Simulation, i.e., experimentation with a computer model of a real system in order to determine the effect of changes in the system, is the most widely used management science method in practice. The use of simulation in business is increasing as the cost of modeling and running experiments with computers has been drastically reduced.
It is particularly useful when the actual business process is complex and characterized by uncertainty in demand, processing capacity and delivery times, future interest and currency rates, etc.
Simulation allows us to capture dynamics and uncertainty in these systems as they evolve over time. It is also possible to model more details of a real system when compared to an analytical or optimization-based model. Experiments are then conducted at a fraction of time and expenses of similar experiments in real systems. A sensitivity analysis of different "what-if" scenarios allows us to evaluate risk of proposed changes to the modeled system as well as forecast outcomes of business decisions before they are implemented in real-world.
Simulation is alternative to some project management methods and traditional analytical tools of operations research to analyze problems related to queuing theory, inventory theory, etc. It also represents a predictive modeling tool for robust planning and risk reduction in many systems. It is widely applied for resource capacity planning, finding and removal of bottlenecks in complex systems, evaluation of effectiveness of current and improved systems, alternative capital investment decisions and analysis of cash flows of businesses.
The course focuses mainly on two methodologies of computer-based simulation: Monte Carlo method and discrete-event simulation. The course participants will learn to model various business processes from the input data collection to interpretation and analysis of the results. The simulation modeling software JaamSim and excel, and Python will be used to model and analyze different systems.
Throughout the course, students will learn different examples of simulation applications from a wide range of systems: banking (e.g., consumer credits), healthcare, logistics, inventory and manufacturing (e.g. capacity planning), telecommunications (e.g. call and support centers), retail and service (e.g. airport processes), etc.