MGS3100 Spreadsheet Modeling Chapter 2 Slides 2a: Introduction

Models and Data Useful (quantitative) models are developed based on relevant data (numbers); models without data are at best theoretical abstractions Data are often collected according to the requirements of models –time series vs. cross-sectional –aggregated vs. disaggregated

Numbers in Models Count Measure Rank Results Constant Variable Coefficient Precision

Terminology and Relationships Price Volume –Sales volume –Production volume Demand Revenue Profit Cost –Overhead cost –Sunk cost –Fixed cost –Variable cost –Total cost Breakeven point Crossover point

Basic Deterministic Models Profit Model –Profit = Revenue - Total Cost –Profit = Price*Units - (FC + VC*units) Break-even point –Profit = 0, or Revenue = Total Cost –Units = Fixed Cost/(Price - Var. Cost) Crossover Point –Total Cost a = Total Cost b –Units = (FC a - FC b )/(VC b - VC a )

Model Validation: Dimensional analysis Multiplication : Apples x Apples = Apples 2 Division: Apples/Days = Apples/Days Cancellation: (Apples/Day) x Days= Apples Dissimilar addition: Apples + Oranges = Apples + Oranges Similar addition: Apples + Apples = Apples Congruity: Apples = Apples Incongruity: Apples/Oranges 2  Oranges

Spreadsheet Modeling Inputs should be logically grouped Primary outputs should be easy to read Input and output data should be labeled Don’t embed parameters in a formula: use cell references Use range names Use fonts and color but don’t overuse them

Sensitivity Analysis The business world is a dynamic environment - stock market, interest rates, currency, oil prices, politicians! How do changes in constants affect the model solution? Look to both sides before you cross the business street! (upper and lower bounds)

What-if Analysis Decision variables are under the control of the manager Testing different values will give the decision maker a sense of how they will affect the outcome What happens if this value is changed?

Graphical and Numerical Methods Generate a range of possible changes Input changes into the model and examine the outcome If there are too many variables, use graphs

What Next? You have analyzed the process You have determined the factors important to your situation You have examined the relationships You have represented the model in a usable form You have checked for the sensitivity of the model

Model Validation Logical test –Are the assumptions realistic? –Do the model results reflect reality? –Obtain test data and “test drive” the model Split the data (holdout sample) –Make model using one part –Test the validity using the other data set

Intelligent Spreadsheet Use Organization Work smart, not hard Design Appearance

Organization Home screen should identify and describe the subject model Inputs should be logically grouped Primary outputs should be easy to read Use tabs (new sheets) to group work Model should be well documented

Work Smart, Not Hard Never embed a parameter in a formula - always use a named cell (range) Name a cells (ranges) that are referenced in other formulas Use absolute references to avoid problems when moving cells Use range names that anyone will understand

Design Clean and professional in appearance Easy to use and understand Keep a steady flow in your model It must be accurate!

Appearance Use a spell checker! Don’t overuse fonts and color Don’t try to put too much on a page