Automate Your Demand Forecasting with a Custom AI System

Most small logistics companies start by forecasting in Excel or Google Sheets. This approach is manual, slow, and highly error-prone. A single broken VLOOKUP or copy-paste error can invalidate an entire week's plan, leading to either idle trucks or rejected loads. It cannot incorporate external variables like holidays or fuel prices without hours of manual data entry.

A built-in forecasting module in a Transportation Management System (TMS) seems like the next step. However, these tools typically rely on simple moving averages. They are good at showing you last month's trend but fail to predict future changes. They cannot account for a new client ramping up volume or a change in shipping patterns, meaning you are always reacting to demand instead of anticipating it.

This leads teams to look at dedicated forecasting software, but these platforms are built for enterprise retail, not logistics. A tool like Anaplan is powerful but requires a six-figure budget, a dedicated implementation team, and a full-time analyst to run. For a 20-person 3PL, the per-seat pricing and complexity make it a non-starter.

Syntora would start by establishing a direct API connection to your TMS to pull at least 24 months of historical shipment data. Python with Pandas would be used to clean and structure this data, joining it with external sources like the EIA.gov API for historical fuel prices. From this prepared data, a range of candidate features would be engineered to capture seasonality, day-of-week effects, and client-specific trends.

The approach would then involve testing multiple model architectures. A SARIMAX model in statsmodels provides a solid baseline for capturing seasonal patterns. A gradient boosting model using LightGBM would also be developed, as this class of model often offers improved predictive accuracy on complex, non-linear relationships compared to traditional baselines. Training such a model on a dataset of 100,000 shipments typically completes within an hour.

The developed LightGBM model would be packaged into a lightweight FastAPI service. Syntora would deploy this service on a serverless platform such as AWS Lambda, allowing it to run on a schedule without a dedicated server. This design supports efficient, on-demand execution.

Forecasts would be automatically written back to a destination of your choice, such as a Supabase database, a Google Sheet, or directly into a custom field in your TMS via its API. Syntora would establish monitoring using tools like CloudWatch to track model accuracy. If the Mean Absolute Percentage Error (MAPE) on a critical lane drifts above a defined threshold for a set period, an alert could be sent for a manual review and potential model retraining.