How AI Can Support OEE Optimization
Every factory owner wants to know how effectively their equipment is producing high-quality products, as quickly as possible, with minimal downtime or waste. It’s common to have between 60% and 70% overall equipment effectiveness (OEE), but if this is the case, money is being left on the table.
The June PMI reports that some manufacturers are increasing production now because they are worried about possible higher export tariffs to the U.S. But this may just be shifting production that would’ve happened later in the year earlier, which could hurt global manufacturing in the second half of 2025.
With manufacturing barely expanding and costs tightening, companies are looking to squeeze every bit of efficiency from their operations. AI-driven optimization of OEE metrics is becoming an essential lever for doing exactly that.
AI’s Impact on Availability
Manufacturers can’t always predict when sugar prices are going to increase. But in food and beverage specifically, downtime is often linked to repeatable patterns — overfilled hoppers, underlubricated machinery, temperature control variances — many of which are detectable with AI. In 2025, UK and European manufacturers will lose more than £80 billion due to downtime.
Planned downtime, such as maintenance, cleaning or changeovers, typically accounts for 20–40% of total downtime. Manufacturers who schedule and optimize this can steer closer to the 20% mark. They can analyze historical data to organize downtime during low-demand periods.
However, unplanned downtime, which may be due to unexpected breakdowns, human errors or maintenance-related issues, can make up 60-80% of total downtime in less efficient plants. Leading factories reduce this with predictive maintenance, training and AI to catch issues before they stop production lines.
By monitoring historical and real-time data from machines’ programmable logic controllers (PLCs) alongside operators' notes and machine logs, AI can identify the root causes of errors and analyze trends to detect inefficiencies. It uses this data to reduce unplanned downtime by detecting early signs of line failure — such as a lack of preventive maintenance or electrical and mechanical issues — and generating an optimized plan to avoid those failures.
Closing the Gap Between Capacity and Output
Once manufacturers have put the tools and algorithms in place to reduce unplanned stops and keep lines running, they can prioritize optimizing speed and configuring processes to hit targets efficiently. Manufacturing utilization held steady at 76.7% in 2025, 1.5 points below its historical norm. However, with AI by their side, we should be targeting above 85%.
Ramp-up tends to take 4-10 weeks across industries, with performance “gathering momentum after around five weeks.” However, it is common for ongoing instabilities to persist through the final phases.
AI can optimize line speed by interpreting the data and providing a plan to reduce ramp-up and stabilization time for the production line. For example, if a manufacturer switches from boneless chicken to chicken cutlets, AI can help set all the variables faster, instead of spending an hour or more making manual adjustments to stabilize the line.
Since AI has been maturing, manufacturers who know their actual line capacity today are in the minority. Due to a lack of data and poor visibility, it is common to underestimate targets. Once manufacturers gain full visibility, they can set higher production targets and push their teams to improve OEE. For example, they can expect the waste target to be reduced from 5% by operational performance to 1.5%.
First-Time-Right Production
The worst thing for a manufacturer is to spend hours massaging or “milking” caviar eggs, only to oversalt them in the preservation stage and ruin the entire line.
AI can fundamentally shift the equation. By monitoring key indicators such as temperature, exposure time and batch timing, AI can detect deviations faster than manual checks ever could. Instead of alerting operators after the mistake, it helps prevent it in real time, optimizing both preservation plans and operator decisions to ensure the entire OEE sequence flows smoothly from preparation to packaging.
This level of oversight is especially critical during product transformation — the phase where quality assurance (QA) specialists face the most complexity and risk. Whether it’s cutting, mixing, thermal processing or formulation, even small changes must be tested, verified and tightly controlled to protect product integrity and avoid costly rework.
To mitigate these risks, manufacturers can deploy tools such as process variable monitoring, tracking line speed, temperature, dwell time and energy consumption, and detect early signs of deviation. Working in tandem with agents, predictive models and control plans, these systems help operators identify quality risks before a full batch is committed. The agents recommend different action plans to ensure optimal targets.
No matter how advanced predictive tools are, data governance and product inspections remain vital to ensure outcomes consistently meet standards. Manufacturers that closely align with their AI provider’s instrumentation and training teams are far more likely to integrate tools effectively and ensure long-term performance on the plant floor.
AI excels in turning once-reactive operations into proactive, precision-driven systems. It detects early signs of unplanned downtime and streamlines changeovers, continuously adjusts to live process variables, guides operators through complex transitions and flags deviations before defects occur. When fully integrated with operator workflows and QA protocols, AI becomes a co-pilot, ensuring every batch runs closer to standard, with less waste, fewer delays and better consistency.
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