Get a Custom Fleet Planning System for Your Business

Many small fleets rely on their TMS, like Rose Rocket, and telematics from Samsara. These platforms are excellent for tracking hours of service and vehicle location, but their planning modules are often basic. They use simple point-to-point Google Maps routing, which cannot handle multi-stop routes with time window constraints or vehicle capacity limits. The routing modules are add-ons, not core features, and their APIs are often restricted, preventing you from feeding in your own business logic.

Consider a dispatcher for a 15-truck local food distributor. Each morning they have 120 stops with different delivery windows and refrigerated vs. dry goods constraints. They export the day's orders to a CSV, manually group stops by zip code in Excel, then drag-and-drop pins in Google Maps to create routes. This process takes 3-4 hours every morning. A last-minute order or a driver calling in sick means restarting the entire manual plan, often resulting in late deliveries and expensive overtime.

The structural issue is that off-the-shelf TMS and telematics platforms are built for data collection and compliance, not complex computation. Their business model is selling subscriptions for tracking and logging, so their architecture isn't designed for the heavy work of solving a Vehicle Routing Problem (VRP). They provide the raw GPS and order data but lack the algorithmic engine to turn that data into an optimal plan that minimizes total mileage across an entire fleet. They cannot merge, re-sequence, and re-assign stops dynamically.

The result is wasted fuel, excessive driver overtime, and an inability to provide customers with accurate ETAs. A route planned manually is often 15-20% less efficient than an optimal one. For a small fleet, that difference is pure profit lost to the inefficiency of tools that were never designed for complex logistical challenges.

The engagement begins with a deep dive into your current operations. Syntora audits your existing TMS, telematics provider, and order management system to map all data inputs. We'd review 3 months of historical order and route data to understand your specific constraints, such as vehicle capacities, driver hours, customer time windows, and traffic patterns. This initial audit produces a clear scoping document detailing the integration points and the specific logic for the model.

The core of the system would be a Python service built with FastAPI, running on AWS Lambda for cost-effective, on-demand processing. For the optimization itself, we'd use Google's OR-Tools library, a powerful open-source solver for vehicle routing problems. This approach allows for custom constraints that off-the-shelf tools can't handle, like matching specific cargo types to specific vehicle capabilities. The API would accept a list of orders and return a complete route plan for each vehicle.

The final deliverable is a private API that integrates directly with your TMS or a simple web interface for your dispatcher. Instead of exporting to Excel, your dispatcher would trigger the planning process with a single click. You receive the full source code in your own GitHub repository, a runbook for maintenance, and complete control over the system. The system is built to fit your workflow, not force you into a new one.