97% Forecast Accuracy
Impact Area: Forecasting & Demand Planning
How an APAC fashion e-commerce leader achieved ~97% SKU-level accuracy during peak sales with Algodel AI
About the Project
A leading fashion e-commerce retailer in APAC sought to improve the accuracy and agility of its demand planning process. The client’s existing systems relied heavily on static planning spreadsheets and backward-looking heuristics, which failed to capture fast-moving consumer shifts and marketing effects in real time.
They required a modern, dynamic platform that could forecast demand at SKU-level granularity and support intraday decision-making across merchandising, inventory, and marketing functions.
Key Issues
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Over-reliance on rigid, rule-based forecasts with limited adaptability
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Absence of real-time performance feedback loops from marketing and campaign data
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Disconnected forecasting from operational execution (e.g., warehouse planning)
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Manual interventions in weekly planning cycles leading to delayed course corrections
Our Approach
An AI-Native Forecasting Platform for Modern Retail
We built a cloud-native demand intelligence platform, integrating time-series forecasting and causal modeling to deliver dynamic, high-accuracy demand signals.
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Data Engineering: Consolidated historical sales, campaign spends, price elasticity, and external signals (e.g., seasonality, weather)
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Advanced Forecasting Models: Built SKU-store-level models using XGBoost and Prophet ensembles, tuned for intraday and weekly granularity
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Driver Attribution Layer: Enabled explainability by surfacing top drivers influencing demand deviation (e.g., discount depth, ad impressions)
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Integration: Synced demand forecasts with warehouse planning systems and campaign planning dashboards
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Alerting & Monitoring: Real-time alerts for significant variances vs plan, enabling course correction by the Growth Team
Impact
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Achieved ~97% SKU-day forecast accuracy during peak sale periods
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Enabled intraday performance correction based on campaign response data
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Improved collaboration between growth, supply chain, and finance teams
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Streamlined warehouse operations by aligning fulfillment planning with real demand
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Enabled automated demand forecast refreshes, reducing manual planning effort by 85%
