Competing with Analytics
A 3-day workshop at the ING-Ivey center in Toronto.
Offered by:
Professors Peter C. Bell and Gregory S. Zaric
Richard Ivey School of Business
December 15 - 17, 2008
9:00am - 5:00pm
Target Audience: Who should take this course?
The course is designed for managers who wish to improve their analytical skills in order to improve their decision-making. After taking the course, you should be able to analyse complex decision situations in order to quantify the risks and rewards associated with the various alternatives.
About the instructors
Peter C. Bell has been teaching Analytics at the Richard Ivey School of Business since 1977. He was recently awarded the INFORMS Prize for teaching the Application of OR/MS. Further details are on his www-site: http://www.ivey.uwo.ca/faculty/Peter_Bell
Gregory S. Zaric has been teaching Analytics at Ivey since 1999 and is the winner of several prestigious teaching awards. Further details are available here: http://www.ivey.uwo.ca/faculty/Greg_Zaric.html
Preliminary Course Outline
December 15:
Basics of Analysis and Model Building in Excel
Building a cash flow model.
Graphs and tables.
“What if?” analysis.
Trend lines and regression with Excel
Introduction to uncertainty: probabilities, distributions, means and measures of variation, forecast errors.
Using regression models for prediction.
Forecasting
Using past annual or monthly data to develop forecasts of the future
Identifying and using trends and seasonality in making a forecast.
Using Excel functions and graphs
Using the Excel Data Analysis Toolpack
December 16:
Excel modelling I
Good model building practice.
Advanced “What if?” analysis: 2 way sensitivity analysis, threshold analysis.
Conditional formatting.
Multiple-variable regression model building and use in Excel.
Decision making
Alternatives, criteria, payoffs, and risk.
Measuring risk and return: risk/return tradeoffs.
Risk profiles.
The many uses for random numbers [the =RAND() function] in Excel
Sorting and randomizing.
Random sampling.
Estimating the probability of complex events.
Event simulation.
December 17:
Excel modelling II
Making and documenting assumptions.
Using sensitivity analysis to test the robustness of assumptions.
Risk analysis and simulation
Building and interpreting an event simulation model in Excel.
Analysing derivatives.
Simultaneous decision problems
Using the Excel Solver add-in.
Optimizing routing and supply chain decisions.
Scientific pricing
Pricing concepts: flexible, variable and dynamic pricing.
Computing optimum prices with Excel.
Dynamic pricing strategies.