Using AI to Optimize High-Impact Machines on Connected Lines

In the food, beverage, and CPG industries, production lines are high-velocity, high-stakes environments. Margins are tight, product lifecycles are short, and line interruptions can ripple across supply chains. Optimizing throughput isn’t just operationally smart, it’s essential to being competitive.

AI is emerging as a strategic asset in this space, enabling manufacturers to identify and focus on the highest-impact machines on a line — the ones whose performance most directly affects throughput — and target improvement activities where they’ll yield the greatest ROI.

From OEE to Opportunity: Where Traditional Metrics Fall Short

Overall Equipment Effectiveness (OEE) remains a core KPI in manufacturing, but on its own, it often fails to identify where to act first. In a typical connected line comprised of multiple unit operations, not all machines are equal in their influence on throughput.

AI steps in and sifts through operational data to determine which machine faults, speed losses, or which micro-stops have the most impact. For example, a modestly performing capping machine may be tolerable on paper, until AI reveals its stoppages lead to frequent line-wide halts due to upstream buffer overflows and downstream starvation. On the other hand, a poorly performing packing machine may have minimal impact as its surge speed compensates for its stoppages using accumulation as a buffer.

Clean Data: The Real Bottleneck

AI’s effectiveness in food, beverage, and CPG environments depends entirely on the quality of operational data. Most OEE/MES systems do not provide sufficient data fidelity for AI consumption. Either the data is focused entirely on the bottleneck machine, or the unit op data lacks either the delineation of states (blocked, starved, failed, etc.) or the necessary fidelity for AI to make prescriptive or predictive models.

Without standardized, high-fidelity data, AI models can’t reliably identify patterns or root causes. Investing in robust data collection and cleansing is step zero.

Focused Improvement = Faster Gains

Once AI identifies the most impactful machines, teams can shift from broad, overarching, improvements to laser-focused interventions. This switch might include:

• Automating manual inspection steps prone to inconsistency
• Streamlining CIP and changeover procedures
• Optimizing center lining and CIL (Clean, Inspect, Lubrication) procedures
• Rate rebalancing to reduce surging/starving patterns on lines

The key is to do less, better — fixing the right things instead of everything.

Real-World Application: Focusing on the True Bottleneck

One beverage manufacturer used AI to analyze 12 months of line data across 8 SKUs. While their filler appeared to be the top downtime source, AI revealed that the labeler, which was prone to micro-stops, was the real constraint due to its position in the line and the lack of accumulation. By investing in a faster label roll-change process and better tension control, they increased throughput by 6% without adding new equipment.

Real-World Application: Line Rate Balancing

In another case, a beverage manufacturer used AI to analyze machine rates to optimize their V-curve. In line flow, a V-curve represents the rate capacities of machines with the lowest or bottleneck being the center or bottom of the V. Typically, a filling machine is considered the center of the V-curve. AI determined that the true bottleneck was a close coupled pair of machines downstream of the filler. It also predicted that by increasing the rate of the filler by 10%, the line output would increase by 7%. After rebalancing the rates and conveyor controls, the line achieved a net increase of 7.5% throughput.

Limitations of Traditional Analytics

Traditional analytics will always serve a valuable role in production optimization. They are key elements of an effective lean, six-sigma program as a day-to-day tool but there are key limitations of these tools and programs.

• OEE measures, AI diagnoses
  OEE tells you what happened. AI analyzes machine performance at a deeper more granular level to diagnose root causes, anomalies, and target interventions.
• OEE treats all losses equally, AI prioritizes by impact
  OEE provides a score but doesn’t factor for line flow and machine interactions. AI ranks machines and loss events by their true impact on throughput, so improvement efforts focus on what matters most.
• OEE is static, AI is adaptive
  OEE is based on fixed thresholds that don’t factor for change. AI systems continuously learn and update from new data, adapting to SKU changes, seasonal demand, or equipment aging.
• OEE is descriptive, AI is predictive and prescriptive
  OEE is backward-looking, describing what happened and is entirely dependent on human interpretation. AI is forward-looking: it predicts potential problems, forecasts line performance, and prescribes optimal actions.

Why It Matters Now

With demand variability, labor constraints, and rising input costs, manufacturing companies are under consistently increasing pressure to do more with less. AI offers a path to intelligent prioritization helping operations leaders focus not just on what’s broken, but on what’s hindering growth.

Final Word

In these fast-moving industries, every second counts. AI transforms production data into a roadmap — highlighting high-impact machines, clarifying where to invest, and accelerating improvements. But it all starts with one thing: clean, reliable data. Get that right, and AI can help turn complexity into clarity, and throughput into competitive advantage.

For more information, contact SmartSights to learn more on how you can go from Good to Great Data.

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