Demand Forecasting
Predict demand with precision.
Plan inventory with confidence.
NaviaLabs generates AI-powered demand forecasts up to 90 days ahead — per SKU, per location. Know what to order, when to order, and how much, before stock levels become urgent.
The Problem
Without accurate demand signals, inventory planning is guesswork
Most growing businesses plan inventory based on last month's sales or gut instinct. The result is a permanent cycle of stockouts, over-buying, and reactive decision-making.
Lagging indicators only
Looking backwards at sales history doesn't account for trends, seasonality, or lead time. By the time demand spikes are visible in reports, it's too late to act.
Category-level planning
Planning at category level misses SKU-level variation. Top-selling items run out while dead stock of related SKUs accumulates. The net average looks fine; the reality is chaos.
No forward visibility for procurement
Without demand forecasts, procurement teams can't place orders at the right time for items with 30–90 day lead times. Emergencies and expediting become the default operating mode.
Platform Capabilities
AI demand forecasting built for operations teams
AI Demand Models
Forecasting models analyse historical sales velocity, seasonality patterns, trend lines, and lead time variability to project demand up to 90 days ahead per SKU.
Seasonal Intelligence
NaviaLabs detects seasonal demand patterns and adjusts forecasts automatically — so you're building stock before peaks and reducing orders before troughs.
SKU-Level Forecasts
Forecasts are generated at individual SKU level, not just category level. High-velocity items and slow-movers are modelled differently based on their actual behaviour.
Forecast Risk Alerts
When a forecast indicates demand will outpace available stock, NaviaLabs raises an early warning — with recommended order quantity and timing to bridge the gap.
Replenishment Planning
Convert demand forecasts directly into replenishment plans. NaviaLabs calculates optimal order quantities that account for MOQ, lead time, and storage capacity.
Forecast Accuracy Tracking
Monitor forecast accuracy over time per SKU and category. Identify where models perform best and where manual override or additional data improves precision.
How It Works
From historical data to 90-day demand plans
Historical data ingestion
NaviaLabs ingests sales history, stock movements, and order data. A minimum of 90 days of history enables reliable base forecasting; 12+ months enables seasonal modelling.
Model training & pattern detection
AI models identify demand trends, seasonality cycles, growth rates, and anomalies (promotions, one-off spikes) to build a demand curve per SKU.
Forecast generation
Rolling 90-day demand forecasts are generated for every active SKU. Forecasts update as new sales data arrives — no manual recalculation required.
Gap identification
NaviaLabs compares the forecast against current stock and committed supply to identify future coverage gaps — weeks before they materialise.
Replenishment recommendation
For each identified gap, NaviaLabs recommends a purchase quantity, target order date, and preferred supplier — respecting MOQs and lead times.
Industry Use Cases
Demand planning for complex supply chains
Production material planning
Align raw material procurement with production forecasts. Prevent material shortages from disrupting production schedules — and eliminate excess safety stock that ties up capital.
Long lead-time planning
When supplier lead times span 30–90 days, accurate demand forecasting is critical. NaviaLabs ensures orders are placed far enough in advance, even for unpredictable demand.
Seasonal demand management
Identify peak periods 6–8 weeks in advance. Build stock intelligently, negotiate better terms with bulk early orders, and avoid costly last-minute restocking.
Customer Outcomes
Plan better. Stock smarter. Grow faster.
FAQ
Common questions
How much historical data is needed to start forecasting?
NaviaLabs can generate useful forecasts with as little as 90 days of sales history. For seasonal modelling, 12+ months of data is recommended. Forecasts improve continuously as more data accumulates.
How does the system handle products with irregular demand?
Items with lumpy or irregular demand are modelled differently — using intermittent demand methods rather than trend-based models. NaviaLabs detects demand patterns and selects the appropriate model automatically.
Can we override AI forecasts manually?
Yes. Forecasts can be overridden at product or category level for known future events — promotions, contract wins, new product launches, or planned discontinuations.
How often do forecasts update?
Forecasts recalculate each time new sales or stock movement data is synced — typically daily. High-frequency businesses can configure more frequent updates.
Does forecasting work for businesses with new products?
New products with no sales history can use manual demand inputs, category-based estimates, or comparable product benchmarks until sufficient sales data is available.
See demand forecasts for your products — today
Import your sales history and see 90-day forecasts per SKU within hours of setup.