Level: Intermediate
【Presenter】
PATRICK GIULIANO, Senior Quality Engineer, Abbott
Charles Chen, Continuous Improvement Expert, Statistics-Aided Engineering (SAE), Applied Materials
Mason Chen, High School Student, Stanford Online High School
[Presentation summary] (using automatic translation)
Cooked foods such as dumplings are usually cooked without precise process control of the cooking parameters. The purpose of this work is to customize the dumpling cooking process based on different dumpling product types. In the process of cooking dumplings, water temperature and cooking time are the most important factors that determine the degree of cooking of dumplings. Dumpling weight, dumpling type and batch size are also variables that affect the cooking process. To build a cooking time prediction model, we built a structured JMPDSD platform with special properties. The internationally recognized ISO22000 Food Safety Control and Hazard Analysis and Critical Control Points (HACCP) schema has been adopted. We compared the JMP neural fit method with the latest data mining algorithms to RSM. Results showed a greater prevalence of main effects from factors such as boiling temperature, product type, dumpling size/batch, and interaction effects constrained by the mixture used in the dumpling composition. In this design/analysis approach, JMP's robust design optimization, Monte Carlo simulations, and HACCP control limits were employed to understand and characterize the sensitivity of dumpling cooking elements to the resulting cooking time. A holistic approach showed a synergistic effect of combining models with different projective properties. Recursive partition-based AI models use classification schemes to estimate interaction effects, and classical (stepwise) regression modeling provides the ability to interpret interactions of two or more orders, including the possibility Curvature in the first quadratic term. In this whitepaper, we have described a new automated dumpling cooking process and analytical framework (using AI schema) that can improve the overall process, reduce energy costs, and reduce labor costs. This new methodology has the potential to change the way we think about business cost estimation and profit modeling in the food service industry.
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