Analytical modeling of business decisions; uncertainty, risk and expected utility; regression modeling to infer relationships among variables.
The process of making good decisions is critical for business analysts and managers because they regularly encounter new problems in an environment characterized by risk and uncertainty. A good decision is not the same as a good outcome, which can sometimes be a matter of luck (uncertainty). Conversely, a bad outcome is not necessarily proof of a bad decision.
In this course, we will explore basic analytical principles that can guide an analyst or manager in making complex decisions. A good decision uses sound reasoning and considers all of the relevant information that is available at the time the decision is to be made.
The analytical areas to be covered in this class include:
Time Series Analysis & Forecasting
Optimization
Monte Carlo Simulation
Data Mining
The learning goals for the course, with respect to the specific analytical areas covered, are that each student be able to:
Transform a seemingly complex business decision problem into an underlying analytical structure
Understand the role of uncertainty and risk in the decision-making process
Manage and analyze available data to understand relationships among variables and create predictions or decisions
Understand the trade-offs involved in a decision
Use available computing technology (e.g., spreadsheets) to arrive at actionable solutions
More specifically, be able to use regression and exponential smoothing tools to create times series and other forecasting and demand planning solutions, build and interpret business optimization and simulation models in areas such as operations, finance and marketing, and begin to use and understand the use of data mining tools
Lastly, to focus on applications, examples, and homework problems relevant to management of business Supply Chains.