Chapter 1: Introduction to Managerial Decision Modeling

Out Line Quantitative Approach meaning Management Science Decision Modeling Some Basic Definition Types of decision modeling Steps in decision modeling Problems in decision modeling

Mgt Science Is a systematic approach to Decision making. Makes extensive use of Math/Stat models Quantitative Approach to DM is also referred to as QA Operation research Decision modeling

What is Decision Modeling? A scientific approach to managerial decision making The development of a (mathematical) model of a real-world scenario Generally mathematical representation The model provides insight into the solution of the managerial problem Models are not the real problems, but abstractions of it

Some basic definitions A variable A measurable quantity that is subject to change. A decision variable A controllable variable(e.g. inventory items to order) A Parameter A measurable quantity that is known and inherent to the problem(e.g. Selling price of a product)

Types of Decision Models Deterministic Models Where all the input data value are known/available with complete certainty Most commonly used deterministic modeling techniques is linear program Probabilistic Models Where some input data values are uncertain

Quantitative vs. Qualitative Data The modeling process begins with data Quantitative Data Numerical factors such as costs and revenues Qualitative Data Factors that effect the environment which are difficult to quantify

Advantage of Decision Models No emotion/no bias Consistent A systematic approach Easy to express/deal with Easy to experiment on Generally experimenting with models requires less time, involves less risk and is less expensive

Disadvantage Model construction is hard QA in expenses of qualitative analysis.

Spreadsheets in Decision Making Computers are used to create and solve models Spreadsheets are a convenient alternative to specialized software Microsoft Excel has extensive modeling capability via the use “add-ins”

Steps in Decision Modeling 1.Formulation Translating a problem scenario from words to a mathematical model 2.Solution Solving the model to obtain the optimal solution 3.Interpretation and Sensitivity Analysis Analyzing results and implementing a solution

Steps in Modeling

Example Model: Tax Computation Self employed couple must estimate and pay quarterly income tax (joint return). Income amount is uncertain 5% of income to retirement account, up to $4000 max Personal exemption = 2 x $3200 = $6400 Standard deduction = $10,000 No other deductions

Tax Brackets Percent of Taxable IncomeTaxable Income up to $14,60010% $14,601 to $59,40015% $59,401 to $119,95025%

Example Model: Break-Even Analysis Profit = Revenue – Costs Revenue = (Selling price) x (Num. units) Costs = (Fixed cost) + (Cost per unit) x (Num. units)

The Break Even Point (BEP) is the number of units where; Profit = 0, so Revenue = Costs BEP = Fixed cost (Selling price) – (Cost per unit)

Possible Problems in Developing Decision Models  Defining the Problem Conflicting viewpoints Impact on other departments Beginning assumptions Solution outdated

Possible Problems in Developing Decision Models  Developing a Model Fitting the textbook models Understanding the model  Acquiring Input Data Using accounting data Validity of data Availability Accessibility Relevance Missing data

Possible Problems in Developing Decision Models  Developing a Solution Hard to understand mathematics Limitations of only one answer Failing to remember there are assumptions  Testing the Solution/Analyzing the Results/Implementation Problems about realization Resistance for change