A leading global mining, metals and petroleum producer had performance issues across its value chain. Process delays on the production end not only triggered underutilization of production equipment, but also resulted in the company missing its production targets. Moreover, industrial equipment performance was below global benchmarks due to spare parts availability and procurement delays. This resulted in millions of related downtime incidents.
The company realized it needed a better way to track mobile equipment performance, specifically the haul trucks that consume the bulk of its operational expenses. It also wanted to build in the capability to benchmark performances across and within operations to identify both good and bad practices.
Cognizant recommended a comprehensive predictive analytics solution with real-time dashboards and alerts to capture equipment status as well as enable visibility into haul truck availability and truck cycle efficiency. This approach also captures parts supply data for inventory and procurement purposes. Our analytics solution includes R engine for advanced analytics, spotfire dashboard for superior visualization and anytime-anywhere secure access to near-real-time global information through AWS cloud.
The new analytics application enables the mine operator to monitor throughput, efficiency and tonnage, as well as scheduled and unscheduled delays, equipment downtime and overall equipment effectiveness (OEE). It also tracks the root cause of downtime on a near-real-time basis. In addition, the support team is able to predict breaches of service-level agreements (SLAs), which not only improves resolution efficiency by implementing proactive solutions and eliminating the root causes of problems that usually demand longer resolution times but also ensures higher operational efficiency.
increase in annual tonnage
target of parts availability on time in full (OTIF) achieved
decrease in mean time to resolve (MTTR) SLA breaches
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