueing, and improved speed consistency. The system operates from a central operations centre coordinating fleet movements and optimizing routes in real-time.","results_text":"The autonomous fleet achieved a 15% reduction in load-and-haul costs and delivered approximately 700 additional operating hours per truck per year. Importantly, zero AHS-related incidents were recorded, demonstrating significant safety improvements alongside operational gains.","roi_type":"Quality","roi_value":"15% cost reduction","vendors_tools":"Komatsu AHS, central operations centre","source_url":"https://www.amsj.com.au/rio-tintos-autonomous-haul-trucks-achieve-one-billion-tonne-milestone/","image_tag":"autonomous haul trucks, Rio Tinto, Pilbara","headline_website":"How Rio Tinto achieved 15% cost reduction through autonomous haul trucks in the Pilbara","subheader_website":"Komatsu AHS smoothed haul cycles and reduced idle time, delivering 700 additional hours per truck annually with zero safety incidents.","image_url":"https://images.unsplash.com/photo-1622645636770-11fbf0611463","slug":"how-rio-tinto-achieved-15-percent-cost-reduction-through-autonomous-haul-trucks-in-pilbara"},{"case_id":"M051","title":"Autonomous haul trucks at BHP WAIO","summary":"A fully deployed autonomous fleet delivered higher availability and more consistent haul cycles at BHP\'s Western Australian Iron Ore operations.","company":"BHP","confidential":"No","location":"Western Australia","region":"APAC","site_type":"Surface (iron ore)","year":"2022","category_main":"Production","use_case":"Haul route optimisation & fleet automation","data_inputs":"Fleet telemetry, GPS tracking, production targets, pit conditions","equipment":"Autonomous haul trucks","commodity":"Iron ore","situation":"Faced with longer haul distances and rising maintenance requirements, BHP needed to improve fleet utilization and reduce operating costs while maintaining consistent production despite expanding pit geometry.","solution":"BHP expanded autonomous haulage with centralized rout
1ing and dispatch systems. This enabled coordinated truck movement, reduced idle time, and stabilized cycle times through intelligent fleet management and optimized path planning.","results_text":"The autonomous fleet achieved 20% lower haulage costs compared to WAIO average and maintained 89% fleet availability. The system consistently delivered material movement targets while reducing operational variability.","roi_type":"Quality","roi_value":"20% lower costs","vendors_tools":"Autonomous haulage system, fleet management system","source_url":"https://www.bhp.com/-/media/documents/media/reports-and-presentations/2022/221003_waiospeeches.pdf","image_tag":"autonomous trucks, BHP, iron ore","headline_website":"How BHP reduced haulage costs by 20% with autonomous fleet at WAIO operations","subheader_website":"Centralized autonomous dispatch delivered 89% availability and consistent cycle times across expanding iron ore operations.","image_url":"https://images.unsplash.com/photo-1651260414968-7f27e8db866f","slug":"how-bhp-reduced-haulage-costs-by-20-percent-with-autonomous-fleet-at-waio"},{"case_id":"M052","title":"Fortescue\'s autonomous fleet transformation","summary":"Large-scale conversion to autonomous haulage at Fortescue\'s Solomon and Chichester hubs increased throughput and reduced bottlenecks.","company":"Fortescue Metals Group","confidential":"No","location":"Solomon & Chichester Hubs, Western Australia","region":"APAC","site_type":"Surface (iron ore)","year":"2020","category_main":"Production","use_case":"Autonomous haulage & dispatch optimisation","data_inputs":"Truck positioning, shovel status, pit conditions, production schedules","equipment":"Autonomous haul trucks (168 trucks)","commodity":"Iron ore","situation":"Manual operations suffered from inconsistent cycle times and frequent queueing at loading points. Production targets were constrained by operational variability and coordination challenges across multiple sites.","solution":"Fortescue converted 168 trucks to Cat MineStar Command with centralized automation coordinating dispatch, shovel alignment, and pathing to ensure smooth material flow. The system manages fleet coordination across Solomon and Chichester operations.","results_text":"The autonomous fleet delivered more than 30% increase in productivity compared with manual operation. The fleet has successfully moved approximately 1.4 billion tonnes, demonstrating sustained long-term performance improvements.","roi_type":"Quality","roi_value":"30%+ productivity increase","vendors_tools":"Cat MineStar Command, control centre","source_url":"https://www.fortescue.com/articles/fortescue-automation-deployment-reaches-significant-milestone-2020-06-23","image_tag":"autonomous fleet, Fortescue, productivity","headline_website":"How Fortescue achieved 30%+ productivity increase through autonomous fleet transformation","subheader_website":"Cat MineStar Command coordinated 168 autonomous trucks across multiple sites, moving 1.4 billion tonnes with consistent performance.","image_url":"https://images.unsplash.com/photo-1653202293335-92f6ace9943f","slug":"how-fortescue-achieved-30-percent-productivity-increase-through-autonomous-fleet"},{"case_id":"M053","title":"Komatsu FrontRunner AHS at a North American coal mine","summary":"Autonomous routing and dispatch improved utilization and reduced queuing at loading points at a North American coal operation.","company":"Komatsu / Undisclosed Mine","confidential":"No","location":"North America","region":"North America","site_type":"Surface (thermal coal)","year":"2024","category_main":"Production","use_case":"Haul route optimisation & dispatch automation","data_inputs":"Truck telemetry, shovel loading rates, queue lengths, material destinations","equipment":"Autonomous haul trucks","commodity":"Thermal coal","situation":"The mine faced bottlenecks from long queues at shovels and mismatches between truck arrivals and loading capacity, leading to reduced utilization and increased hauling costs. Manual dispatch struggled to balance loading across multiple active areas.","solution":"Implementation of Komatsu\'s FrontRunner AHS integrated
1with Modular Mining\'s DISPATCH optimization tools reduced delays and balanced loading. The system coordinates truck assignments to minimize wait times and maximize shovel utilization.","results_text":"The autonomous system achieved a 20% reduction in haulage costs and 10% increase in material moved per shift. Fleet utilization improved by up to 30% through optimized dispatch and reduced queueing at loading points.","roi_type":"Quality","roi_value":"20% cost reduction","vendors_tools":"Komatsu FrontRunner AHS, Modular Mining DISPATCH","source_url":"https://www.komatsu.com/en-us/newsroom/2021/komatsu-mining-customers-surpass-4-billion-tons-milestone","image_tag":"autonomous dispatch, coal mining, fleet optimization","headline_website":"How a North American coal mine reduced haulage costs by 20% with Komatsu FrontRunner AHS","subheader_website":"Integrated autonomous dispatch balanced loading and reduced queuing, delivering 30% better fleet utilization.","image_url":"https://images.pexels.com/photos/2101137/pexels-photo-2101137.jpeg","slug":"how-north-american-coal-mine-reduced-haulage-costs-by-20-percent-with-komatsu-frontrunner"}]')}}]);
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.