It is feasible, practical, and prudent to explore new ideas, evaluate alternatives, and peek into the future using what-if analytics. Common analytics techniques focus on statistics, but business managers often need more decision-making guidance and fewer statistics. Simulation techniques help to identify, analyze, and compare various decision-making scenarios, and to evaluate a range of options by playing the what-if game.
A well-rounded analytics organization includes analysts who are skilled with simulation, and these people often become the most in-demand analysts.
Combining models, assumptions and decision variables yields insights helpful when choosing the best path into the future. Simulation models enhance understanding of key behavior patterns, leading to increased confidence and ability to define and achieve key business objectives. Implementing simulation as a core part of business analytics practice simply makes sense. Business questions starting with "why" and extending to "what if" can be answered with certainty and clarity.
This course provides an introduction to simulation analytics. Topics include definitions, general system concepts, modeling techniques, and application areas. Pragmatic examples are provided throughout the course. A framework to position simulation in the broader BI program is also provided.
