Challenge
A Fortune 500 mining company, managing a diversified portfolio of metal and mineral products with operations across over 30 countries, set a strategic goal of deploying AI and machine learning solutions to improve asset reliability and minimize production downtime.
Before partnering with C3 AI, the company’s production sites relied on legacy systems to maintain a key production asset: haul trucks. The legacy systems failed to consistently detect truck engine and cooling system failure and provided operators with little to no lead time to respond to alerts. Operators were also flooded with false alerts that resulted in lost maintenance team productivity. Unanticipated truck failures significantly increased unplanned downtime, resulting in millions of dollars in losses every year. The company looked for an AI-enabled solution that could more accurately predict truck failures to reduce unplanned downtime and minimize maintenance costs.
Approach
In 12 weeks, the C3 AI team partnered with experts from the mining company to configure the C3 AI Reliability application to predict engine and cooling system failures for a fleet of 79 trucks. With C3 AI Reliability, the mining company can predict truck failures up to 14 days in advance, allowing maintenance personnel to schedule and conduct repairs during planned downtime. With C3 AI Reliability’s early alerting capability, the company significantly reduced unplanned downtime, helping improve the overall productivity of its mining operations and operating margins.
Solution Architecture
Proven results in weeks, not years
Get insights into C3 AI’s capabilities, enterprise AI best practices, and highest-value use cases.
Gain insights into the C3 AI Platform's capabilities, its model-driven architecture, and test it against your company's sample data set.
Identify a high-impact business problem and collaborate with the C3 AI team to rapidly build an AI application that solves it.
Scale and deploy a tested C3 AI application into production. Incorporate user feedback and optimize algorithms to drive maximum economic value.