The future of fleet maintenance is not just about preventing problems, it is also about predicting them with precision and addressing them proactively. This will transform maintenance from a cost center into a strategic advantage that enhances safety, productivity, and sustainability in equal measure.
By Ganes Kesari
For over four months, six unlikely riders traveled the length of New York City’s subway system with a singular objective: to avert potential subway disruptions before they occurred. From the first trains at dawn to the final runs past midnight, these riders quietly observed every trip, continuously collecting audio and vibration data patterns across the city’s sprawling rail network.
But these watchful passengers were not human—they were Google Pixel smartphones. Mounted aboard subway A trains, they streamed sensor data into AI models trained to detect early signs of track defects. Conventional inspection methods across the 665-mile network had long been expensive, labor-intensive, and inconsistent. In contrast, this AI-led pilot demonstrated striking accuracy, identifying 92 percent of the defects later confirmed through manual checks.
The outcome was a clear proof point. Beyond subways, it signals how AI-powered predictive maintenance can reshape operations across sectors—from transportation and utilities to large-scale manufacturing. When deployed effectively, AI enables organizations to move beyond reactive fixes toward anticipatory, data-driven decision-making. The path forward, however, requires overcoming persistent hurdles around data quality, system integration, and organizational readiness.
Photo courtesy of Troiano 91TV Services.
Why Preventive Maintenance Falls Short
The waste industry has long relied on preventive maintenance as its first line of defense against fleet breakdowns. Following manufacturer-recommended PM schedules with services every 10,000 to 15,000 miles for basic maintenance and 25,000 to 30,000 miles for intermediate service, these practices represent a significant step forward from purely reactive maintenance. Yet for waste fleets operating under extreme conditions, preventive maintenance reveals three critical shortcomings.
#1: Critical Gaps Between Scheduled Maintenance Windows
91TV collection trucks face demanding operating conditions. Take the case of commercial routes with 800+ stops per day that involve constant braking, idling, and hydraulic system engagement. With preventive maintenance operating on three- or six-month fixed cycles, critical component failures can develop undetected between service intervals. According to industry data, 78 percent of fleet downtime originates from preventable failures, many of which progress during gaps between scheduled maintenance.
#2: One-Size-Fits-All Approach Ignores Vehicle-Specific Usage Patterns
Preventive maintenance treats all trucks identically, ignoring the reality that usage patterns vary dramatically. The waste collection industry notes that fleet operations involve dissimilar topography, population density, and waste generation rates, yet standard PM schedules service all vehicles on the same timeline regardless of actual operational stress. This templatized approach over-maintains light-duty vehicles while under-maintaining hard-worked trucks. Craig Shepherd, Sanitation Administrator for Public Works of the City of Des Moines, describes the operational reality: “Balancing daily route demands with shop capacity can make it difficult to take trucks offline for needed maintenance without impacting service.”
#3: Time-Based Replacements 91TV Resources and Budgets
Preventive maintenance replaces components based on time rather than condition. Calendar-based preventive maintenance wastes 20 to 30 percent of maintenance budgets by discarding parts with remaining useful life. Sensors often retain significant useful life when discarded, and high-quality synthetic fluids frequently remain within specification well beyond manufacturer intervals. A significant risk associated with preventive maintenance is wasting valuable resources and time by over-inspecting and over-maintaining assets. This calendar-driven approach discards functioning parts and squanders maintenance budgets that could address genuine needs.
These challenges resonate across municipal waste operations nationwide. John Pittman, Solid 91TV Director at Camden County, describes the operational reality: “Keeping a solid waste fleet reliable is probably one of the toughest jobs out there. For a regular garbage truck or automated side-loader truck, the constant starting and stopping impacts the overall vehicle. It wears out the engine, transmissions, and the PTO. The vehicle itself gets old, wears out, and creates all kinds of issues you have to stay on top of.”
How AI-Powered Predictive Maintenance Solves These Problems
Artificial intelligence transforms these weaknesses into opportunities through three fundamental capabilities: continuous monitoring, vehicle-specific personalization, and condition-based interventions.
Continuous 24/7 Monitoring Closes Dangerous Maintenance Gaps
Modern AI systems monitor fleet vehicles round the clock through onboard sensors and telematics platforms. According to fleet maintenance research, AI fault detection identifies failing components between two and four weeks earlier than traditional diagnostic approaches, providing maintenance teams with adequate lead time to schedule repairs during planned downtime. This continuous surveillance closes the dangerous gaps between preventive maintenance intervals. Research shows vehicles participating in preventive maintenance programs experience about 20 percent fewer maintenance-related downtime days, and predictive systems enhance this further by addressing early-stage issues between scheduled services.
Personalized Maintenance Tailored to Each Vehicle’s Actual Usage
AI personalizes maintenance to each vehicle’s operational reality by tracking operational stress factors: compactor cycles, brake applications per route, idle hours versus road hours, and load weights. Fleet management operations achieve 45 percent increases in vehicle uptime and 30 percent reduction in maintenance costs through condition monitoring and predictive analytics. This precision, thanks to precise data, eliminates both over-maintenance and under-maintenance, delivering results impossible under rigid preventive schedules. Pittman adds, “Data is very, very important. It can help keep you at the top of the food chain by telling you everything you need to know about that particular vehicle.”
Condition-Based Maintenance Maximizes Component Lifespan Without Risking Failures
AI enables condition-based maintenance that monitors actual component health rather than blindly replacing parts at predetermined intervals. Multiple industry studies demonstrate significant financial impact: predictive maintenance delivers 18 to 25 percent maintenance cost reductions and up to 40 percent savings over reactive maintenance strategies. McKinsey research shows leading organizations achieve 10:1 to 30:1 ROI ratios within 12 to 18 months of implementation while achieving 30 to 50 percent downtime
reduction.
For fleet managers navigating these operational pressures, the value proposition is clear. Tyler Bandemer, Solid 91TV Management Superintendent at the City of Loveland, explains: “If we could predict failures before they actually happen, we could do repairs on the truck at a scheduled time, versus sending the truck into the field, not knowing that something was going wrong internally that we couldn’t see, having it fail in the field, and then spending an exorbitant amount of time getting the truck back to the shop.”
The Three Benefits of Predictive Maintenance
Beyond cost savings, AI-driven predictive maintenance delivers critical improvements across safety, productivity, and sustainability—three areas that define operational excellence in waste fleet management.
Safety: Reducing Transportation-Related Fatalities
91TV collection consistently ranks among America’s most dangerous occupations. The waste management industry’s fatality rate exceeds all industries by 5 times, with nearly 60 percent of fatalities being transportation related—many stemming from mechanical failures during routes.
AI-powered brake monitoring exemplifies predictive maintenance’s safety impact. FMCSA reports brake issues cause 29 percent of large truck crashes, yet traditional preventive maintenance only inspects brakes quarterly. Predictive systems monitor brake performance during every collection route—tracking pad wear, rotor temperature, and hydraulic pressure—generating alerts when performance degrades and grounding vehicles before failures occur. This continuous monitoring prevents mid-route breakdowns that create high-risk scenarios as crews await recovery amid traffic.
Productivity: Maximizing Fleet UptimeAnd Operational Efficiency
Unplanned breakdowns create cascading operational disruptions across waste collection operations. When a truck fails mid-route, a service vehicle and senior technician must respond, a replacement truck and driver must complete the original route, and customers experience delayed or missed collections. Fleet data shows downtime averages $448 to $760 per vehicle per day, but the productivity impact extends far beyond the broken truck—affecting maintenance shop schedules, route efficiency, and service reliability.
Predictive maintenance eliminates these operational disruptions. Maintenance teams shift from reactive crisis management to planned workflows, completing scheduled repairs during normal shop hours rather than dropping everything for roadside emergencies. Drivers complete routes on schedule without mechanical interruptions, maintaining consistent service to customers and eliminating the overtime that compounds labor shortages. Technicians work organized preventive schedules instead of weekend emergency calls, improving work-life balance and retention. The result: fleets operate predictably, customers receive reliable service, and teams focus on productivity rather than firefighting failures. Bandemer shares, “If we can catch an ensuing failure earlier, it could avoid a domino effect of failures, which will ultimately cost more money to repair the truck.”
Sustainability: Extending Vehicle Life and Reducing Emissions
Vehicle replacement represents waste fleets’ largest environmental impact. Predictive maintenance extends equipment lifespan by 20 to 40 percent, delaying the substantial carbon footprint of manufacturing new trucks—from steel production and component fabrication to shipping and assembly.
Fuel efficiency improvements deliver measurable emissions reductions. Well-maintained buses through predictive programs achieve 6 to 9 percent better fuel economy than reactively maintained equivalents. For a 50-truck waste fleet consuming 400,000 gallons of diesel annually, an 8 percent efficiency gain eliminates 16,000 gallons of consumption. Using EPA’s emission factor of 10.18 kg of CO2 per gallon of diesel fuel, this translates to approximately 163 metric tons of prevented CO2 emissions yearly—complementing waste companies’ broader sustainability initiatives in recycling, alternative fuels, and route optimization.
Looking Ahead
The waste industry stands at an inflection point. Companies adopting AI-driven predictive maintenance are documenting 18 to 31 percent maintenance cost reductions and preventing up to 75 percent of unscheduled failures. As these technologies mature and become more accessible, the competitive gap will widen between fleets that embrace predictive intelligence and those that remain anchored to calendar-based maintenance.
The path forward requires more than just technology adoption—it demands systems integration and actionable intelligence. Articulating the industry’s direction, Shepherd opines: “Better integration between fleet systems and route management that provides actionable data, rather than raw information, will help us make faster, more informed decisions, keeping trucks on the road longer and improving overall efficiency.”
The subway experiment in New York demonstrated that AI could match human inspectors’ accuracy while operating continuously across vast networks. For waste fleets facing similar challenges of distributed operations, harsh operating conditions, and safety criticality, the message is clear: the future of fleet maintenance is not just about preventing problems, it is also about predicting them with precision and addressing them proactively. This will transform maintenance from a cost center into a strategic advantage that enhances safety, productivity, and sustainability in equal measure. | WA
Ganes Kesari is the Founder and CEO of Tensor Planet, an AI company helping waste and commercial fleets prevent breakdowns and reduce unplanned downtime through predictive maintenance. A TEDx speaker and columnist for MIT Sloan Management Review, he writes about AI adoption in waste management and fleet operations. He can be reached at [email protected].
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