What challenges did BRF face before AI implementation?

High supply chain unpredictability from commodity price fluctuations and erratic weather impacts. Manual demand planning and forecasting incurred significant time delays (10+ days for insights). Lack of scalable analytics hindered supply chain, sales, and production optimization. Analysts' productivity constrained by manual data merges and analyses. Difficulty in scaling and democratizing analytics across business units.

What was the impact of the AI solution?

Reduced insight generation time from 10 days to nearly real-time. Improved sales forecasting accuracy and operational agility. Increased revenue via tailored recommendation adherence (statistically significant gains reported). Streamlined analyst workload—analysts focus on strategy, not routine analysis. Broadened and democratized AI use across supply chain and commercial teams.

Source

MicrosoftProductionEvidence: Medium65/100

BRF enhances food supply chain efficiency and sales forecasting

Use case typeSupply chain forecastingUpdated Jun 13, 2026

BRF, one of the largest global food producers based in Brazil, implemented Microsoft Azure Machine Learning to enable advanced analytics and AI-powered forecasting across its supply chain and sales processes. Supply chain unpredictability due to commodity price volatility and erratic weather caused challenges in demand planning, pricing, and operational efficiency. BRF launched a Center of Excellence (COE) to develop and scale machine learning models for forecasting supply, optimizing production, and generating tailored customer recommendations. Azure Machine Learning accelerated new model deployments (from 10 days to nearly real-time), democratized AI for business users, and improved explainability for model results. The automated ML and MLOps capabilities allowed analysts to focus on strategic tasks rather than manual analytics, and enabled rapid expansion of AI use across multiple business units. An AI-powered recommendation engine piloted with 70% of BRF's sales teams drove measurable revenue gains as customers adhered to machine-driven suggestions. Data transparency, explainability, and end-user trust in AI were key to company-wide adoption of new analytics-driven business processes. The company expresses a commitment to sustainability and reducing food waste while delivering quality products efficiently.

Organization: BRF
Industry: Consumer & Food
Location: Brazil

Reported outcomes

10 days

timeTime & speed

Strategic outcomes

Speed & agilityReduced insight generation to nearly real-timeBetter decisions & insightImproved sales forecasting accuracyNew product / capabilityDeployed AI forecasting and recommendation modelsCustomer experience & trustIncreased trust through model explainability

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

Normalized claim

Time: 10 days decrease

microsoft.comCustomer storyInferred claimMedium evidence strength

Reduced insight generation time from 10 days to nearly real-time.

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

Customer: BRF
Provider: Microsoft
Maturity: Production
Linked source: microsoft.com

Supply chain unpredictability due to commodity price volatility and erratic weather caused challenges in demand planning, pricing, and operational efficiency

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1AI-Driven Supply Chain Forecasting
2Personalized Customer Recommendation Engine for Food Sales
3Automated Demand Planning and Price Optimization

High supply chain unpredictability from commodity price fluctuations and erratic weather impacts.
Manual demand planning and forecasting incurred significant time delays (10+ days for insights).
Lack of scalable analytics hindered supply chain, sales, and production optimization.
Analysts' productivity constrained by manual data merges and analyses.
Difficulty in scaling and democratizing analytics across business units.

Established a Center of Excellence in advanced analytics to drive adoption of Azure Machine Learning and MLOps.
Deployed machine learning models for demand forecasting, pricing, and customer recommendation generation.
Automated model development, deployment, and management with Azure MLOps tools.
Democratized business user access to AI-generated insights for better decision-making.
Increased model transparency and explainability to foster trust among business users.

Technologies

Azure Machine Learning

Reduced insight generation time from 10 days to nearly real-time.
Improved sales forecasting accuracy and operational agility.
Increased revenue via tailored recommendation adherence (statistically significant gains reported).
Streamlined analyst workload—analysts focus on strategy, not routine analysis.
Broadened and democratized AI use across supply chain and commercial teams.

Architecture

BRF’s Center of Excellence in advanced analytics deploys Azure Machine Learning models for demand/sales forecasting and customer recommendations. Automated MLOps workflows manage development, deployment, and monitoring. Business users access model insights through streamlined dashboards. AI models integrate with existing ERP and supply chain systems for end-to-end transparency and rapid time-to-decision.

Sources & evidence1

Evidence: Medium65/100Evidence strength