Data and AI for Food & Beverage

Food & Beverage companies face strong margin pressure driven by raw material volatility, energy costs, logistics constraints, retailer negotiation power and consumer price sensitivity.

Balancing these pressures while protecting profitability requires accurate, timely visibility into costs, demand and commercial performance across the full value chain, from sourcing and production through to retail execution.

Food & Beverage companies must secure food safety, product quality and traceability across complex supply chains spanning agricultural production, factories, recipes, ingredients, packaging and distribution networks.

Ensuring consistency and trust at every stage is essential to protect consumers, brand reputation and regulatory compliance.

This complexity increases as products move across multiple entities, geographies and channels.

Large Food & Beverage groups often operate with a separation between brand or product companies, production entities and market or distribution companies.

This organizational split creates complexity in how products are defined, produced, transferred, localized, sold internally and finally activated in each market.

The result is often fragmented product definitions, unclear ownership and manual rework.

Food & Beverage companies must manage increasingly complex portfolios, including branded products, retailer-specific assortments, private-label products and white-label products with different quality grades, margins and price positioning.

The same manufacturer may need to manage different product versions, recipes, packaging and pricing depending on the retailer and market segment.

Product, recipe, ingredient, packaging, supplier, grower, plant, quality, regulatory, customer, retailer, assortment and pricing data are often fragmented across ERP, PLM, MES, CRM, procurement, supply chain tools, PIM, data pools, local systems and spreadsheets.

In large, decentralized groups, this fragmentation can be reinforced by acquisitions, heterogeneous local ERP environments, different data models and identifiers, and data ownership split across Group and local functions.

Product data is not always transferred cleanly from the brand or production company to the market company.

This creates issues across:

• Internal selling
• Product localization
• Commercial activation
• Assortment readiness
• Retailer listing
• Regulatory compliance
• Operational efficiency

Market companies also often struggle to provide retailers with complete, accurate and timely product information.

When product data is incomplete or inconsistent, it delays retailer listing, creates operational inefficiencies, weakens commercial execution and increases compliance risk.

AI initiatives are constrained not by model availability but by insufficient contextualization, inconsistent business definitions and a lack of trusted governance.

Critical operational knowledge also frequently resides with experienced employees rather than within accessible systems. This creates additional operational risk as workforces transition and experienced employees leave the organization.

Without a trusted operational context, Energy and Utilities companies struggle to:

• Modernize infrastructure
• Improve operational resilience
• Capture and reuse operational knowledge
• Reliably forecast demand
• Scale AI initiatives
• Establish consistent business definitions
• Govern critical operational information

Creating trusted operational context requires Energy and Utilities organizations to connect and govern critical data across operational and enterprise environments.

The critical data involved includes:

• Asset data
• Engineering data
• Operations data
• ERP data
• GIS data
• SCADA data
• Customer data
• Supplier data
• Reference and master data
• Regulatory reporting data
• Environmental reporting data
• Cybersecurity reporting data
• Operational reporting data