AI Agents in Food & Beverage with Allie Systems
Allie Systems' AI powered software increases food and beverage manufacturing performance with AI agents enabling you to "talk to the factory" and optimize process parameters.
The food and beverage manufacturing industry is rapidly changing, driven by economic pressures, consumer preferences, and regulatory demands. Rising labor costs and widespread shortages force companies toward automation and advanced technologies. Simultaneously, high freight costs are squeezing profit margins, compelling manufacturers to reevaluate their supply chain strategies and seek more efficient logistics solutions while maintaining food traceability in compliance with new regulatory frameworks such as the USA’s Food Safety Modernization Act (FSMA).
I’ve experienced the pressures faced by food and beverage manufacturers firsthand while working on production analytics and autonomous systems across multiple continents. So when I caught up with Alex Sandoval, Founder and CEO of Allie, I was impressed by how his technology is making the next bite more consistent.
AI-Driven Autonomy
Imagine a yogurt factory where machines adjust their settings to perfect consistency or a brewery where AI agents fine-tune fermentation temperatures in real-time for optimal flavor. This is a world where food waste can be slashed to near zero and production lines reconfigure themselves to meet dynamic demand with minimal human intervention. While this vision may seem like it’s far out in the future, it's rapidly becoming a necessity in an industry plagued by inefficiencies.
By leveraging factory-level intelligence with AI agents, manufacturers can create a new model for success - one that optimizes processes, minimizes waste, and ensures consistent quality across complex product lines demanded by quickly changing consumer tastes.
AI Agents enable Autonomous Decision-Making in Manufacturing
AI agents in modern food processing facilities represent a major leap in control system architectures. Agents powered by reinforcement learning algorithms operate as autonomous decision-making units responsible for process parameters. For instance, in a bread production line, an AI agent can optimize the proofing process by continuously analyzing data from temperature, humidity, and dough volume while cross-referencing this information with historical data and production targets. When deviations are detected, the agent initiates proactive actions, from adjusting machine parameters to recalibrating ingredient ratios in the distribution center.
What sets Allie’s AI agents apart is their direct integration with PLCs via industrial protocols like OPC-UA, enabling immediate response times. The real power lies in their ability to directly control machines, adjusting configurations in real time. This autonomous control is always subject to a human-in-the-loop approval system to ensure critical changes are vetted before implementation as is common in other manufacturing segments such as chemical processing.
Allie’s RealTime Factory system connects control systems, data, and computing. It begins with the data sources, where IoT sensors, PLC data, MES and ERP systems, and historical production records continuously raw information into the system. Datasets are channeled into a structured format for processing. The heart of the system lies in the AI and machine learning component, where this processed data feeds models and AI agents.
The agents’ real-time understanding of the manufacturing line is fed into the Decision Making and Control stage, where a rule engine applies predefined operational parameters where a human-in-the-loop interface allows for approval and oversight. The PLC Integration Module communicates directly with manufacturing equipment, while a protocol like OPC-UA ensures standardized, secure communication across different industrial systems. This architecture enables a manufacturing environment where AI agents can autonomously control and optimize processes, adjusting machine configurations in response to production demands.
From Architecture to Equipment
To operationalize AI agents in manufacturing environments, integration is required with equipment across various ISA-95 levels. At the process level in beverage production, this can include blowers, pasteurizers, filling machines, coolers, and labeling machines. In food processing, this can include fryers and ovens, tumblers, batter mixers and applicators, IQF freezers, and packaging systems. This includes data streaming from top OEMs such as GEA, Tetra Pak, Alfa Laval, Krones, and Multivac.
Oftentimes this requires integration with PLCs from major vendors such as Siemens S7 series or Allen-Bradley ControlLogix, which provide required real-time data streaming. Edge computing such as industrial-grade single-board computers or specialized IoT gateways is crucial for processing data at the source to reduce latency in the AI agent decisions. These devices require robust, water and heat-resistance hardware to ensure uninterrupted operation under harsh conditions. These devices support a range of protocols, including Industrial Ethernet (e.g. EtherNet/IP, Profinet) wireless standards, and IoT-specific protocols (MQTT, OPC UA).
Integration between edge computing devices and ERP systems is also necessary. This allows for real-time data flow from and to the floor to business systems, allowing for accurate order tracking and automated adjustments based on product specifications. Common ERP systems include SAP S/4HANA, Oracle ERP Cloud, and Microsoft Dynamics 365, as well as industry-specific solutions.
Autonomy in Action at JUMEX
To taste Allie in action, take a sip of JUMEX, the leading juice producer in Mexico, who is building an AI-first approach to beverage production. Grupo Jumex currently holds the largest market share in Mexico and has a presence in 23 countries across all continents. With 12 production plants utilizing 18 different fruits and vegetables, they've created a portfolio of over 500 products. This scale and complexity made JUMEX an ideal candidate for AI-driven optimization.
Allie’s implementation strategy focused on comprehensive data integration across PET, Hot-fill, and Glass lines, providing a holistic view of the entire production process. Allie deployed IoT gateways across production lines, connecting key equipment to stream data end-to-end from blowers down to palletizers.
These intelligent systems learned from historical data to optimize procedures, predict potential failures and improve resource allocation. The AI agents continuously fine-tuned parameters such as fill rates, pasteurization temperatures, and conveyor speeds, creating recommended micro-adjustments.
The results were transformative: Each production line achieved an increase of 17% in efficiency gains, without significant changes to existing equipment or additional labor costs. These gains translated to over 500,000 additional bottles on average per month per line. JUMEX's enhanced production capabilities allowed for shortened lead times and more flexible production runs. JUMEX is confident with Allie it can more effectively manage economic pressures, consumer preferences, and regulatory demands.
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