Demand for freight transportation is projected to nearly double by 2035-from 19.3 billion tons in 2007 to 37.2 billion tons in 2035 (1). Additional capacity comes at a very high price, both financially and environmentally. This will put tremendous pressure on transportation providers to be more efficient in their operations while controlling costs as they scale.

For the discussion we have categorized fleet maintenance into three areas named Scheduled Maintenance, Unscheduled Maintenance and Predictive Maintenance. Primary maintenance cost drivers are parts costs, inventory costs, labor costs, warranty costs, facilities costs and supply chain costs associated with scheduling and routing assets for repairs.

Analytics can help determine the optimal scheduled maintenance intervals and repair scopes during each visit based on analysis of historical repair events and their effectiveness.

Analytics helps drive the right balance in this regard. Reduction in field failures saves costs while reducing down time associated with unscheduled maintenance. Proper management of unscheduled maintenance helps improve customer satisfaction.

Predictive maintenance is a highly desirable approach for reducing the amount of unplanned maintenance and extending the life of components.

When condition monitoring capabilities exist, accurate information on health of the part is possible to be obtained in real-time. This helps timely replacement of the part while it is still under warranty.

Analytically identifying chronic failure patterns means that the corporation can run “replacement campaigns” proactively, instead of reacting to field failures, maximizing the useful part life results in direct savings in deferred part-replacement costs.