FEATURE Smart factories/software OPTIMISE PRODUCTION WITH REAL-TIME DATA


Tom Fairbairn, distinguished engineer, Solace, says while AI has exposed fractures in manufacturing’s digital backbone, event-driven integration will provide an infrastructure whereby data availability keeps pace with production needs


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anufacturing has always been an industry that operates on tight margins, where milliseconds count and downtime can result in


significant financial losses. But the rise of intelligent systems - from predictive maintenance algorithms to adaptive cobots - has transformed those margins from tight to almost unsustainably thin. Not because AI fails, but because the infrastructure beneath it was never built for this moment. For decades, manufacturers stitched together their operations with point-to-point integrations, batch processing, and APIs designed for yesterday’s workflows. These worked when production schedules were predictable and quality checks happened at the end of the shift. AI, though, doesn’t wait for shift changes. A vibration sensor on a CNC spindle can trigger an alert the instant performance drifts. Vision systems can flag a micro-crack in a component before it reaches the next station. Supply chain models can spot a shortage early enough to reroute parts automatically. The response window for these instances isn’t measured in hours anymore, but in milliseconds. And milliseconds are where most manufacturing operations fall apart. Walk any modern factory floor and you’ll see the promise of AI enabled cyber-physical systems in industry. Digital twins now model entire production lines, and predictive analytics optimising everything from chemical reaction times to HVAC schedules. Yet beneath this intelligent surface, critical data often moves through architectures built for the age of clipboards. Consider what happens when an AI model


predicts equipment failure. The sensor data flows through one system. The maintenance schedule lives in another. Inventory for replacement parts sits in a third. By the time these systems synchronise – if they synchronise at all – the prediction becomes a post-mortem.


At this stage, manufacturing needs a fundamental, architectural shift. Traditional integration assumes data can wait: for the next API call, for tonight’s batch job, for Monday’s report. Modern manufacturing generates events, not entries. Every temperature spike, every torque measurement, every quality inspection is a signal that could trigger cascading actions across the entire operation.


AI amplifies this need. Collaborative robots automationmagazine.co.uk


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learning from human operators don’t pause for data synchronisation. Generative design algorithms creating lightweight aerospace components need real- time constraints from materials, cost, and production systems simultaneously. Digital twins simulating production adjustments require continuous feeds from their physical counterparts in order to be of any use at all.


What manufacturing needs is event-driven integration - an approach where every significant occurrence publishes instantly to all relevant systems through a decentralised network. Instead of pulling data on schedule, systems subscribe to the world as it happens.


Integration is less plumbing now, and more like neural pathways. When demand forecasting algorithms spot market shifts, production planning adjusts in real-time. When energy management AI identifies waste patterns, equipment schedules optimise automatically. These are just two of a plethora of instantaneous adjustments that occur daily throughout a manufacturing process. The aim? Manufacturing operations that sense and


respond like living organisms rather than mechanical processes. Early adopters are already reaping the benefits. The promise of streamlining AI in manufacturing, with zero unplanned downtime, perfect quality, and optimal efficiency is closer than ever. But that promise depends entirely on infrastructure that can keep pace, and not stand as a limiting factor to future AI innovation.


For manufacturing leaders, the mandate is clear:


stop treating integration as a back-office IT concern and recognise it as the strategic capability that will define who benefits from the age of industrial AI. In a world where predictive maintenance can prevent million-dollar failures and supply chain AI can navigate global disruptions, the companies that thrive won’t be those with the smartest algorithms. They’ll be those whose data moves in real-time.


Solace solace.com


SNAP Signal Data from legacy Machines


* More than 20,000 sensors.


* Factory Automation. * Process Automation. * Hazardous Areas. * Harsh Environments. * Measurement & Inspection. * LED Lighting. * Vision Systems. * Machine Safety. * Vehicle Detection. * Flow & Temperature.


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Automation | October 2025 17


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