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Unilever Optimizes Hellmann’s Mayo Fill Levels with AI-based Predictive Analytics

Live from Automation Fair: Unilever addressed mayonnaise fill level inconsistencies using Rockwell Automation's FactoryTalk LogixAI, enhancing efficiency and reducing overfill giveaway through predictive analytics.

For food producing brands, maintaining precision in fill levels is crucial for both product quality and cost efficiency. On its 320 to 350 jar/min Hellmann’s mayonnaise packaging line, Unilever’s Pouso Alegre food and nutrition facility outside of Sao Paulo, Brazil, faced challenges with its traditional PID (proportional integral derivative) loop system, which relied on feedback from downstream checkweighers to adjust fill levels. This reactive approach sometimes led to overfilling, increasing material costs and reducing overall efficiency, according to Jean Ramos, digital factory coordinator, and Denis Castro de Almeida, digital factory coordinator LATAM, both of Unilever.

The ideal weight was 500 g, but the scrap window was already narrow—499 g for underfills, and 508 g for overfill scrap. How could Unilever tighten the standard deviation and move the average fill closer to the 500-g ideal weight, without losing scrap to underfill? Ideal weight was 500 g, but 499-g underfills were scrap, as were overfills over 508 g. Before and after bell curves show a tighter grouping of fills much closer to the ideal 500-g jar without going under, greatly reducing giveaway and scrap.Ideal weight was 500 g, but 499-g underfills were scrap, as were overfills over 508 g. Before and after bell curves show a tighter grouping of fills much closer to the ideal 500-g jar without going under, greatly reducing giveaway and scrap.

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