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TRANSPORTATION & LOGISTICS

Data Analytics Optimizes Transportation Equipment Utilization

The Challenge

A global mining company with more than a dozen mines on three continents faced financial hurdles caused by the delays in transporting ore, among other inefficiencies. The massive transportation equipment used by this company and the complex operations involved in the process were difficult to track in real time. To avoid further interruptions and to reduce the financial loss caused by the delay, the company asked Cognizant for help.

Our Approach

We established a Center of Excellence to collaborate with the client’s management team to design and deliver a solution that would gather sensor data on its global installed base of mobile equipment, monitor that equipment’s performance and apply algorithmic analysis to improve the efficiency of its use. Our machine learning solution provides a dashboard for real-time monitoring and benchmarking at various stages of the transportation cycle. The solution captures data on equipment location, movement, load, use, speed and efficiency to ensure optimal use of equipment.

Our cloud-based AI analytics solution also helped mine operators to monitor the throughput and efficiency by viewing the root cause of lower yields on a near real time basis. The project, completed in seven months, included a pilot at three different sites with an initiative to extend the solution globally in its second phase.

Cloud-Based Data Analytics Solution for Increased Efficiency and Capital Cost Savings

Our artificial intelligence solution eliminated the need for manual assignment matching at each of the company’s 15 mines—where one person had to spend three days per site to make a report—saving 24 hours weekly of manual equipment management time at each site. The strategy also helped the company to direct trucks based on necessity and thus reduce downtime. These efforts resulted in 8% increase in annual tonnage moved and in a total capital cost reduction of $30 million annually.

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Increased

annual throughput by 8% at the pilot site

Reduced

annual capital cost by $30 million

Saved

manual equipment management time by 24 hours per site per week

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