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Cooked foods such as dumplings are typically prepared without precise process control on cooking parameters. The objective of this work is to customize the cooking process for dumplings based on various dumpling product types. During the cooking process in dumpling preparation, the temperature of the water and time duration of cooking are the most important factors in determining the degree to which dumplings are cooked (doneness). Dumpling weight, dumpling type, and batch size are also variables that impact the cooking process. We built a structured JMP DSD platform with special properties to build a predictive model on cooking duration. Internationally recognized ISO 22000 Food Safety Management and the Hazard Analysis Critical Control Point (HACCP) schemas were adopted. JMP Neural Fit techniques using modern data mining algorithms were compared to RSM. Results demonstrated the prevalence of larger main effects from factors such as: boiling temperature, product type, dumpling size/batch as well as interaction effects that were constrained by the mixture used in the dumpling composition. JMP Robust Design Optimization, Monte Carlo Simulation and HACCP Control Limits were employed in this design/analysis approach to understand and characterize the sensitivity of dumpling cooking factors on the resulting cooking duration. The holistic approach showed the synergistic benefit of combining models with different projective properties, where recursive partition-based AI models estimate interaction effects using classification schema and classical (Stepwise) regression modeling provides the capability to interpret interactions of 2nd order, and higher, including potential curvature in quadratic terms. This paper has demonstrated a novel automated dumpling cooking process and analysis framework which may improve process throughout, lower the cost of energy, and reduce the cost of labor (using AI schema). This novel methodology has the potential to reshape thinking on business cost estimation and profit modeling in the food-service industry.