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Transformative global supply chain planning “from farm to fork” with AI

Producing practical and tasty food for the world

MBRF, one of the world’s largest food companies, has a presence in 117 countries as a global food company based on a truly multi-protein, 100% integrated platform. Present on the table of millions of people around the world with strong, iconic, and market-leading brands such as Sadia, Perdigão, Qualy, Sadia Bassi, National Beef, Banvit, and Paty. There are 8 million tons of food produced each year, serving more than 425,000 customers with simplicity, agility, and efficiency.

International and country-specific regulations affect demand planning

MBRF follows international certification requirements, as well as each country’s food industry regulations and Halal practices. Issues related to sustainability, animal welfare, and other environmental, social, and governance (ESG) aspects have a significant impact on demand planning and forecasting.

To maintain this trajectory, in operations focused on poultry and swine production and management, MBRF planners faced several global challenges in supply chain management. MBRF identified the need for a system that would support planners to anticipate and respond quickly to risks and opportunities on a global scale. Accurately forecasting and adjusting quickly to fluctuations in supply and demand – taking into account production and certification constraints, shelf life, and safety stock needs to ensure continuous supply – proved to be an operational challenge.

Transformation aligns business processes with best practices

MBRF selected SAP Integrated Business Planning (IBP), a cloud-based ERP system, for global supply management, given the company’s need for a proven, data-driven tool for operations and supply planning, with a strong global presence and SAP’s deep knowledge of the food industry.

MBRF transitioned to IBP to optimize global supply chain performance management. For example, in addition to increasing the accuracy of forecasts with AI-enhanced statistical models and machine learning algorithms, MBRF is able to optimize inventory management, improve logistics efficiency, and unify and consolidate the entire planning area.

Integrated management of the poultry and pork production chain: 500,000 deliveries per month x 5M tons of food per year

MBRF relies on the cloud-based ERP system for short- and long-term poultry and swine supply chain planning. This includes managing everything from daily visibility into sales, production, and inventory variations, to demand and production data for the annual budget, which is critical for the year ahead.

SAP IBP is part of a process in the poultry and pork chain that reaches 500,000 deliveries per month to 415,000 customers, representing 5 million tons of food produced per year.

Integrated poultry and swine supply chain management ensures that forecasts and delivery times are accurate. It enables rapid response to changes and increases operational efficiency and accuracy.

According to MBRF, the use of advanced technologies and artificial intelligence (AI) is essential to overcome the challenges of supply chain management with detailed and flexible planning. In addition, consolidating information on a single platform, aligning financial and operational planning, and improving collaboration between departments are key steps for the company’s operation.

MBRF uses SAP Business AI in SAP IBP for demand planning and sensing, statistical modeling, forecast accuracy and integrated analytics, time series analysis, and exception management – all essential for its more than 400 poultry and swine supply chain planners.

“At MBRF, we have experienced benefits with SAP IBP with Business AI to maintain close visibility of sales and production planning and inventory variations in the poultry and swine chain,” says Gilberto Andretta, Executive Technology Manager at MBRF. “For example, as MBRF produces food, there are some SKUs that have a short shelf life. Therefore, any variation in demand must be met quickly, otherwise MBRF loses this product.”

MBRF is also using SAP IBP with the Joule virtual assistant to leverage SAP Help Portal and Chatbot for conversational research and assistance. MBRF’s position is that the virtual assistant will revolutionize the way it interacts with SAP business systems, simplifying all touchpoints.

Planning is 33% faster with centralized demand and forecast data

Instead of manually aggregating data from scattered spreadsheets, poultry and swine chain planners can now accurately access and analyze integrated demand and forecast data 33% faster through a centralized dashboard.

The use of virtual assistant and generative AI will help planners access advanced analytics to improve supply chain planning, adapt to market changes, and optimize inventory levels.

As a result of its digital transformation, MBRF is able to optimize supply chain performance, meet its ESG commitments, comply with regulations, and respond to customer demands faster and more effectively.

For its performance in the supply chain, MBRF was selected as the winner of the SAP Innovation Award 2025 in the Cloud ERP Champion, Food Industry Leader category. Learn more about AI-based and cloud-based deployment in the MBRF pitch deck.


Read More from This Article: Transformative global supply chain planning “from farm to fork” with AI
Source: News

Category: NewsOctober 27, 2025
Tags: art

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    Tiatra LLC.

    Tiatra, LLC, based in the Washington, DC metropolitan area, proudly serves federal government agencies, organizations that work with the government and other commercial businesses and organizations. Tiatra specializes in a broad range of information technology (IT) development and management services incorporating solid engineering, attention to client needs, and meeting or exceeding any security parameters required. Our small yet innovative company is structured with a full complement of the necessary technical experts, working with hands-on management, to provide a high level of service and competitive pricing for your systems and engineering requirements.

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