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Summary – Facing competition and cost pressures, fleet management with off-the-shelf tools encounters rigidity, data silos, manual workflows, and vendor lock-in, undermining agility and driving up budgets. Custom software unifies GPS, engine telemetry, consumption data, IoT inputs, and business rules in real time via an open-source microservices architecture, automatically normalizes data flows, activates predictive maintenance, optimizes routes (TSP), and manages EV charging to maximize availability and reduce consumption by 8–15%.
Solution: deploy a modular, scalable platform to transform fleet management into a profitability driver and sustainable competitive advantage.
In an environment of heightened competition and pressure on operating costs, fleet management has become a strategic concern. Far more than just GPS tracking or a few Excel spreadsheets, it now requires a unified platform capable of continuously aggregating and analyzing diverse data (telemetry, fuel consumption, history, business constraints). Custom software provides this flexibility by integrating modular, scalable components, making fleet management a direct driver of profitability and a sustainable competitive advantage.
Why Off-the-Shelf Solutions Plateau Quickly
Standard tools and spreadsheets quickly reach their limits when faced with the growing complexity of fleets. They fail to consolidate all critical data needed to manage operations in real time.
Functional Limitations and Rigidity
Off-the-shelf solutions often offer preconfigured modules with no room for fine-tuning. The imposed workflows don’t always align with internal processes, leading to costly workarounds and time-consuming manual tasks.
This rigidity results in discrepancies between field reality and generated reports, making data usage imprecise. Users juggle multiple applications, perform numerous imports and exports, and lose efficiency—a clear sign that it’s time to modernize your enterprise software.
Over time, evolving needs remain unmet: adding a new IoT sensor, a vehicle type, or implementing a TSP (Traveling Salesman Problem) algorithm requires awkward workarounds or the involvement of multiple vendors.
Issues with Disparate Data
Collecting data via proprietary APIs or CSV exports generates heterogeneous streams. Fields don’t always use the same units, timestamps vary by source, and consolidation becomes a challenge.
Without automatic normalization or configurable business rules, errors multiply, and the operations team spends more time cleaning datasets than managing the fleet.
As a result, strategic reporting relies on outdated snapshots, slowing decision-making and hindering incident anticipation.
Maintenance and Scalability Hindered
Updates for standard solutions are scheduled centrally, without accounting for each organization’s specific priorities. Any customization may be jeopardized by even the smallest version upgrade.
Dependence on vendor release cycles creates lock-in, impacting the budget and IT roadmap. Teams lose agility in the face of unforeseen business or regulatory changes.
Example: A Swiss SME managed its fleet using Excel and a minimally customizable standard tool. It quickly found that consolidating IoT sensor feedback and planning routes was prone to errors and delays. This situation demonstrated that the lack of a flexible platform blocked any continuous improvement efforts.
How Custom Solutions Integrate All Critical Data
Custom software unifies GPS tracking, engine telemetry, fuel consumption, and IoT data into a single interface. It adapts to business constraints and key performance indicators.
Consolidation of Telemetry Streams
The heart of a custom system is a data bus capable of receiving and processing real-time streams via a middleware. GPS units, engine sensors, and OBD-II modules converge into a unified layer.
Thanks to a microservices architecture, each data type is ingested, normalized, and stored in a data lake or cloud warehouse, ensuring scalability and security, built on a hexagonal architecture and microservices.
The result is immediate access to dynamic dashboards, where every vehicle movement translates into actionable KPIs for fleet optimization.
Enrichment with History and Business Constraints
Beyond live streams, custom solutions allow cross-referencing vehicle usage history, maintenance schedules, and compliance rules. Data from IoT sensors is contextualized.
For example, integrating battery charging cycles or particulate filter maintenance is handled through dedicated modules, configurable by vehicle model, usage periods, and local regulatory constraints.
This contextualization makes it possible to define business-specific indicators, such as total cost of ownership or asset utilization rate, with a level of granularity unattainable by standard solutions.
Managing EV Fleets and Charging Stations
The rise of electric vehicles requires integrating recharge scheduling and charger availability, sometimes spread across multiple countries or private networks.
A custom module orchestrates charging based on remaining range, distance to be traveled, and station availability windows. It can dynamically reroute to include the minimal detour.
Example: A Swiss public services provider integrated an EV scheduling module into its custom platform. The system demonstrated a 12% reduction in vehicle downtime by optimizing charging slots according to demand peaks.
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What Companies Truly Gain
Implementing a custom solution transforms fuel savings, fleet availability, and employee productivity. It makes the fleet controllable and proactive.
Reducing Fuel Consumption and Unnecessary Trips
By leveraging route optimization algorithms and real-time data, the system identifies inefficient or redundant trips, supported by a precise IT requirements specification.
The correlation between speed, engine RPM, and consumption is analyzed continuously, with alerts for energy-intensive behavior. Drivers receive recommendations for more efficient driving.
This fine-tuned management often reduces consumption by 8% to 15% without heavy investments in the fleet, simply by optimizing usage and routes.
Fewer Breakdowns and Predictive Maintenance
Thanks to wear-pattern analysis from sensors and AI, the software predicts breakdowns before they occur. Alert thresholds adjust based on vehicle age, mileage, and operating environment.
Automated maintenance scheduling and proactive work order generation prevent unplanned downtime. Maintenance costs decrease, as interventions are targeted and carried out at the optimal time.
Example: A Swiss industrial components manufacturer saw unexpected mechanical issues drop by 30% after deploying a predictive maintenance module on its service fleet. The company also recorded a significant drop in emergency repair costs.
Real-Time Visibility and Agile Decision-Making
Decision-makers access a consolidated dashboard combining business KPIs (utilization rate, total cost of ownership) and operational indicators (location, vehicle status, battery charge).
In case of an unforeseen event (traffic, incident), the system proposes dynamic resource reassignment, reprioritizes deliveries, and avoids delays.
Full operational traceability facilitates regulatory compliance and the generation of reports for shareholders or authorities, ensuring transparency and reliability.
Custom Architecture for a Sustainable Competitive Advantage
An open-source-based hybrid and modular ecosystem guarantees scalability and security. It adapts to fleet changes and the specific challenges of each organization.
Real-Time Tracking and Advanced Telemetry
Each vehicle communicates continuously via an IoT connector or OBD-II device. Raw data is received, enriched, and stored in a scalable platform built on open APIs.
Tracking-dedicated microservices provide high performance even during traffic spikes, thanks to an API-first integration. The non-blocking architecture, based on frameworks like Node.js and TypeScript, ensures responsiveness and low latency.
The open-source approach limits vendor lock-in risks and enables the integration of new data sources with controlled development, ensuring an optimized total cost of ownership.
Route Optimization with TSP and Machine Learning
For complex routing, the TSP (Traveling Salesman Problem) algorithm and adaptive machine learning models calculate the optimal sequence of stops based on time, distance, and priority constraints.
Models learn from historical data to anticipate travel-time deviations and propose safety margins. They can be enriched in real time with traffic and weather data.
With this approach, the custom software automatically adjusts schedules, even when new pickup or delivery points arise urgently, while minimizing operational costs.
Dynamic Reallocation, Geofencing, and Compliance
In case of an incident or demand spike, the platform automatically reallocates available vehicles, taking into account driver qualifications and cargo specifics.
Geofencing secures sensitive zones and triggers alerts for unauthorized entry or exit. Compliance rules (driving hours, rest periods) are integrated to mitigate regulatory risks.
The architecture’s modularity enables rapid deployment of new components—security, advanced analytics, or ERP integration—without undermining the existing foundation.
Transform Fleet Management into a Performance Lever
A custom solution unifies and leverages all fleet data to reduce costs, anticipate maintenance, and improve productivity. It combines real-time tracking, predictive maintenance, route optimization, and EV integration into a single contextual system.
By adopting a modular, open-source, and scalable architecture, each company retains control of its roadmap and avoids vendor lock-in. Business processes, internal KPIs, and geographic constraints are accounted for to ensure a lasting competitive advantage.
Our Edana experts support IT and operations leaders in analyzing their challenges, designing, and deploying custom solutions to turn fleet management into a true profitability driver.
