🎯 Executive Summary
Predicting failures and maintenance needs in transport fleets (trucks, buses, taxis) based on IoT data — preventing roadside breakdowns and cutting maintenance costs.
💰 Estimated ROI: 25–40% lower emergency‑repair costs, 30% less fleet downtime, longer useful vehicle life.
🏭 Target Industries: Logistics, public transport, taxi, mining
⏱ Implementation Time: 10‑week pilot, 5–6 months development
📊 Key KPIs: MTBF, predicted‑failure rate, repair cost per km, fleet availability
📋 Technical Project Details
This system is designed for preventive monitoring of vehicle condition and prediction of failures, where the main challenge is precise monitoring of data from various sensors such as temperature and vibration along with ECU information. By collecting sensor information, the system extracts abnormal change patterns to enable preventive maintenance and avoid serious, costly breakdowns. This approach uses AI‑based prediction models to analyze the data and issue timely alerts, which is vital in the transport and logistics industries. By integrating data, the system can identify complex patterns and increase operational efficiency.
In the development stage, the focus was on integrating diverse sensors so that real‑time information is collected and the AI models can deliver accurate predictions. The system not only monitors the current condition but, by analyzing trends, also estimates the likelihood of future failures — a feature that reduces downtime and improves safety. Data security is ensured through encryption protocols so that sensitive information is protected against unauthorized access. This project has been delivered and its results show reduced maintenance costs and longer equipment life.
Beyond the technical aspects, the system emphasizes usability so that operators can easily view data and manage alerts. Using intelligent dashboards, it produces analytical reports that ease decision‑making. This shift from manual to intelligent monitoring not only increases accuracy but also boosts overall productivity across the supply chain.
Ultimately, the goal of this system is to create a preventive system for fleet management that minimizes risks and optimizes operations. This innovation can be a model for other heavy vehicles and play a key role in reducing operating costs. With a successful rollout, this system is expected to become an industry standard.