What challenges did Luxottica face before AI implementation?

Complex, multi-echelon supply chain with high demand volatility and seasonality. Challenge managing a global distribution network and a diverse, frequently changing product portfolio. Manual-intensive inventory and replenishment processes that limited scalability and responsiveness.

What was the impact of the AI solution?

Reduced stock levels by 10% within 6 months while maintaining service levels. 50% reduction in manual planning intervention. Service level improvement to 98.5%, with 80% fewer back orders in spare parts. Accelerated, more frequent and data-driven supply chain planning cycles. Direct Azure consumption increased by over $90,000 annually.

Source

Microsoft

Luxottica streamlines and automates global supply chain planning and execution

Use case typeSupply chain forecastingUpdated Jun 13, 2026

Luxottica, a major global eyewear manufacturer, partnered with ToolsGroup to transform and automate its supply chain management using Microsoft Azure cloud technologies. Before the deployment, Luxottica faced a complex supply chain with high product heterogeneity, seasonality, and a global footprint spanning 26 distribution centers. ToolsGroup migrated their Service. Optimizer 99+ to Azure, automating inventory optimization, demand variability modelling, and replenishment planning, using advanced analytics and AI/ML on the Azure cloud platform and Cortana Intelligence Suite. The system enabled Luxottica to significantly lower stock levels while maintaining excellent customer service. Manual intervention in planning decreased by 50%, and back orders were cut by 80% in the spare parts segment, with service levels reaching 98.5% within three months. Additional benefits included faster, highly automated S&OP cycles, scalable analytics, and more effective decision-making under volatile e-commerce and long-tail demand.

Organization: Luxottica
Industry: Manufacturing
Location: Italy

Reported outcomes

+98.5%

quantified impactOther quantified impact

−10%quantified impact−50%quantified impact+80%quantified impact

Strategic outcomes

Speed & agilityAutomated supply chain planning cyclesRisk & complianceImproved supply reliabilityScale & capacityScaled global planning operationsBetter decisions & insightEnabled more effective decision-making

Primary read

Use case focus

Showing 2 of 2

1Automated Supply Chain Planning and Inventory Optimization
2Predictive Demand Forecasting for Fashion and Apparel

Complex, multi-echelon supply chain with high demand volatility and seasonality.
Challenge managing a global distribution network and a diverse, frequently changing product portfolio.
Manual-intensive inventory and replenishment processes that limited scalability and responsiveness.

Implementation of ToolsGroup’s Service.Optimizer 99+ as an automated, cloud-based solution on Microsoft Azure.
Use of advanced analytics, stochastic modelling, and machine learning for demand forecasting and inventory planning.
Integration with IoT and real-time market data through Azure and Cortana Intelligence Suite.

Technologies

Microsoft Azure Cortana Intelligence Suite Azure Cloud Platform

Reduced stock levels by 10% within 6 months while maintaining service levels.
50% reduction in manual planning intervention.
Service level improvement to 98.5%, with 80% fewer back orders in spare parts.
Accelerated, more frequent and data-driven supply chain planning cycles.
Direct Azure consumption increased by over $90,000 annually.

Architecture

ToolsGroup’s Service.Optimizer 99+ operates as a SaaS solution on Microsoft Azure cloud. It ingests demand, supply chain, and IoT data, leveraging Cortana Intelligence Suite and Azure Cloud Platform for analytics and optimization. Outputs are delivered through automated workflows, dashboards, and integration with enterprise business processes such as S&OP and spare parts management.

Implementation partners1

ToolsGroup

Sources & evidence1

Groundedness: 4/5

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

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Customer evidence