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 92
3 Decision Models Decision models are models that can be used to understand, analyze, or facilitate making a decision 93
4 Outsourcing Model Model components F = fixed cost of inhouse manufacturing V = unit variable cost of inhouse manufacturing C = unit cost of outsourcing D = demand volume Total Manufacturing Cost = TMC = F + V * D Total outsourcing cost = TOC = C * D. 94
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 95
6 Airline Pricing Model 96
7 Model Analysis WhatIf 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 97
8 Data Tables Summarizes the impact of one or two inputs on a specified output Excel tools Oneway data tables Twoway data tables 98
9 One Way Data Table 99
10 Two Way Data Table 910
11 Scenario Manager 911
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 914
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)] 915
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, ) 916
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 917
18 Data Fitting 918
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 919
20 Modeling Example: Gasoline Consumption m = miles/day driven d = days/month f = miles/gallon Miles driven/month = md Gallons consumed/month = md/f 920
21 Modeling Example: Revenue Model Total revenue = Price * ( * Price ) = * Price * Price 921
22 Modeling Example: New Product Development 922
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. 923
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 924
25 Newsvendor Spreadsheet Model 925
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. 926
27 Newsvendor Model Monte Carlo Simulation 927
28 Fitting Probability Distributions Goodnessoffit 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 928
29 Crystal Ball Fit Distribution 929
30 Distribution Fitting Results 930
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 931
32 Example 932
33 Revised Model 933
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