AI Demand Forecasting with Azure Cognitive Services and Power BI: 28% Accuracy Gain & 20% Stockout Reduction

A global retail company was experiencing chronic inventory imbalances caused by inaccurate, manually produced demand forecasts. Power BI was already in place for reporting, but all insights were backward-looking. Numlytics built an AI demand forecasting Power BI solution on Azure, integrating Azure Cognitive Services forecasting and Azure Machine Learning to deliver a 28% improvement in forecast accuracy and 20% reduction in stockouts across all regions.

The Challenge: Manual Forecasting Creating Inventory Imbalances

Without predictive analytics retail capability, the company was forecasting demand manually, a process that couldn't account for seasonality, regional trends, or external factors. The result was systematic overstock in some regions and recurring stockouts in others.

• Inventory imbalances: Excess stock in some regions and chronic stockouts in others, driven by inaccurate manual demand predictions.
• Manual forecasting: Analysts producing demand forecasts from spreadsheets, a process that was time-consuming, error-prone, and unable to process regional and seasonal patterns simultaneously.
• Descriptive-only Power BI: Existing dashboards showed what happened, but had no Azure ML demand prediction capability to show what would happen.
• No automated replenishment: Reorder decisions were made manually, weeks after demand signals appeared in data.

The Numlytics Solution: Azure AI Demand Forecasting Platform

Numlytics built a six-component architecture integrating Power BI with Azure's full AI stack, shifting the client from descriptive reporting to inventory optimisation machine learning with automated replenishment triggers.

1. 01
   Power BI Data Modelling

   Sales, market trend, and supply chain data ingested from SAP ERP. Complex Power BI data models visualising seasonal fluctuations and regional demand. Custom DAX measures tracking inventory turnover, stockout frequency, and overstock risk, the foundation for all AI demand forecasting.

2. 02
   Azure Data Lake Storage

   Azure Data Lake established as the central repository for historical and real-time sales and inventory data. Provides the scalable data foundation that Azure Cognitive Services forecasting models require.

3. 03
   Azure Cognitive Services Time Series Forecasting

   Time series forecasting models deployed to predict future demand from historical sales and market patterns. AI analyses seasonality, regional trends, and external factors such as weather and holidays, core of the AI demand forecasting Power BI solution.

4. 04
   Azure Synapse Analytics ETL

   ETL pipelines built in Synapse for efficient data flow between all systems. Synapse-to-Power BI integration enables predictive analytics retail dashboards to scale across all geographies simultaneously.

5. 05
   Azure Machine Learning Continuous Improvement

   Azure ML trains and refines demand models continuously as new sales data arrives. Custom models for specific product lines, regions, and customer segments improve Azure ML demand prediction accuracy over time.

6. 06
   Azure Logic Apps Inventory Automation

   Inventory reordering automated based on AI forecasts. Logic Apps triggers purchase orders in SAP ERP when AI models predict stock will breach threshold, eliminating manual intervention from the replenishment cycle entirely.

The Results

Technology Stack

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