의사결정론의 개요 중앙대학교 경영대학 박해철. the classical areas of management...

의사결정론의 개요의사결정론의 개요

중앙대학교 경영대학 박해철중앙대학교 경영대학 박해철

The Classical Areas of ManagementThe Classical Areas of ManagementThe Classical Areas of ManagementThe Classical Areas of Management

• Finance• Marketing• Organizational Behavior/Personnel Management• Accounting• Management Information System• Management science/Operations Management• Strategy• International Business

The Importance of Management ScienceThe Importance of Management ScienceThe Importance of Management ScienceThe Importance of Management Science

• Management science–The discipline of applying advanced analytical

methods to help make better decisions.–Devoted to solving managerial-type problems using

quantitative models

• Applications of Management Science–Forecasting, capital budgeting, portfolio analysis,

capacity planning, scheduling, marketing, inventory management, project management, and production planning.

Successful Applications of Management ScienceSuccessful Applications of Management ScienceSuccessful Applications of Management ScienceSuccessful Applications of Management Science

Successful Applications of Management Science (cont’d)Successful Applications of Management Science (cont’d)Successful Applications of Management Science (cont’d)Successful Applications of Management Science (cont’d)

Problem Solving ApproachesProblem Solving ApproachesProblem Solving ApproachesProblem Solving Approaches

• Managers tend to use a qualitative approach to problem solving when

1.The problem is fairly simple.

2.The problem is familiar.

3.The costs involved are not great.

• Managers tend to use a quantitative approach when

1.The problem is complex.

2.The problem is not familiar.

3.The costs involved are substantial.

4.Enough time is available to analyze the problem.

Advantages of the Quantitative ApproachAdvantages of the Quantitative ApproachAdvantages of the Quantitative ApproachAdvantages of the Quantitative Approach

• Directs attention to the essence of an analysis: to solve a specific problem.

• Results in more objective decisions than purely qualitative analysis.

• Incorporates advances in computational technologies to managerial problem-solving.

ModelsModelsModelsModels

• A Model–An abstraction of reality. It is a simplified, and often

idealized, representation of reality. •Examples : an equation, an outline, a diagram, and a

map

–By its very nature a model is incomplete.

• Mathematical models–Use numbers and algebraic symbols –Decision variables–Uncontrollable variables

Deterministic versus Probabilistic ModelsDeterministic versus Probabilistic ModelsDeterministic versus Probabilistic ModelsDeterministic versus Probabilistic Models

• Deterministic models–Used for problems in which information is known with

a high degree of certainty.–Used to determine an optimal solution to the problem.

• Probabilistic models–Used when it cannot be determined precisely what

values (requiring probabilities) will occur (usually in the future).

The Management Science ApproachThe Management Science ApproachThe Management Science ApproachThe Management Science Approach

Example - Breakeven AnalysisExample - Breakeven AnalysisExample - Breakeven AnalysisExample - Breakeven Analysis

• Breakeven analysis (cost-volume analysis)–Is concerned with the interrelationship of costs,

volume (quantity of output or sales), and profit.

• The Break-Even Point (BEP)–The volume for which total revenue and total cost are

equal.–The dividing line between profit and loss; sales higher

than the break-even point will result in a profit, while sales that is lower than the break-even point will result in a loss.

–Where you get “out of the red.”

Breakeven AnalysisBreakeven AnalysisBreakeven AnalysisBreakeven Analysis

• Components of Break-Even Analysis–Volume: the level of output of a machine, department,

or organization, or the quantity of sales.–Revenue: the income generated by the sale of a

product. Total revenue = revenue per unit (selling price per unit) multiplied by units (volume) sold.

–Costs: costs that must be taken into account•Fixed costs are not related to the volume of output.•Variable costs increase and decrease with output.

Total Revenue Increases Linearly as Volume IncreasesTotal Revenue Increases Linearly as Volume IncreasesTotal Revenue Increases Linearly as Volume IncreasesTotal Revenue Increases Linearly as Volume Increases

Fixed CostsFixed CostsFixed CostsFixed Costs

Total Variable CostTotal Variable CostTotal Variable CostTotal Variable Cost

Total CostTotal CostTotal CostTotal Cost

Profit and the Break-Even PointProfit and the Break-Even PointProfit and the Break-Even PointProfit and the Break-Even Point

Profit

Example Example Example Example

The Classical Areas of The Classical Areas of Management ScienceManagement Science

The Classical Areas of The Classical Areas of Management ScienceManagement Science

• Linear Program• Network Model• Decision Analysis• Queuing Theory• Simulation• Project Management

Linear Program :Linear Program :


x1 = quantity of server model 1 to producex2 = quantity of server model 2 to producemaximize Z = 60x1+50x2

Subject to:

Example - Cost per Ounce and Dietary Requirements for Diet ProblemExample - Cost per Ounce and Dietary Requirements for Diet ProblemExample - Cost per Ounce and Dietary Requirements for Diet ProblemExample - Cost per Ounce and Dietary Requirements for Diet Problem

Example - Diet Problem (continued)Example - Diet Problem (continued)Example - Diet Problem (continued)Example - Diet Problem (continued)

Example - Workforce SchedulingExample - Workforce SchedulingExample - Workforce SchedulingExample - Workforce Scheduling

Other ApplicationsOther ApplicationsOther ApplicationsOther Applications

• Transportation problems–Developing distribution plans that will minimize total

distribution costs given the capacities of the various factories and the needs of the warehouses.

• Assignment problems–Assigning jobs to machines in such a way that the

total cost of performing the jobs is minimized.

Network Model :Network Model :

A Network Diagram of a Transshipment ProblemA Network Diagram of a Transshipment ProblemA Network Diagram of a Transshipment ProblemA Network Diagram of a Transshipment Problem

A Network Diagram of Harley’s Sand and Gravel PitA Network Diagram of Harley’s Sand and Gravel PitTransshipment ExampleTransshipment Example

A Network Diagram of Harley’s Sand and Gravel PitA Network Diagram of Harley’s Sand and Gravel PitTransshipment ExampleTransshipment Example

Decision Analysis :Decision Analysis :

Decision AnalysisDecision AnalysisDecision AnalysisDecision Analysis

• Decision analysis problems are characterized by the following:1. A list of alternatives.

2. A list of possible future states of nature.

3. Payoffs associated with each alternative/state of nature combination.

4. An assessment of the degree of certainty of possible future events.

5. A decision criterion.


Suppose that a real estate developer must decide on a plan for developing a certain piece of property. After careful consideration, the developer has ruled out “do nothing” and is left with the following list of acceptable alternatives:

1. Residential proposal.2. Commercial proposal #1.3. Commercial proposal #2.

Suppose that the developer views the possibilities as1. No shopping center.2. Medium-sized shopping center.3. Large shopping center.

General Format of a Decision TableGeneral Format of a Decision TableGeneral Format of a Decision TableGeneral Format of a Decision Table

Payoff Table for Real Estate DeveloperPayoff Table for Real Estate Developer Payoff Table for Real Estate DeveloperPayoff Table for Real Estate Developer

Decision Making under RiskDecision Making under RiskDecision Making under RiskDecision Making under Risk

• Decision making under partial uncertainty–Distinguished by the present of probabilities for the

occurrence of the various states of nature under partial uncertainty.

–The term risk is often used in conjunction with partial uncertainty.

• Sources of probabilities–Subjective estimates–Expert opinions–Historical frequencies

Real Estate Payoff Table with ProbabilitiesReal Estate Payoff Table with ProbabilitiesReal Estate Payoff Table with ProbabilitiesReal Estate Payoff Table with Probabilities

Expected Monetary Value (EMV) approach

Provides the decision maker with a value that represents an average payoff for each alternative. The best alternative is, then, the one that has the highest expected monetary value. The average or expected payoff of each alternative is a weighted average: the state of nature probabilities are used to weight the respective payoffs.

Approaches to Incorporating Probabilities in Approaches to Incorporating Probabilities in the Decision Making Processthe Decision Making Process

Approaches to Incorporating Probabilities in Approaches to Incorporating Probabilities in the Decision Making Processthe Decision Making Process

• Expected Monetary Value (EMV) approach– Provides the decision maker with a value that represents an

average payoff for each alternative.

• Expected Opportunity Loss (EOL)– The opportunity losses for each alternative are weighted by the

probabilities of their respective states of nature to compute a long-run average opportunity loss, and the alternative with the smallest expected loss is selected as the best choice.

• Expected Value of Perfect Information (EVPI)– A measure of the difference between the certain payoff that could

be realized under a condition of certainty and the expected payoff under a condition involving risk.

Decision Tree FormatDecision Tree FormatDecision Tree FormatDecision Tree Format

Decision trees are used by decision makers to obtain a visual portrayal of decision alternatives and their possible consequences.

Decision Tree for Real Estate Developer ProblemDecision Tree for Real Estate Developer ProblemDecision Tree for Real Estate Developer ProblemDecision Tree for Real Estate Developer Problem

Real Estate Problem with a Second Possible DecisionReal Estate Problem with a Second Possible DecisionReal Estate Problem with a Second Possible DecisionReal Estate Problem with a Second Possible Decision

Format of Graph for Sensitivity AnalysisFormat of Graph for Sensitivity AnalysisFormat of Graph for Sensitivity AnalysisFormat of Graph for Sensitivity Analysis

Sensitivity Analysis enables decision makers to identify a range of probabilities over which a particular alternative would be optimal.

The Expected Value Line for Alternative The Expected Value Line for Alternative a.a.The Expected Value Line for Alternative The Expected Value Line for Alternative a.a.

Example of Finding the Expected Value for Alternative Example of Finding the Expected Value for Alternative aa when P(#2) Is .50when P(#2) Is .50Example of Finding the Expected Value for Alternative Example of Finding the Expected Value for Alternative aa when P(#2) Is .50when P(#2) Is .50

All Three Alternatives Are Plotted on a Single GraphAll Three Alternatives Are Plotted on a Single GraphAll Three Alternatives Are Plotted on a Single GraphAll Three Alternatives Are Plotted on a Single Graph

The Line with the Highest Expected Profit Is Optimal for a Given Value of The Line with the Highest Expected Profit Is Optimal for a Given Value of P(#2)P(#2)

The Line with the Highest Expected Profit Is Optimal for a Given Value of The Line with the Highest Expected Profit Is Optimal for a Given Value of P(#2)P(#2)

Queuing Models :Queuing Models :

Major Elements of Waiting-Line SystemsMajor Elements of Waiting-Line SystemsMajor Elements of Waiting-Line SystemsMajor Elements of Waiting-Line Systems

Waiting lines are commonly found in a wide range of production and service systems that encounter variable arrival rates and service times.

First come, first served (FCFS)Priority Classification

The Total Cost Curve Is U-ShapedThe Total Cost Curve Is U-ShapedThe Total Cost Curve Is U-ShapedThe Total Cost Curve Is U-Shaped

The most common goal of queuing system design is to minimize the combined costs of providing capacity and customer waiting. An alternative goal is to design systems that attain specific performance criteria (e.g., keep the average waiting time to under five minutes

Operating CharacteristicsOperating CharacteristicsOperating CharacteristicsOperating Characteristics

Lq = the average number waiting for service

L = the average number in the system (i.e.,waiting for service or being served)

P0 = the probability of zero units in the system

r = the system utilization (percentage of time servers are busy serving customers)

Wa = the average time customers must wait for service

W = the average time customers spend in the system (i.e., waiting for service and service time)

M = the expected maximum number waiting for service for a given level of confidence

Line and Service Symbols for Average Number Waiting, Line and Service Symbols for Average Number Waiting, and Average Waiting and Service Timesand Average Waiting and Service Times

Line and Service Symbols for Average Number Waiting, Line and Service Symbols for Average Number Waiting, and Average Waiting and Service Timesand Average Waiting and Service Times

Formulas for Poisson Arrivals, Any Service DistributionFormulas for Poisson Arrivals, Any Service DistributionFormulas for Poisson Arrivals, Any Service DistributionFormulas for Poisson Arrivals, Any Service Distribution

Single-Server, Finite Queue Length FormulasSingle-Server, Finite Queue Length FormulasSingle-Server, Finite Queue Length FormulasSingle-Server, Finite Queue Length Formulas

Simulation :Simulation :

SimulationSimulationSimulationSimulation

• Simulation–A descriptive tool for the study of the behavior of a

system under various conditions.–The goal in simulation is to create a model that will

reflect the behavior of some real-life system in order to be able to observe how it may behave when certain inputs or parameters are changed.

–Unlike analytical techniques, it is not an optimizing technique.

The Monte Carlo MethodThe Monte Carlo MethodThe Monte Carlo MethodThe Monte Carlo Method

• Monte Carlo Simulation–A commonly used approach for achieving randomness

that derives its name from its similarity to games of chance.

• Characteristics of random numbers–All numbers are equally likely.–No patterns appear in sequences of numbers.

Random NumbersRandom NumbersRandom NumbersRandom Numbers

• In the Monte Carlo process, values for a random variable are generated by sampling from a probability distribution..

Monte Carlo MethodUse of Random Numbers


• When wheel is spun actual demand for PC’s is determined by a number at rim of the wheel.


• Process of spinning a wheel can be replicated using random numbers alone.

• Transfer random numbers for each demand value from roulette wheel to a table.

Simulating a Coin TossSimulating a Coin Toss Simulating a Coin TossSimulating a Coin Toss

Normally Distributed Random NumbersNormally Distributed Random NumbersNormally Distributed Random NumbersNormally Distributed Random Numbers

Example - Example - 재고관리 재고관리 SimulationSimulationExample - Example - 재고관리 재고관리 SimulationSimulation

일회주문량 (Q)=35 재주문점 (R)=35

1 2 3 4 5 6 7 8 9

주 기초재고 수요를 위한 난수 수요량 기말재고 재주문점 보충기간을 위한

난수 보충 기간 ( 주 ) 입고량 b)

1 60 12 5 55 0

2 55 64 7 48 0

3 48 09 5 43 0

4 43 82 8 35 35 11 1

5 35 23 6 29 0 35

6 64 51 7 57 0

7 57 29 6 51 0

8 51 10 5 46 0

9 46 56 7 39 0

10 39 28 6 33 35 96 6


1 2 3 4 5 6 7 8 9

주 기초재고 수요를 위한 난수 수요량 기말재고 재주문점 보충기간을

위한 난수 보충 기간

( 주 ) 입고량 b)

11 33 42 6 27 0

12 27 05 5 22 0

13 22 45 6 16 0

14 16 62 7 9 0

15 9 34 6 3 0

16 3 86 8 -5 35 57 2 35

17 30 18 5 25 0

18 25 22 6 19 0 35

19 54 75 8 46 0

20 46 16 5 41 0

21 41 52 7 34 35 28 1

22 34 60 7 27 0 35

23 62 93 9 53 0

24 53 38 6 47 0

25 47 94 9 38 0


1 2 3 4 5 6 7 8 9

주 기초재고 수요를 위한 난수 수요량 기말재고 재주문점 보충기간을

위한 난수 보충 기간

( 주 ) 입고량 b)

31 37 57 7 30 35 33 1

32 30 19 5 25 0 35

33 60 48 6 54 0

34 54 99 9 45 0

35 45 00 4 41 0

36 41 68 7 34 35 21 1

37 34 63 7 27 0 35

38 62 15 5 57 0

39 57 53 7 50 0

40 50 29 6 44 0

총계 1629 261 1368c) 15

평균 40.73 6.53 34.20 2.14

Steps in SimulationSteps in SimulationSteps in SimulationSteps in Simulation

Define the problem

Set objectives

Develop model

Gather data

Validate model

Design experiments

Run simulations

Analyze and interpret results

Advantages of SimulationAdvantages of SimulationAdvantages of SimulationAdvantages of Simulation

1. It is particularly well-suited for problems that are difficult or impossible to solve mathematically.

2. It allows an analyst or decision maker to experiment with system behavior in a controlled environment instead of in a real-life setting that has inherent risks.

3. It enables a decision maker to compress time in order to evaluate the long-term effects of various alternatives.

4. It can serve as a mode for training decision makers by enabling them to observe the behavior of a system under different conditions.

Limitations of SimulationLimitations of SimulationLimitations of SimulationLimitations of Simulation

• Probabilistic simulation results are approximations, rather than optimal solutions.

• Good simulations can be costly and time-consuming to develop properly; they also can be time-consuming to run, especially in cases in which a large number of trials are indicated.

• A certain amount of expertise is required in order to design a good simulation, and this may not be readily available.

• Analytical techniques may be available that can provide better answers to problems.

Project Project Management :Management :

Unique, one-time operations designed to accomplish a specific set of objectives in a limited time frame.

Build A

A Done

Build B

B Done

Build C

C Done

Build D

Ship

JAN FEB MAR APR MAY JUN

On time!

What is Project ?What is Project ?

St. Adolf’s HospitalSt. Adolf’s Hospital

직전선행활동직전선행활동ActivityActivity 내역내역 책임자책임자

AASelect administrative and medical staff.Select administrative and medical staff.BBSelect site and do site survey.Select site and do site survey.CCSelect equipment.Select equipment.DDPrepare final construction plans and layout.Prepare final construction plans and layout.EEBring utilities to the site.Bring utilities to the site.FFInterview applicants and fill positions inInterview applicants and fill positions in

nursing, support staff, maintenance, nursing, support staff, maintenance, and security.and security.

GGPurchase and take delivery of equipment.Purchase and take delivery of equipment.HHConstruct the hospital.Construct the hospital.IIDevelop an information system.Develop an information system.JJInstall the equipment.Install the equipment.KKTrain nurses and support staff.Train nurses and support staff.


직전 선행활동직전 선행활동ActivityActivity 내역내역 책임자책임자

AASelect administrative and medical staff.Select administrative and medical staff. ——JohnsonJohnsonBBSelect site and do site survey.Select site and do site survey. ——TaylorTaylorCCSelect equipment.Select equipment. AAAdamsAdamsDDPrepare final construction plans and layout.Prepare final construction plans and layout. BBTaylorTaylorEEBring utilities to the site.Bring utilities to the site. BBBurtonBurtonFFInterview applicants and fill positions inInterview applicants and fill positions in AAJohnsonJohnson


GGPurchase and take delivery of equipment.Purchase and take delivery of equipment. CCAdamsAdamsHHConstruct the hospital.Construct the hospital. DDTaylorTaylorIIDevelop an information system.Develop an information system. AASimmonsSimmonsJJInstall the equipment.Install the equipment. E,G,HE,G,HAdamsAdamsKKTrain nurses and support staff.Train nurses and support staff. F,I,JF,I,JJohnsonJohnson

ImmediateImmediateActivityActivity DescriptionDescription Predecessor(s)Predecessor(s) ResponsibilityResponsibility





Network Diagram

FinishStart

A

B

C

D

E

F

G

H

I

J

K






Completion Time

FinishStart

K6

I15

F10

C10

D10

E24

G35

H40

J4

A12

B9

Path 기대시간 (wks)

A-F-K 28A-I-K 33A-C-G-J-K 67B-D-H-J-K 69B-E-J-K 43






Completion Time

FinishStart

K6

I15

F10

C10

D10

E24

G35

H40

J4

A12

B9

Other Areas of Other Areas of Management ScienceManagement Science

Other Areas of Other Areas of Management ScienceManagement Science

• Game Theory• Markov Analysis• Nonlinear Program• Multi-criteria Decision Making Model• Scheduling• Dynamic Program

의사결정론의 개요 중앙대학교 경영대학 박해철. the classical areas of management...

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