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Product and Process Improvement Using Mixture-Process Variable Methods and Robust Optimization Techniques

A process for producing low-fat mayonnaise was investigated with the goal of optimizing the raw material (RM) recipe and process variable (PV) levels. The investigations included (1) optimizing the recipe for discrete combinations of the PVs, (2) optimizing the recipe to be robust to variations in the PVs, (3) optimizing the PV settings to be robust to variations in the recipe, (4) accounting for a cost constraint, and (5) quantifying the uncertainty in the robust, optimal settings resulting from model estimation and model selection uncertainties. A mixture-process variable (MPV) design was constructed and run as a split-plot experiment. Separate models involving the RM proportions and PV levels were fit and used to develop combined MPV models. Squared-error loss, mean-squared error, and bootstrap methods were applied to develop optimal and robust solutions. Optimal settings of RM proportions and/or PV levels were developed that were less costly and more robust than the reference recipe and PV settings that had been tentatively identified by a nonstatistical approach.