Demand Forecasting
Stop Planning Tomorrow's Demand with Yesterday's Data.
Axlian turns messy sales, stock, promotion, holiday, and external data into ready-to-use forecasts your team can trust.
Axlian turns your existing systems into forecast-ready demand intelligence.
Turn forecasting complexity into planning confidence
Designed for retail teams that need cleaner forecasts, faster cycles, and fewer manual corrections, Axlian helps planners move from spreadsheet work to demand decisions faster.
Confidence
Get machine-generated, expert-curated forecasts your planners can act on immediately.
Efficiency
Reduce time spent cleaning files, fixing spreadsheets, and rebuilding forecasts by hand.
Speed
Cut forecast cycle time so teams move from data correction to demand decisions faster.
Reliable AI Forecasting from Connected Data to Expert-Ready Demand Plans
Axlian cleans and connects sales, stock, POS, ecommerce, promotions, and holiday data so every forecast starts from reliable signals.
Axlian blends AI, statistical, machine learning, and neural models to capture both long-term trends and short-term demand shifts.
Axlian combines AI-generated forecasts with expert review for faster, more reliable planning.
Axlian helps planners test scenarios, manage exceptions, and adapt forecasts to promotions, seasonality, stockouts, and lifecycle shifts.
Axlian delivers forecasts through files or automated feeds, tracks accuracy, and improves models with every cycle.
Forecasting accuracy starts before the model runs.
Axlian manages the full forecasting cycle, turning shared data into ready-to-use forecasts that improve with every run.
Forecasting Built on Clean Data and Expert Intelligence
Axlian combines cleaner inputs, external signals, machine learning, and human curation to deliver reliable forecasts into the tools your team already uses.
Forecasts built from cleaner data
- Remove outliers and noise
- Fill missing data gaps
- Normalize and enrich forecast features
External signals included
- Add external demand signals
- Enrich forecasts with market data
- Use custom and third-party data sources
ML plus human curation
- Review AI-generated forecasts
- Refine with industry expertise
- Add forecasting support without extra hires
Delivered into existing workflows
- Feed forecasts into ERP and Excel
- Connect planning and reporting tools
- Keep teams working in familiar systems
Forecast Cycle Time
Planner Weekly
Forecast Improvement
From disconnected data to trusted demand plans, every forecast ready for action.
How brands run on Axlian
Explore Axlian Magic
Fashion Retail
Solving the Markdown Cycle
See how a high-growth fashion brand improved margins by 15% in 90 days.
Cosmetics
Beating the Trend Cycle
See how cosmetics brand cut core stockouts to zero by replenishing on real-time POS signals.
CPG
Ending the Out-of-Stock Penalty
See how a CPG brand hit a 98% retailer fill rate and stopped paying penalty fees.
Transform the Way you Work
AI demand forecasting software uses machine learning to predict future sales by SKU, location, and time period, accounting for seasonality, promotions, new products, and external signals beyond what simple historical averages catch.
Customer deployments typically see 5 to 10 basis points of forecast accuracy improvement with 60% reduction in forecast cycle time. Accuracy depends on data quality and SKU complexity.
Yes. Axlian forecasts at the SKU-location-week level by default, with rollups available by category, channel, region, and time period for different planning needs.
Promotions get explicit lift modeling including cannibalization across related SKUs. Seasonality is detected automatically per SKU and adjusted across forecast cycles.
Statistical forecasting uses historical patterns alone. AI forecasting blends statistical models, machine learning, neural networks, and external signals, adapting to changing demand patterns faster.
Time to value is typically 6 to 12 weeks, including data ingestion, model setup, accuracy validation, and integration with your existing planning workflow.
Yes. Planners can adjust forecasts where they have business context such as promotions, customer commitments, or competitor moves. Overrides are tracked and the system learns from them over time.
