Know the breakdown before it happens
Sianty reads live telematics, OBD-II codes, and service history for every vehicle in your fleet — and flags the ones about to fail, days before they actually do. It's a predictive maintenance layer built into your existing fleet management software, not a separate app to check.
Anomaly detected — brake pad wear signature on rear axle. Predicted failure window: 6–9 days. Work order suggested.
What is predictive maintenance for fleets?
Predictive maintenance is a data-led approach to vehicle upkeep that uses telematics, OBD-II diagnostic codes, mileage, engine hours, and repair history to forecast when a specific part is likely to fail — so a workshop can schedule the repair before the vehicle breaks down. Instead of servicing every vehicle on a fixed calendar or waiting for a fault to appear, predictive maintenance software like Sianty scores each vehicle's real condition and routes the ones approaching failure straight into a work order.
What it reads
Live sensor and telematics streams, DTC fault codes, odometer and engine-hour data, driver behavior, and past job cards.
What it does
Compares current readings against failure patterns for that make, model, and component to produce a risk score per vehicle.
What you get
An early alert, a predicted failure window, and — inside Sianty — an auto-generated work order routed to your workshop.
The signals that actually predict a breakdown
A single sensor reading rarely tells the full story. Sianty's predictive engine looks at combinations of signals over time — because a brake failure and a battery failure leave very different fingerprints in the data, and treating them the same way produces false alarms instead of useful warnings. The list below isn't exhaustive, but it covers the signals responsible for most unplanned fleet downtime.
Engine temperature
Sustained overheating patterns that precede cooling-system and gasket failures.
Battery voltage drift
Slow voltage decline across start cycles, the earliest sign of a battery nearing end of life.
Brake pad & rotor wear
Braking force, pedal travel, and stopping-distance drift flagged well before pads are metal-on-metal.
Tyre pressure & tread
Pressure loss trends and load patterns that predict blowouts before they happen on the road.
Engine fault codes (DTC)
OBD-II codes read in context of recent history, not as isolated one-off alerts.
Fuel & oil trends
Consumption creep that often points to a developing mechanical inefficiency.
Driver behavior
Harsh braking, idling, and acceleration patterns that accelerate wear beyond the norm.
Service & repair history
Every past job card feeds the model, so recurring issues are weighted more heavily.
From raw signal to scheduled repair, in five steps
Every step below runs automatically once a vehicle is connected — no manual data entry, no separate dashboard to check.
Connect the vehicle
Pair existing telematics hardware or an OBD-II dongle — or rely on mileage and engine-hour data if no device is installed.
Setup · One-timeStream live data
Fault codes, fuel trims, temperature, braking patterns, and idle time flow into Sianty continuously in the background.
ContinuousScore vehicle health
Every vehicle gets a live condition score per component, benchmarked against failure patterns for its make and model.
AutomatedAlert the right people
Fleet managers get a risk alert with a predicted failure window; nothing is buried in a report nobody reads.
InstantAuto-create the work order
A job card is generated in your connected Sianty workshop, technician assigned, parts checked against inventory.
Closed loopWhy "predictive" beats a fixed service calendar
Each maintenance model answers a different question at a different point in time. Reactive maintenance answers "what broke?" Preventive maintenance answers "what's due?" Predictive maintenance is the only one that answers "what's about to fail, and when?" — before the part actually gives out on the road.
Reactive
Fix after it fails- No warning before breakdown
- Highest downtime per incident
- Repairs cost more once a part fails completely
Preventive
Fix on a fixed schedule- Predictable service calendar
- Ignores actual part condition
- Wastes healthy parts, still misses some failures
Predictive — Sianty
Fix exactly when needed- Days of advance warning per part
- Repairs scheduled around your routes
- Auto work order, no manual tracking
UAE fleets run in extreme heat, high mileage, and dense city-to-highway cycles that accelerate wear on brakes, cooling systems, and tyres. A fixed service calendar built for milder climates under-services some vehicles and over-services others. Predictive maintenance adjusts to each vehicle's actual operating conditions — heat load, idle time, route type — so the service interval reflects how the vehicle is actually being driven, not a generic mileage marker. For workshops already using Sianty's garage management system, that same condition data also improves parts forecasting, since recurring predictive alerts across a fleet make it easier to plan inventory around the components that fail most often in local conditions.
What's included in Sianty's predictive maintenance
Live telematics feed
Continuous ingestion of GPS, engine, and sensor data from your existing devices.
Failure-window forecasting
Predicted range for when a component is likely to fail, not just a generic warning.
Per-vehicle health score
A single condition score per vehicle, broken down by component — engine, brakes, battery, tyres.
Risk alerts & routing
Notifications to the fleet manager and workshop the moment a vehicle crosses a risk threshold.
Auto work-order creation
A predictive alert becomes a job card in your connected Sianty workshop automatically.
Historical trend view
Track how each vehicle's condition score has moved over weeks and months, per component.
Parts availability check
Predicted repairs are matched against current inventory before the work order is confirmed.
Fleet-wide dashboard
One screen ranking every vehicle by risk, so the highest-priority repairs surface first.
What predictive maintenance changes on the ground
These figures reflect the typical range fleets see within the first two to three months of switching on predictive maintenance, once enough vehicle data has been collected to sharpen the risk model.
Fewer roadside breakdowns
Lower unplanned repair spend
Average early-warning lead time
Longer average part lifespan
Predictive maintenance across your operation
Logistics & delivery fleets
Delivery windows don't wait for a breakdown. Predictive maintenance flags brake and tyre wear early enough to route the repair around tomorrow's deliveries instead of cancelling today's.
Brake pad wear detected — 7 day window
Battery health declining — schedule this week
Construction & heavy equipment
Heavy machinery failures are expensive and hard to schedule around. Predictive maintenance tracks engine hours, hydraulic pressure, and load stress to catch wear before a site stalls.
Hydraulic pressure drift — inspect coupling
Overheating pattern flagged on hot days
Car rental & leasing
Every hour a vehicle sits in the workshop is an hour it isn't earning rental revenue. Predictive maintenance keeps the fleet turnaround tight without skipping real condition checks.
Tyre tread trending low — check before next rental
AC compressor signature flagged — pre-summer check
Public & school transport
Passenger safety leaves no room for a reactive approach. Predictive maintenance surfaces brake, steering, and tyre risk early, keeping every vehicle within compliance windows automatically.
Steering alignment drift — safety check scheduled
All systems within safe range
Predictive maintenance doesn't stand alone
It's a module inside Sianty Fleet Management Software, which already runs on the same job card, technician, and invoicing engine as our garage management system — so a prediction turns into a completed repair without switching tools. A fleet manager sees the alert; the workshop sees a ready-to-work job card; nobody has to bridge the two manually.
Risk detected
Predictive engine flags a vehicle nearing failure.
Job card created
A work order is generated with the predicted issue attached.
Technician assigned
Routed to the right bay and technician automatically.
Invoice & history logged
Repair closes with billing and service history updated fleet-wide.
Why fleets are moving to predictive maintenance now
Fixed service intervals were built for a different era
Manufacturer service schedules assume average driving conditions — moderate climate, mixed city and highway use, predictable load. Commercial fleets rarely match that profile. A delivery van running twelve hours a day in Gulf summer heat wears its brakes, battery, and cooling system on a completely different curve than the interval printed in the owner's manual. Servicing by the calendar under-protects the vehicles running hardest and over-services the ones running light, and it does both at the same time across a mixed fleet, which means the total maintenance spend ends up higher even though outcomes are worse.
Predictive maintenance replaces that single calendar with a live condition score per vehicle, so the service interval reflects how each vehicle is actually being used — not an assumption made at the factory, and not a number pulled from a spreadsheet that hasn't been updated in years.
Telematics data is already being collected — most of it goes unused
Most fleets already have GPS trackers or OBD-II devices installed for location tracking and fuel reporting. That same hardware is streaming engine, braking, and diagnostic data every second the vehicle runs — data that, in most setups, is discarded after the trip ends. Sianty's predictive maintenance module reads that existing feed and turns it into a maintenance signal, so fleets don't need to install new hardware to start predicting failures; they need to start using the data their vehicles are already producing.
Combined with job card history already logged in Sianty's garage management system, that data becomes a feedback loop: every repair makes the next prediction more accurate for that vehicle, that route, and that driver.
It scales the same way your fleet does
A fleet manager tracking five vehicles and one tracking five hundred face the same underlying problem: knowing which vehicle needs attention before it becomes a phone call from the side of the highway. Predictive maintenance answers that question the same way at either scale, because the model doesn't get harder to read as the fleet grows — it just ranks more vehicles by risk on the same dashboard. That matters for growing operations in particular, where adding vehicles usually means adding blind spots unless the maintenance process scales with them.
Predictive maintenance, answered directly
Predictive maintenance in fleet management is the practice of using live vehicle data — telematics, OBD-II fault codes, mileage, and sensor readings — to forecast when a part is likely to fail, so the repair can be scheduled before a breakdown happens.
Preventive maintenance services a vehicle on a fixed schedule regardless of its actual condition. Predictive maintenance services a vehicle based on its real condition, detected from live data, which avoids both premature servicing and missed failures.
Sianty combines telematics feeds, OBD-II diagnostic trouble codes, odometer and engine-hour readings, historical job card and repair data, driver behavior signals, and manufacturer service intervals to build a condition score for every vehicle in the fleet.
Yes. Fleets using predictive maintenance typically lower unplanned repair costs, reduce roadside breakdowns, extend part life by avoiding early replacement, and cut vehicle downtime because repairs are scheduled instead of reactive.
Sianty connects to most existing telematics devices and OBD-II dongles already installed in a fleet. If a vehicle has no connected device, Sianty can still predict service needs using mileage, engine hours, and repair history.
Yes. When a vehicle crosses a risk threshold, Sianty can automatically generate a job card in the connected workshop, assign a technician, and notify the fleet manager, closing the loop between detection and repair.
The fastest way is to book a live demo, where we walk through your current fleet setup, confirm which vehicles already have compatible telematics, and show the risk dashboard using your own vehicle types.
Accuracy improves as more service history is logged for a given vehicle and component. Early on, Sianty relies on manufacturer failure patterns and fleet-wide data; over time, each repair recorded in the workshop refines the model for that specific vehicle, driver, and route.
Yes. Most fleets run both — a baseline preventive schedule for routine servicing, with predictive alerts layered on top to catch anything developing faster than the fixed schedule accounts for. Sianty shows both on the same vehicle timeline.
No. Even a five-vehicle fleet benefits, since a single unplanned breakdown has an outsized impact on a small operation. The setup cost is the same regardless of fleet size, and Sianty scales the dashboard from a handful of vehicles to several hundred.
What it takes to switch on predictive maintenance
Most fleets are live within a week of the demo call. There's no rip-and-replace of existing hardware and no separate system for the workshop to learn.
Audit what's already connected
We check which vehicles already have telematics or OBD-II devices reporting, and which rely on mileage and engine-hour tracking instead.
Set risk thresholds
Alert sensitivity is tuned per vehicle class, since a school bus and a delivery van should not trigger the same threshold for brake wear.
Connect the workshop
Once thresholds are set, predictive alerts route straight into job cards inside your existing Sianty workshop, ready for a technician to pick up.
See your fleet's risk score before you buy anything
Book a live 20-minute demo. We'll show predictive maintenance running on vehicle types like yours — no setup required to see it work.