What challenges did Blue Yonder face before AI implementation?

Retailers face risks of out-of-stock scenarios leading to lost sales. Current systems lack integration of disparate data sources like ERP, CRM, and weather forecasts. Retailers require reliable analysis of vast data volumes every hour. Need for scalable solutions to manage growing data complexity and processing demands.

What was the impact of the AI solution?

Improved forecast accuracy by up to 40%. Generated over 1 billion weekly sales forecasts. Reduced logistics costs and stock management issues. Enhanced data analysis for higher operational efficiency.

Rank#2,343of 2,978

Source

MicrosoftExpanded

Blue Yonder modernized retail supply chain management

Use case typeInventory optimizationUpdated Jun 13, 2026

Blue Yonder, a supply chain solutions company, modernized retail management using Exasol on Microsoft Azure and Power BI platform. The solution integrates diverse data sources including ERP, CRM, merchandise management, and weather forecasts to provide real-time analysis and insights for retailers to optimize stock levels. By leveraging scalable analytics performance from Azure and Power BI with Exasol's high-performance analytics, Blue Yonder enables retailers to manage over a billion weekly sales forecasts, improving forecast accuracy up to 40%. As a result, retailers achieve significant cost savings and reduce stockouts globally.

Organization: Blue Yonder
Industry: Retail
Location: United States
Published: October 2023

Reported outcomes

+40%

accuracyQuality & accuracy

Strategic outcomes

New product / capabilityEnabled real-time scalable retail analyticsBetter decisions & insightImproved sales forecast accuracyScale & capacityHandled billion-scale weekly forecastsCost efficiencyReduced logistics and stock costs

Catalog median for quality & accuracy deployments: +90% across 281 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1retail stock optimization
2predictive sales forecasting
3logistics cost reduction

Retailers face risks of out-of-stock scenarios leading to lost sales.
Current systems lack integration of disparate data sources like ERP, CRM, and weather forecasts.
Retailers require reliable analysis of vast data volumes every hour.
Need for scalable solutions to manage growing data complexity and processing demands.

Implemented Exasol analytics database on Microsoft Azure.
Integrated Exasol with Microsoft Power BI for real-time, scalable data analytics.
Enabled analysis of ERP, CRM, merchandise, and weather data.
Deployed predictive applications for improving sales forecasts.
Utilized Azure cloud infrastructure for scalability and affordability.

Technologies

Azure ML Azure IoT Power BI

Improved forecast accuracy by up to 40%.
Generated over 1 billion weekly sales forecasts.
Reduced logistics costs and stock management issues.
Enhanced data analysis for higher operational efficiency.

Architecture

Exasol analytics database is deployed on Microsoft Azure's cloud infrastructure. It integrates with Microsoft Power BI for scalable real-time analytics. The system analyzes data from ERP, CRM, merchandise management, and weather sources for retailers to generate accurate forecasts and optimize stock levels.

Implementation partners1

Exasol

Sources & evidence1

ExpandedExpanded

The same organization appears in newer AI deployment evidence.