Retail Store Forecasting
Explainable, omnichannel forecasting at store level
Accurate store-level forecasting is foundational to retail execution, yet it has become increasingly complex. Omnichannel fulfillment, volatile demand patterns, and external disruptions have reduced the effectiveness of traditional time-series forecasting methods that rely solely on historical sales.
o9 Retail Store Forecasting delivers granular, explainable forecasts that reflect how demand is generated and fulfilled at each store. By distinguishing between demand placed at a store and demand fulfilled by a store, the solution supports accurate inventory positioning across BOPIS, ship-from-store, and in-store purchasing.This enables retailers to improve service levels, reduce inventory risk, and respond faster to emerging demand signals.
When historical averages stop working
Store-level demand is influenced by far more than past sales. Promotions, pricing changes, weather, local events, and digital demand signals all affect how and where customers purchase. Traditional forecasting models struggle to capture these drivers, leading to persistent bias and execution inefficiencies.
New product introductions further complicate forecasting, as no historical data exists. Without attribute-based modeling, forecasts rely heavily on manual overrides and intuition.
From black-box forecasts to explainable intelligence
Leading retailers are moving toward explainable, signal-driven forecasting models that combine internal transaction data with external drivers. Transparency is critical—planners must understand why a forecast changed in order to trust and act on it.
o9 enables this shift by delivering white-box forecasting that clearly attributes forecast changes to underlying drivers.
What Makes o9 Different
Explainable Forecasting Models
Forecast changes are transparent and attributable to specific drivers.
Omnichannel Demand Separation
Demand is modeled by fulfillment method, improving inventory placement.
Hierarchical Forecast Consistency
Forecasts remain aligned across planning levels.
Enterprise-Scale Performance
Supports millions of SKU-store combinations without latency.
Industries Supported
Powered by the o9 Digital Brain
Retail Store Forecasting runs on the o9 Digital Brain, integrating internal sales data with external market signals such as weather, events, and digital behavior. The Enterprise Knowledge Graph ensures consistency and traceability across forecasts and downstream plans.
Modular by design, enterprise by default
The solution combines statistical forecasting, machine learning, and external signal integration within a transparent, explainable architecture.
Core Building Blocks
Baseline Store-Level Forecasting
Generates statistical forecasts accounting for seasonality, trends, and promotions at store level.
Hierarchical Forecast Management
Ensures alignment across product and location hierarchies.
New Product Introduction Forecasting
Uses attribute similarity to forecast demand for new items.
Omnichannel Demand Modeling
Separates demand by fulfillment type for accurate execution.
Advanced Building Blocks
Market Signal Integration
Ingests weather, events, macroeconomic, and digital signals.
Machine Learning Feature Engineering
Identifies the most predictive demand drivers per product and store.
Short-Term Demand Sensing
Adjusts near-term forecasts using recent POS signals.
Bias Monitoring and Explainability
Tracks forecast accuracy and explains deviations.
A digital operating model for VUCA conditions
APEX is o9’s AI-powered operating model for enterprises navigating volatility, uncertainty, complexity, and ambiguity (VUCA). It enables organizations to plan, execute, and learn as one connected system.
The o9 Digital Brain powers APEX by connecting enterprise data, knowledge, and decisions through a single intelligent model.
Collaborative Demand Planning is one of the building blocks of the Digital Brain. It contributes domain-specific capabilities into the enterprise-wide model that enables APEX from the ground up—linking this solution to decisions across the entire value chain.
→ Learn how the APEX Operating Model works
Where AI drives real decisions
AI enhances Retail Store Forecasting by delivering explainable, omnichannel demand intelligence at store level.
Machine learning models combine historical sales with leading indicators such as weather, promotions, and digital demand signals to improve accuracy.
Prescriptive analytics translate forecast insights into actionable guidance for inventory positioning and replenishment.
Generative AI explains forecast drivers using natural language, while agentic AI automates monitoring, bias detection, and model recalibration.
→ Learn more about o9 AI innovations
What our customers say
"We made the conscious decision with o9 to bring a quicker ROI by integrating with our legacy SAP. [...] When the full ERP transformation happens, we’re ahead of the game."
Paul Tips
Product Owner at Canyon Bicycles
"What's really succeeding with us is the idea of the connection to the data and a best-in-class UX/UI, so the people that use the business can really make an impact."
David Almeida
Chief Strategy & Technology Officer at Anheuser-Busch InBev
"With o9 AI/ML-based forecasting in place, we’re already seeing improved forecast accuracy, stronger cross-functional collaboration, and faster, more informed decision-making—all within a centralized platform."
Gaby Gutierrez
VP of Global Supply Chain Planning at Amway
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Frequently Asked Questions (FAQ)
It predicts demand at store level while accounting for omnichannel fulfillment.