In present day economic conditions, plastic plays an ever increasing role. The need for plastics to achieve more requirements has led to an ongoing increase of the number of plastic materials available. Plastic molds are built to eliminate as many secondary operations as possible. The process creates an economic part. Injection Molding is the preferred process by which the parts are produced. Typically a process engineer would spend several hours standing at the injection molding machine using experiential background to estimate various parameter combinations that might lead to a good part. As costs increase and time-to-market shortens, there is a need to provide a more predictable timeline for introducing new product. Computer Aided Engineering (CAE) and, specifically, Moldflow MPI, have demonstrated the ability to merge science with operator experience in order to achieve a higher level of predictability prior to introducing new product. Moldflow MPI is software developed for the injection molding process. This study was conducted to determine if predicted part weight from MPI could be used to produce a new product, given the assumptions, limitations, and methodology are followed. In other words, the study was designed to ascertain if the predicted part weight from MPI matches the part weight from an actual injection molding machine in a manufacturing environment. The Moldflow process was set up using Moldflow recommendations for DOE flow, and Taguchi plus factorial conditions. Using part weight, response surface methodology curves were viewed for the three key parameters (melt temperature, mold temperature, and pack time) to predict part weight. The selected parameters for mold temperature, melt temperature, and pack time were entered into the injection molding machine. After stabilization, 400 shots were run on the injection molding machine. One hundred shots were collected at random. The one hundred shots were weighed on a four place decimal gram scale and recorded. The data were entered into JMP statistical software and analyzed for distribution and a p value of > .05. Findings indicated a p value of 0; thus the hypothesis was rejected. The predicted parameters of mold temperature, melt temperature, and pack time cannot be used to predict actual part weight.