“Systems rarely perform exactly as predicted” was the starting line for the blog Predicting Process Variability and is the driving force behind most improvement projects. As stated, variability is inherent in all processes whether these processes are concerned with manufacturing a product within a plant, producing product via an entire supply chain complex or providing a service in the retail, banking, entertainment or hospital environment. If one could predict or eliminate the variability of a process or product, then there would be no waste (or Muda in the Lean World which will discussed in a third part) associated with a process, no overtime to finish an order, no lost sales owing to having the wrong inventory or lengthy lead-times, no deaths owing to errors in health care, shorter lead times, etc. which ultimately leads to reduced costs. For any organization (manufacturing or service), reducing costs, lead-times, etc. is or should be a priority in order to compete in the global world. Reducing, controlling and/or eliminating the variability in a process is key in minimizing costs.
Systems where it is too expensive or risky to do live tests. Simulation provides an inexpensive, risk-free way to test changes ranging from a "simple" revision to an existing production line to emulation of a new control system or redesign of an entire supply chain.
Large or complex systems for which change is being considered. A "best guess" is usually a poor substitute for an objective analysis. Simulation can accurately predict their behavior under changed conditions and reduce the risk of making a poor decision.
Systems where predicting process variability is important. A spreadsheet analysis cannot capture the dynamic aspects of a system, aspects which can have a major impact on system performance. Simulation can help you understand how various components interact with each other and how they affect overall system performance.
Systems where you have incomplete data. Simulation cannot invent data where it does not exist, but simulation does well at determining sensitivity to unknowns. A high-level model can help you explore alternatives. A more detailed model can help you identify the most important missing data.
Systems where you need to communicate ideas. Development of a simulation helps participants better understand the system. Modern 3D animation and other tools promote communication and understanding across a wide audience.