Loading…
Use Case
Standard inventory forecasting tools use generic models that ignore your specific demand patterns, seasonality, and supplier lead times. They treat your SKUs like the average SKU in your category, which means stockouts when your demand spikes earlier than the benchmark, and overstock when your seasonality doesn't match the template.
Custom AI forecasting trains on your actual historical data: SKU-level demand curves, supplier reliability, warehouse constraints, and generates reorder recommendations your ops team can act on. The model learns what drives demand in your specific business, not a generic model's assumptions about your industry.
We build demand forecasting models, reorder point calculators, and alert systems integrated with your ERP or WMS. Your ops team reviews exceptions, not spreadsheets.
Tell us about your inventory forecasting challenge.
Six components that replace manual reorder calculations with a data-driven pipeline your ops team can trust.
A forecasting model trained on your actual historical sales data: SKU by SKU. It learns your product-specific seasonality, demand curves, and promotional lift patterns rather than applying a generic industry benchmark. The result is reorder recommendations based on your data, not a vendor's averages.
Reorder points are calculated per SKU using your historical lead times, demand variability, and configurable safety stock logic. The calculator accounts for supplier reliability variance, a supplier that is frequently late gets a longer safety stock buffer than a consistently on-time supplier.
Safety stock rules are configurable by product category, warehouse, or customer tier. High-velocity SKUs with high stockout cost get aggressive buffers. Low-velocity products with long shelf lives get lean safety stock. Your ops team sets the business rules; the model does the calculation.
The forecasting pipeline integrates with your existing ERP or WMS: NetSuite, SAP, Shopify, WooCommerce, or custom systems. Inventory levels, sales history, and purchase orders are pulled from your source of truth. Reorder recommendations can be pushed back to your system or surfaced in a dashboard.
Predicted stockouts (based on current inventory plus incoming purchase orders minus projected demand) trigger alerts to your ops team before the stockout occurs. Overstock alerts flag SKUs where projected demand does not consume current inventory before the next order cycle.
For SKUs with no sales history, the model uses category-level demand patterns as a starting point, blended with any analogous product data you provide. New product forecasts are clearly marked with lower confidence scores until sufficient history accumulates, typically 8 to 12 weeks of sales data.
From raw sales data to live reorder recommendations, the build sequence.
Data extraction and cleaning
We pull historical sales data, inventory records, and purchase order history from your ERP or data warehouse. The data cleaning phase identifies and handles gaps, returns, cancelled orders, and promotional periods that would otherwise skew the model.
Feature engineering
Features are built from your raw data: seasonality indices per SKU, promotional lift multipliers, supplier lead time distributions, and demand volatility scores. These features capture the patterns that drive reorder decisions in your specific business.
Model training and backtesting
The forecasting model is trained on your historical data and backtested against held-out periods, typically the last three to six months of actuals. We measure forecast accuracy (MAPE, bias) and tune the model until accuracy meets your targets.
Reorder logic configuration
Your ops team defines the business rules: service level targets by category, safety stock multipliers, supplier lead time assumptions, and order frequency constraints. These rules are encoded into the reorder point calculator and can be adjusted without touching the model.
Integration and deployment
The pipeline integrates with your ERP or WMS. Reorder recommendations are surfaced in your system or in a standalone dashboard, whichever your ops team uses to make purchasing decisions. The pipeline runs on a weekly or daily schedule.
Monitoring and model refresh
Forecast accuracy is monitored over time. Models are refreshed on a scheduled basis as new sales data accumulates. Significant demand shifts (caused by market changes or new distribution channels) trigger an alert for a model review.
Product businesses with meaningful sales history and ops teams making regular reorder decisions.
Managing reorder decisions manually across hundreds of SKUs means constant firefighting between stockouts and overstock. A custom forecasting model generates weekly reorder recommendations per SKU, your ops team reviews exceptions rather than running the numbers from scratch.
Seasonal demand patterns in wholesale are hard to model with generic tools. A custom model trained on your specific seasonality (accounting for your customer mix, geographic distribution, and historical promotional events) gives more accurate reorder timing than off-the-shelf forecasting.
Finished goods forecasts need to translate into component-level reorder requirements through the BOM. A custom pipeline handles multi-level BOM explosion from demand forecasts to component reorder points, something generic inventory tools handle poorly.
We’d rather decline than take a project that won’t deliver value.
Businesses with fewer than 12 months of clean sales history
Time-series forecasting models need sufficient historical data to learn seasonality and demand patterns. With less than 12 months of history, the model cannot distinguish seasonality from noise. If you have less history, we can still build a baseline model, but forecast accuracy will be lower and we will set that expectation clearly during discovery.
Teams who want a dashboard without acting on the recommendations
The value of inventory forecasting comes from acting on the reorder recommendations consistently. If your procurement process is too manual or approval-heavy to respond to weekly reorder signals, the model output will not translate to business impact. The system is most valuable when your team can act on recommendations within a few business days.
Tell us how many SKUs you manage, which ERP or WMS you use, and whether stockouts or overstock are your bigger problem today. We'll reply within one business day.