Demand Forecasting AI
SKU-Level Accuracy at Scale
Cut forecast error 20-40%, reduce stockouts and excess inventory - integrated with SAP, Oracle, Kinaxis, o9 and your S&OP cycle.
Quick Answer
NeoBram builds demand forecasting AI for manufacturers that delivers SKU-level forecasts with 20-40% lower MAPE than statistical baselines, integrated with SAP IBP, Oracle, Kinaxis and o9 - and embedded into your existing S&OP cycle.
Why This Matters
Forecast Accuracy Drives Every Other Cost.
Every percentage point of forecast error costs working capital, service level, or both. Most manufacturers still rely on ERP statistical forecasts or planner spreadsheets - and accept 30-50% SKU-level MAPE as normal.
Modern ML demand forecasting cuts that error 20-40% by learning from price, promotion, weather, web traffic, distributor sell-through and macro signals - per SKU per location.
We deploy forecasts inside SAP IBP, Oracle, Kinaxis or o9 - planners review and override in their existing UI. The AI augments your S&OP, it doesn't replace it.
Our Tech Stack
Production-Grade Tools We Deploy
Source data
ML & forecasting
Planning integration
MLOps
Architecture Deep-Dive
How We Build It
SKU-level multi-horizon forecasting
Daily and weekly forecasts at SKU-store / SKU-DC granularity, with hierarchical reconciliation up to brand, region and total.
- Daily and weekly horizons up to 18 months
- Hierarchical reconciliation (MinT, top-down, bottom-up)
- Calibrated prediction intervals (P10/P50/P90)
- Driver decomposition per forecast
Promotion & event lift
Models price elasticity, promotion lift, halo and cannibalisation - so the forecast reflects what the planner actually plans.
- Per-SKU price elasticity
- Promotion uplift with halo and cannibalisation
- Holiday and event calendars per market
- Trade-spend ROI estimates
New product introduction (NPI)
Forecasts new SKUs using analog-based and attribute-based models when no history exists.
- Analog SKU selection with similarity scoring
- Attribute-based regression for true-new products
- Ramp curves with confidence bands
- Iterative refinement as actuals arrive
S&OP-ready governance
Planner override workflow, accuracy tracking by planner / category, and continuous learning from override outcomes.
- Side-by-side ML vs statistical vs consensus
- Override reason capture for learning
- Forecast Value Add (FVA) tracking
- Monthly accuracy and bias reports
Data Security, Governance & Safety
Enterprise AI demands enterprise-grade security. Every solution we deploy follows strict data sovereignty, safety, and compliance standards.
Data Sovereignty
- Your data stays in your infrastructure - always
- Deploy on your cloud (AWS, Azure, GCP) or on-premise
- No data leaves your environment
- Full compliance with regional data residency requirements
Model Safety & Guardrails
- NVIDIA NeMo Guardrails for content safety
- PII detection and redaction with Presidio
- Prompt injection defense and input sanitization
- Hallucination detection and factual grounding
Access Control & Audit
- Role-based access control for all AI systems
- Immutable audit logs for every interaction
- SOC 2 Type II, ISO 27001 compliance frameworks
- GDPR, HIPAA, and industry-specific regulations
Responsible AI
- Bias testing with Fairlearn and AI Fairness 360
- Model explainability via SHAP and LIME
- Transparency reports for stakeholders
- Continuous fairness monitoring in production
FAQ
Frequently Asked Questions
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