Few sectors carry higher stakes than food and beverage manufacturing. Margins are tight, regulatory scrutiny is constant, and a single quality failure can pull product from shelves across an entire region. Yet the same plants must also run faster and waste less. The way forward runs through technology convergence, which is the merging of digital, physical, and biological capabilities that, the World Economic Forum argues, is carrying industry beyond the fourth industrial revolution toward the fifth. Where Industry 4.0 connected the factory, the fifth industrial revolution (5IR) reorients it around human-centricity, where AI and IoT augment people rather than replace them. For food manufacturers, that convergence is how the use of smart sensors ultimately translates into higher OEE, safer food, and resiliency.
The connected plant starts with real-time data
At its core, the fourth industrial revolution is about data collection and turning insights into actions. The Industrial Internet of Things (IIoT) makes this possible: low-cost wireless sensors on motors, conveyors, fans, and gear systems stream temperature, vibration, wear, moisture, and runtime data around the clock. On its own that data is noise; connected to enterprise systems and analytics, it becomes an early-warning signal that flags a drift toward a defect, breakdown, or safety risk long before a human inspection would.Catching quality and efficiency issues in the moment
Real-time data only delivers value when it reaches the process operators in usable form. Too often, production, quality, inventory, and maintenance data sits trapped in unconnected equipment, departmental applications, or paper records. In many cases people are overwhelmed by high volumes of hard-to-interpret, isolated data sets, making it impossible to see how data correlates, and what is needed to adjust food processing. Another issue is that reports are often after the fact, so that it’s not actionable.This is where a modern data fabric and machine learning come into play. By combining many sources of data, AI can deliver prescriptive insights that guide operators in real time to prevent food safety issues, reduce waste, and increase throughput. Machine learning does what people cannot: it analyzes dozens of interacting variables at once. Where teams once pored over a handful of parameters in spreadsheets after the fact, today's models can evaluate a much larger number of contributing factors collected with a higher frequency while production runs, enabling continuous adjustment and sustained quality and yield.
Predictive maintenance: the engine behind higher OEE
Keeping mission-critical equipment running is essential for productivity and meeting customer demand. Running equipment to failure and fixing it one emergency at a time is costly and disruptive, as a single breakdown of a small part can halt an entire production line. Modern Asset Performance Management (APM) offers a smarter path: by harnessing IIoT data, AI-driven analytics predicts why and when an asset will fail and schedules the fix at the right moment, fitting work into planned downtime instead of reacting to breakdowns. According to McKinsey research, predictive maintenance can reduce machine downtime by 30% to 50% while extending machine life by 20% to 40%.The payoff shows up directly in Overall Equipment Effectiveness (OEE), the product of availability, performance, and quality. Predictive maintenance lifts availability by preventing unplanned stoppages, protects performance by keeping assets at their designed speed, and safeguards quality. Higher OEE is not an abstraction; it is more sellable product from the same line, at lower cost.
From insight to action: agentic AI on the line
The next leap is agentic AI that adds execution to AI recommendations. Fed by a continuous stream of IIoT data, agents reason across context, act across systems, and operate semi- or fully autonomously within governed business constraints. For the plant that means two things at once: sharper decision support for people on the floor, and automated execution of the routine orchestration that used to slow them down.Picture an IIoT sensor on a conveyor belt motor. As bearings wear, the sensor detects rising vibration and temperature and the AI flags the abnormal pattern and alerts operators and technicians long before the motor fails. But it doesn't stop at the alert: an AI agent automatically opens a work order for preventive replacement, then checks the technician skills the job needs, confirms the replacement motor and spare parts are in stock, and slots the work into an upcoming planned-downtime window. By the time a technician picks up the task, the AI agent has everything thought out for them – the parts, skills, working instructions, and schedule are lined up so the swap happens fast, with no surprise stoppage and no compromise to food safety.
Crucially, the human stays in the middle. This is where Industry 4.0 hands off to Industry 5.0 with a shift from pure automation toward human-centric, resilient operations. The agent handles the orchestration drudgery, but operators and technicians keep judgment, approval, and the hands-on craft. On safety-critical calls, that human-in-the-loop oversight is a feature, not a hedge: the agent accelerates the response while a person stays accountable for the decision.
This is the Agentic Enterprise in practice. Industry-specific AI agents and people operating as one coordinated system. For food manufacturers, that is not a future architecture to plan for. It is available today.
Bringing it together: Treon, AWS, and Infor
Realizing this vision requires a connected technology stack, not a single product. Treon, a global provider of industrial AI solutions, supplies the predictive maintenance layer. AWS provides the secure, scalable cloud and AI infrastructure to ingest and analyze high-volume sensor data. Infor brings the industry-specific ERP, asset, workflow, and agentic AI capabilities that convert signals into governed business action. For food manufacturers, that means predictive maintenance insights do not stop at detection, but they flow directly into the systems where work is approved, scheduled, assigned, and completed.More specifically, the Treon Flow solution combines wireless vibration and temperature sensors, gateways, AI analytics, a workflow manager, and a technician-focused mobile companion app into a secure, cloud-native solution that is easy to deploy. Post installation, Treon Industrial Node C can then continuously monitor asset health. Its advanced AI-based anomaly detection algorithm identifies emerging faults early, while Treon’s AI-native diagnostics agent analyzes the data and submits events to Infor’s Industry Cloud Platform. There, an organizer AI agent receives the event and creates a work order within Infor CloudSuite solutions such as the ERP. Following approval, the work order is scheduled and assigned to ensure timely repair. Finally, a mobile app assists the technician in the field by listing the required parts, navigating to the correct asset, and providing step-by-step repair instructions.
Treon Flow is an ideal fit for industrial motors, conveyors, fans, and gear systems across food and beverage plants. It is available in AWS Marketplace for fast, secure deployment.
The bottom line
Industry 5.0 and the Agentic Enterprise are no longer a future ambition for food manufacturers, it is the operating model for staying competitive while keeping food safe. Real-time data and IIoT make the plant visible; AI turns that visibility into quality and efficiency; and predictive maintenance drives OEE and service levels higher. Treon and Infor provide proven building blocks with solutions powered by AWS that are ready to deploy. The manufacturers that connect them will deliver safer products, more reliably, at lower cost.“The next generation of industrial AI will be defined by collaboration. No single company can deliver its full value alone. By combining Treon's asset intelligence and AI analytics with the cloud capabilities of AWS and the operational workflows of Infor, we're creating an orchestrated solution that helps industrial companies improve reliability, efficiency, and decision-making.” - Tom Nordman, SVP, Sales & Marketing, Treon
“Food and beverage manufacturers under acute macroeconomic pressure will benefit greatly from the convergent technologies that define the fifth industrial revolution. The key industry performance indicators across quality and efficiency will push ever upwards thanks to integrations across smart sensors, business systems, and AI infrastructure. We're excited to make this possible by bringing together the unique strengths of Treon, AWS, and Infor.” - Marcel Koks, Senior Director, Industry & Solution Strategy, Infor