Predictive School Bus Fleet Budgeting with Data-Driven Insights

One of the largest obstacles school bus fleet managers must contend with is budgetary restriction. With service expenses and fuel costs eating up most of the budget, it can be difficult to accurately gauge appropriate funds distribution and avoid funds reallocations throughout the year. By collecting and analyzing fleet data, managers can get a better picture of what assets are costing the most in terms of downtime, productivity loss, and total cost of ownership.

Capitalize on Data Capture

From 2015-2020, U.S. fuel prices were fairly steady, staying on average between $2-3, according to the U.S. Energy Information Administration. The past two years have, however, seen fuel prices fluctuating in a way they haven’t since 2008, at one point reaching above an average of $5 per gallon. Such drastic price swings make it that much harder to budget for fuel.

Tracking and monitoring fuel use and related metrics across the fleet can help managers reduce overspend related to fuel misuse/theft, mechanical issues, and driver behavior to more accurately predict future fuel costs associated with each bus.

Fleet solutions like telematics and fleet management software (FMS) provide a real-time view of fuel capacity at time of filling, amount of fuel pumped, and average fuel consumption (including fluctuations in consumption due to a bus’s age or route changes). Using this data, fleets can determine instances of fuel cost overages among assets, as well as the cause, for better insight into the potential fuel costs for the next year.

In addition to fluctuating fuel costs, school bus fleets must also contend with the continuing supply shortages and associated inflation. Not only is it more difficult and costly to acquire new buses, it’s also becoming more difficult and costly to extend the life cycle of current fleet assets. With robust maintenance and repair data for the fleet, managers can hone in on inefficiencies in preventive maintenance (PM) schedules and service workflows (i.e. buses spending too long in the shop waiting on parts, services, or pickup), quickly view PM compliance rates, and source and address causes of repeat repairs to reduce service spend and improve asset lifecycles.

Improve Fleet Utilization

While asset use and fleet utilization may not seem like costing metrics at first glance, they can play a significant role in extending a bus’s useful life and improving asset ROI.

Daily school bus routes can look extremely different from one bus to the next, and these differences can impact mechanical components in several ways and even across different seasons.

In winter, for instance, an urban-based route with lower speed limits and more frequent stops will likely have more engine wear than a bus that runs a longer route across higher speed limits.

Fleets can use route, service, and usage data to get a better idea of which assets are running optimally and which need PM schedule improvements, as well as which buses may need to swap routes. This type of utilization optimization allows fleets to get more productivity out of underused assets while extending the useful life of overused assets, helping to minimize downtime related to breakdowns and unexpected repairs.

Predictive Costings

Historical fleet data is the backbone of predictive costing analysis. While historical data can be pulled from manual documentation, integrated solutions like FMS provide more accurate data accumulation on a larger scale and aggregate the data into easy-to-read, configurable reports. This allows for at-a-glance overviews of key fleet metrics that managers can use to quickly spot issues and investigate potential problems on a more granular level.

Sorting costly problems like fuel misuse, downtime, and repeated repairs makes it easier to control those cost sources and reduce unnecessary spend.

In addition to early issue reduction, historical fleet data paints a picture of past cost trends on which future predictions can be made.

For instance, if a bus uses X gallons of gas per route or specific time period, managers can apply that information with potential fuel cost trends — based on economic and seasonal patterns, as well as market forecasting — to then determine approximate fuel costing per bus per year. Additionally, fleets can use service histories to proactively improve PM schedules to reduce repair spend and more accurately predict future cost trends based on those improvements.

Rachael Plant is a content marketing specialist for Fleetio, a fleet management software company that helps organizations track, analyze and improve their fleet operations.