System dynamics modeling of complex processes such as product development, manufacturing, and service, is an efficient approach for assessing value potential of different business transformation alternatives at small and large enterprises. Process elements such as generation of concepts, detailed designs, pilot level plant trials, etc. can be modeled including first-pass work, testing and review, rework identification and defect fixing, along with release readiness, staffing, schedule pressures, overtime and many other business metrics.Enterprise level processes, with their complex logic loops, can be represented as a system of coupled nonlinear differential equations, whose solutions can reveal the intricate underlying dynamics. Design of experiments, performed on the system dynamics models representing the business processes, are an inexpensive way of gaining insights into the impact of interactions between the numerous process control variables. Monte Carlo simulations performed on response surfaces are an effective solution for assessing process robustness given the variations in the process control variables. In this paper, the authors present a brief demonstration of using design of experiments and Monte Carlo simulations on response surfaces applied to a system dynamics model representing a typical product development process.