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Transportation & Logistics

Data Mining for Efficient Mining Operations


The Challenge

A major mining company found several significant inefficiencies in the way it managed housing for its onsite workers. Erratic housing needs and patterns, inaccurate daily occupancy reporting, price differences and varying rules for employees and contractors made this a complex undertaking for the logistics team. The team also struggled with transportation planning. All of these problems were driving up the costs.

The company asked Cognizant for a technology-based solution to address the challenges and drive down the costs.


Our Approach

Cognizant’s Artificial intelligence team worked with the client to develop a proof of concept for a secure data analytics solution that automates basic reporting, manages ad hoc schedule changes, predicts no-shows, and flags non-compliance and reporting anomalies that have cost impact. We built an “optimization engine” that processes current occupancy data and recommends optimum space allocations based on a back-to-back optimization approach. Our team consolidated these features into a digital analytics platform (DAP) and shifted the platform to a cloud environment.

In subsequent stages, we expanded the platform’s capabilities to analyze data on the company’s fleet of trucks as well as a range of plant and equipment productivity metrics. These expansions enable predictive and preventive maintenance of the fleet and help deliver efficiency improvements throughout the organization.

Deliver Significant Cost Savings by Building a DAP

Modern mining is far more automated than it used to be. Cognizant partnered with the client to design and build a cloud-based data analyt­ics platform that not only provides cost-effective solutions to the challenges of managing housing and transportation but also has the potential to serve and optimize the logistics operations of the entire company.

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$20 million

cost savings from optimal utilization of housing space

50%

reduction in no-show costs

50%

reduction in costs due to erroneous records and reporting


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