Case Study: Predictive Quality for Peanut Butter Manufacturer
A peanut butter manufacturer faced a challenge in the form of delayed quality feedback – the final product consistency (set) could only be tested 2-3 days after production, which resulted in costly rework and waste. INS3 used AI-based quality predictive models to make real-time changes before defects occurred.
The Challenge
Delayed Quality Feedback: The peanut butter’s final consistency (“set”) could only be tested 2–3 days after filling, leading to late-stage quality failures.
High Rework & Waste: If the product did not set properly, entire batches had to be reprocessed or discarded.
Process Variability: Factors such as heat application, stabilizer levels, and material rework affected the final product quality, making it difficult to maintain consistency.
Solution
AI-Driven Predictive Quality: Machine learning models analyzed production parameters in real time to predict final product consistency before completion.
Process Optimization: AI recommendations allowed operators to adjust parameters such as heat, stabilizers, and rework inputs to ensure proper set formation.
Integrated Data Visibility: INS3 connected MES, SCADA, and lab test data to provide a unified view of quality trends and process deviations.
Results
Reduction in Waste: Improved predictive accuracy to minimize rework and disposal.
Enhanced Quality Control: Ability to adjust processes in real time, reducing product inconsistencies.
Operational Efficiency Gains: Faster identification of potential issues before production completion.
INS3’s predictive quality solution enabled proactive quality management, reducing waste and improving efficiency, with ongoing refinements to maximize ROI.
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