Transition from Spreadsheets to AI Demand Forecasting

Most 30-person logistics teams run their forecasting on a master spreadsheet. This model is built with VLOOKUPs and pivot tables, pulling data manually from a Warehouse Management System (WMS). The process is slow, prone to copy-paste errors, and completely breaks if a SKU code is updated in the source system. It cannot account for external factors like weather, public holidays, or client-specific promotions without someone manually adding new columns and formulas.

Consider a logistics team managing 1,000 SKUs for a beverage distributor. Every month, an analyst spends two days exporting sales history and trying to align it with a separate spreadsheet of marketing promotions. A sudden heatwave is coming, but the model has no way to incorporate weather data. The forecast is based only on last year's sales, leading to stockouts of popular drinks and overstock of others. The forecast is a single number, giving planners no sense of the potential upside or downside risk.

The forecasting modules built into many ERP and WMS platforms offer a slight improvement but are architecturally rigid. They often use basic time-series models (like ARIMA) that are blind to causal factors. You cannot add a feature for a key client’s annual sale or a competitor's product launch. The models are black boxes, providing a number with no explanation, which makes it impossible for planners to trust or override the system's logic with their domain knowledge.

The structural problem is that spreadsheets are static calculation tools, and built-in ERP modules are designed for transactional recording, not predictive analysis. Neither is built to continuously integrate diverse, dynamic data sets and learn complex patterns. A logistics operation needs a system designed from the ground up for feature engineering and probabilistic modeling to manage inventory effectively.

The engagement starts with a data audit. Syntora would connect to your WMS or ERP to extract at least 12-24 months of sales history for all 1,000 SKUs. We would then identify and map external data sources, like weather APIs and public holiday calendars. You receive a data readiness report that identifies quality issues and confirms the predictive signals available before any code is written.

The technical approach involves a Python-based forecasting pipeline. We would use a library like LightGBM to build a model that can handle complex relationships between sales, seasonality, promotions, and other factors. This model would be wrapped in a FastAPI service deployed on AWS Lambda, running on an automated schedule. For unstructured data, like client emails about upcoming sales, the Claude API can parse the text and create structured inputs for the forecasting model. This entire pipeline costs under $50/month to run.

The delivered system pushes updated forecasts directly to your WMS or a dedicated Supabase dashboard every 24 hours. Instead of one number, it provides a probabilistic forecast (e.g., a 90% confidence interval) for each SKU. This allows your team to make smarter decisions about safety stock levels. You receive the full source code, a runbook for maintenance, and a system that integrates into your existing workflow.