# Building and Using Spreadsheet Decision Models

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1 Chapter 9 Building and Using Spreadsheet Decision Models

2 Models A model is an abstraction or representation of a real system, idea, or object. Models could be pictures, spreadsheets, or mathematical relationships Models contain data, uncontrollable variables, and decision variables 9-2

3 Decision Models Decision models are models that can be used to understand, analyze, or facilitate making a decision 9-3

4 Outsourcing Model Model components F = fixed cost of in-house manufacturing V = unit variable cost of in-house manufacturing C = unit cost of outsourcing D = demand volume Total Manufacturing Cost = TMC = F + V * D Total outsourcing cost = TOC = C * D. 9-4

5 Types of Decision Models Descriptive - describe relationships and provide information for evaluation Prescriptive (optimization models) - determine an optimal policy, that is, the best course of action that a decision maker should take to maximize or minimize some objective 9-5

6 Airline Pricing Model 9-6

7 Model Analysis What-If Analysis evaluate how specific combinations of model inputs that reflect key model assumptions affect model outputs (often called sensitivity analysis). Excel tools Data tables Scenario manager Goal seek 9-7

8 Data Tables Summarizes the impact of one or two inputs on a specified output Excel tools One-way data tables Two-way data tables 9-8

9 One Way Data Table 9-9

10 Two Way Data Table 9-10

11 Scenario Manager 9-11

12 Goal Seek Find the value of an input that produces a known result within a spreadsheet Example: find the breakeven point in the outsourcing decision model Set cell is B18; To value = 0; By changing cell is B

13 Optimization Models: Excel Solver Find the unit price that maximizes profit Solution: Price = \$428.57; profit = \$115,

14 Tools for Model Building Logic and business principles Common mathematical functions Data fitting Spreadsheet engineering 9-14

15 Logic and Business Principles Profit = Revenue - Cost Revenue = (Unit price)(quantity sold) Cost = Fixed cost + Unit cost*quantity produced Quantity sold = Min(Quantity produced, Demand) Profit = (Unit price)min(quantity produced, Demand) [Fixed cost + (Unit cost)(quantity produced)] 9-15

16 Net Present Value Measures the worth of a stream of cash flows, taking into account the time value of money. A cash flow of F dollars t time periods in the future is worth F (1 + i) t dollars today, where i is the discount rate. Excel function NPV(rate, value1, value2, ) 9-16

17 Common Mathematical Functions Linear: y = mx + b Logarithmic: y = ln(x) Polynomial: y = ax 2 + bx + c (quadratic) Power: y = ax b Exponential: y = ab x 9-17

18 Data Fitting 9-18

19 Spreadsheet Engineering Improve the design and format of the spreadsheet itself. Improve the process used to develop a spreadsheet. Inspect your results carefully and use appropriate tools available in Excel. Use the Data Validation tool Inspect and audit formulas 9-19

20 Modeling Example: Gasoline Consumption m = miles/day driven d = days/month f = miles/gallon Miles driven/month = md Gallons consumed/month = md/f 9-20

21 Modeling Example: Revenue Model Total revenue = Price * ( * Price ) = * Price * Price 9-21

22 Modeling Example: New Product Development 9-22

23 Models Involving Uncertainty Uncontrollable inputs often exhibit random behavior, which must be incorporated into models Specify probability distributions for the appropriate uncontrollable inputs. Example: assume that demand is normally distributed with a mean of 50,000 and a standard deviation of 10,000 units. Models that include randomness are called probabilistic, or stochastic, models. 9-23

24 Newsvendor Model C = purchase cost R = sale price S = salvage value D = demand during a single period Q = quantity purchased Net profit = R * Quantity Sold + S * Surplus Quantity -C* Q 9-24

26 Monte Carlo Simulation The process of generating random values for uncertain inputs in a model, computing the output variables of interest, and repeating this process for many trials in order to understand the distribution of the output results. 9-26

27 Newsvendor Model Monte Carlo Simulation 9-27

28 Fitting Probability Distributions Goodness-of-fit tests H 0 : the sample data come from a specified distribution 1e.g., normal2 H 1 : the sample data do not come from the specified distribution 9-28

29 Crystal Ball Fit Distribution 9-29

30 Distribution Fitting Results 9-30

31 Model Assumptions All models reflect assumptions used by the modeler. Assumptions simplify models and make them easier to manipulate and solve Assumptions should be as realistic as necessary to make models useful but not overly complex Assumptions should be clearly stated and documented 9-31

32 Example 9-32

33 Revised Model 9-33

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