What challenges did Unilever face before AI implementation?

Inaccurate demand forecasting leading to stockouts and overstocking. Global supply chain disruptions and material shortages. Inefficient manual procurement workflows and delayed order fulfillment. Lack of real-time visibility across the supply chain.

What was the impact of the AI solution?

Increased demand forecast accuracy to 95%. Reduced inventory waste by 30%. Improved procurement agility and logistics efficiency. Achieved significant cost and operational savings.

Source

MicrosoftProductionEvidence: Medium50/100

Unilever Boosts Supply Chain Efficiency and Forecast Accuracy

Use case typeWorkflow automationUpdated Jun 13, 2026

Unilever, a global manufacturer, implemented advanced AI technologies across its supply chain to improve agility, efficiency, and responsiveness. Facing global disruptions and volatile market demands, the company shifted from traditional, manual processes to a data-driven, AI-powered ecosystem. Incorporating solutions like Dynamics 365 Supply Chain Management, Azure AI, and Power Automate, Unilever overhauled its forecasting, procurement, and logistics processes. By adopting intelligent forecasting models, real-time procurement automation, and logistics optimization, Unilever achieved notable improvements in visibility and decision-making. The integration of Microsoft Copilot agents further streamlined order tracking and operational support, enabling dynamic inventory management and automated supplier communication. These changes resulted in higher forecast accuracy, reduced inventory waste, and significant cost savings, demonstrating the tangible business value of Microsoft’s AI-driven supply chain solutions. Unilever’s case exemplifies how integrating Microsoft’s technology stack enables end-to-end automation and resilience, setting a new benchmark for supply chain transformation in manufacturing.

Organization: Unilever
Industry: Manufacturing
Location: United Kingdom
Published: June 2025

Reported outcomes

+95%

accuracyQuality & accuracy

−30%quantified impact

Strategic outcomes

Better decisions & insightImproved demand forecasting accuracySpeed & agilityImproved procurement and logistics agilityCost efficiencyReduced inventory wasteScale & capacityEnabled end-to-end supply chain automation

Catalog median for quality & accuracy deployments: +41% across 63 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Accuracy: 95% increase

alfapeople.comJun 24, 2025UnknownInferred claimMedium evidence strength

Increased demand forecast accuracy to 95%.

Normalized claim

Quantified impact: 30% decrease

alfapeople.comJun 24, 2025UnknownInferred claimMedium evidence strength

Reduced inventory waste by 30%.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Unilever
Provider: Microsoft
Maturity: Production
Linked source: alfapeople.com

The integration of Microsoft Copilot agents further streamlined order tracking and operational support, enabling dynamic inventory management and automated supplier communication

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Intelligent Demand Forecasting for Manufacturing Supply Chains
2Automated Procurement and Order Fulfillment
3Real-Time Logistics Optimization with AI Agents

Inaccurate demand forecasting leading to stockouts and overstocking.
Global supply chain disruptions and material shortages.
Inefficient manual procurement workflows and delayed order fulfillment.
Lack of real-time visibility across the supply chain.

Implemented Dynamics 365 Supply Chain Management for unified planning and visibility.
Integrated Azure AI for predictive demand forecasting and inventory optimization.
Automated procurement and logistics workflows with Power Automate.
Deployed Microsoft Copilot agents to streamline order tracking and routine updates.

Technologies

Dynamics 365 Supply Chain Management Azure AI Power Automate Microsoft Copilot

Increased demand forecast accuracy to 95%.
Reduced inventory waste by 30%.
Improved procurement agility and logistics efficiency.
Achieved significant cost and operational savings.

Architecture

AI models in Azure ingest historical and real-time supply chain data, powering predictive analytics for demand forecasting and procurement. Dynamics 365 Supply Chain Management serves as the unified platform for planning and execution, while Power Automate orchestrates automated workflows between procurement, inventory, and logistics. Microsoft Copilot agents provide operational support within Dynamics for updates and order management.

Sources & evidence1

Evidence: Medium50/100Evidence strength