1. Introduction to Management. Science. Introduction to Modeling. Management
Science. • Management science. • Is a scientific approach to decision making.
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Introduction to Management Science Introduction to Modeling
Management Science • Management science • Is a scientific approach to decision making. Makes extensive use of mathematical/statistical models
• This body of knowledge involving quantitative approaches to decision making is also referred to as • Quantitative analysis • Operations research • Decision modeling
• It had its early roots in World War II and is flourishing in business and industry with the aid of computers
Quantitative Analysis • Potential Reasons for a Quantitative Analysis Approach to Decision Making • The problem is complex for informal methods. • The problem is very important. • The problem is new. • Alternatively, the problem is repetitive.
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Decision Models • Decision models : • Generally mathematical representations. • Provide analytical framework for evaluating modern business problems. • Provide techniques applicable in many areas • Accounting, Economics, and Finance • Logistics, Management, and Marketing • Production, Operations, and Transportation
Decision Models • Decision models subject to • Limitations • Assumptions • Simplifications
• Models are not the real problems, but abstractions of it.
Real World
Management Situation
Analysis
Managerial Judgment
Results Interpretation
Symbolic World
Abstraction
Model
Decisions Intuition
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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)
The “Black Box” View of a Model
Decisions (Controllable) Parameters (Uncontrollable)
Model
Performance Measure(s) Consequence Variable(s)
• Decision models • Relate decision variables (controllable inputs) with fixed or variable parameters (uncontrollable inputs). • Frequently seek to maximize or minimize some performance measures (objective function) subject to constraints. • The values of the decision variables that provide the mathematically-best output are referred to as the optimal solution for the model.
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• Advantages of Decision Models • No emotion/no bias • Consistent • A systematic approach • Easy to express/easy to deal with • Easy to experiment on
• Generally, experimenting with models (compared to experimenting with the real situation) • requires less time • is less expensive • involves less risk
• Disadvantages of Decision Models • Constructing models could be hard • Models could be really hard to solve if not impossible • Can lose the real problem (too much abstraction/assumptions) • Quantitative analysis in expense of qualitative analysis
• Cost/benefit considerations must be made in selecting an appropriate model. • Frequently a less complicated (and perhaps less precise) model is more appropriate than a more complex and accurate one due to cost and ease of solution considerations.
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Types of Decision Models Decision Models
Deterministic Models
Probabilistic Models
• Deterministic models assume • Complete certainty. • All information needed is available with fixed and known values.
• Most commonly used deterministic modeling technique is Linear Programming
• Probabilistic models are also called stochastic models. • Probabilistic models • assume some of data is not known with certainty. • take into account information will be known after decision is made.
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Steps in Decision Modeling 1. Formulation.
2. Solution.
3. Interpretation.
Steps in Decision Modeling • Defining the problem • Develop clear and concise problem statement • Do not solve the wrong problem
• Developing a model • Select and develop a decision model • Select appropriate problem variables • Develop relevant mathematical relation for consideration and evaluation
Steps in Decision Modeling • Acquiring input data • Collect accurate data for use in model. • Possible data sources are: • Official company reports. • Accounting, operating, and financial information. • Views, and opinions from knowledgeable individuals.
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Steps in Decision Modeling • Developing a solution • Solution of set of mathematical expressions • Alternative trial and error iterations • Complete enumeration of all possibilities • Utilization of an algorithm/heuristic • Series of steps repeated until satisfactory solution is attained
Steps in Decision Modeling • Testing a solution • Prior to implementation of model solution, testing solution. • Testing of solution is accomplished by examining and evaluating: •Data utilized in model by acquiring new data •The model itself
Steps in Decision Modeling • Interpretation and What-if Analysis (Analyzing the results and sensitivity analysis) • Vary data input values and examine differences in various optimal solutions • Make changes in model parameters and examine differences in various optimal solutions
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Steps in Decision Modeling
• Implementing the results
• Optimal solution must be interpreted carefully •Do not forget the assumptions •Model is not the real problem •Optimal solution is there to give insight • Solution implementation usually requires making changes within the organization • Recommendations often require changes in data, data handling, resource mixes, systems, procedures, policies, and personnel
Possible Problems in Decision Modeling • Defining the problem • Conflicting viewpoints • Impact on other departments • Real life is too ambiguous– a jungle • Problems that change quickly
• Developing a model • Beginning assumptions • Fitting textbook models • Understanding/accepting a model
Possible Problems in Decision Modeling • Acquiring data • Availability • Accesibility • Relevance • Quality • Missing data
• Developing a solution • Complex mathematics • Giving only-one answer (insight is important) • Failing to remember there are assumptions
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Possible Problems in Decision Modeling • Testing the solution/analyzing results/implementing results • Problems about realization/implementation • Time dimension (no immediate effects) • Resistance for change
An Example to Spreadsheet Use: Break-even Analysis • Break-even point • Point of equality • Unit is the “# of something” •-=+ • Revenues = Expenses • Profit = 0
Example: YesilÇayirlar • YeşilÇayırlar Development Corporation (YDC) is a small real estate developer that builds only one style house. The selling price of the house is 115,000TL. • Land for each house costs 55,000TL and lumber, supplies, and other materials run another 30,000TL per house. Total labor costs are approximately 20,000TL per house • The salaries of the employees and the office rents sum up to 40,000TL per month.
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Example: YesilÇayirlar • Revenue per house = r(x) = 115000x • Costs • Fixed cost = F = 40000 • Variable costs per house = v(x) = (55000 + 30000 + 20000)x = 105000x • Total costs = c(x) = F + v(x) = 40000 + 105000x
• Break-even point r(x) = c(x) 115000x = 40000 + 105000x Solving, x = 4 houses
Example: YesilÇayirlar • What is monthly profit if 12 houses are sold? p(x) = 115000x – 105000x – 40000 p(x) = 10000x – 40000 p(12) = 10000(12) – 40000 = 80000TL
• What is monthly profit if 2 houses are sold? p(2) = 10000(2) – 40000 = -20000TL
Graph of Break-Even Analysis
Thousands of TLs
1200 Total Revenue = 115000x
1000 800 600
Total Cost = 40000 + 105000x
400 200 0
Break-Even Point = 4 Houses
0
1
2 3 4 5 6 7 8 Number of Houses Sold (x)
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