Simulation A food manufacturer is starting a new process which will take a raw piece of meat send it down a conveyor to one of two breader/batter machine and then all product will be regrouped together and sent through a single oven. Your job is to predict how much of the product in a 10000-piece batch will be below 160 degrees Assuming incoming product will weigh on average 250grams with a standard deviation of 25grams and is normally distributed. Assuming product is randomly assigned to one of two breader/batter machines (50% to each machine) Assume pickup tests accurately represent the weight pickup of each breader/bather process Assuming cooking tests accurately represent the final cook temperature at a given weight Assume the ovens and breader/bather machines are on “max” setting and cannot be adjusted Assume the given regression lines were accurately built. Build a DSS system that allows the initial product weight average to be changed to see the effect on the amount of undercooked ( Below 160 degree) product The regression line to predict the product that has gone through brader/bather machine 1: Endweight=1.247*Startweight-33.01 with a standard error of 4.415 The regression line to predict the product that has gone through breader/bather machine2: Endweight =1,393*Startweight+5.316 with a standard error of 5.427 Regression line to predict cook temperature based on incoming product weight: Temp=0.1216*Endweight+209.6 with a standard error of 5.658 (the negative sign is highlighted because it can be
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