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.
You Will Learn
- Key capabilities of simulation
- Categories of simulation models
- Domains of applicability
- How to build and implement simulation models
- Data management requirements for simulation
- How business problems can be defined and solved
- The role of experimental design
- How insights can be generated
- How to explore and discover routes to successful outcomes
- How analytics, simulation, and BI are interconnected disciplines
Business analytics leaders, BI program leaders; BI architects and project managers; business analytics team members; business managers and decision makers; functional analysts; operations managers; process improvement specialists