Upload
marilyn-black
View
223
Download
8
Embed Size (px)
Citation preview
Decision Support and Decision Support and Business Intelligence Business Intelligence
SystemsSystems(8(8thth Ed., Prentice Hall) Ed., Prentice Hall)
Chapter 4:Chapter 4:
Modeling and AnalysisModeling and Analysis
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-2
Major Modeling IssuesMajor Modeling Issues problem identification and environmental
analysis: scanning the environment to figure out what problems exist and can be solved via a model
variable identification: identifying the critical factors in a model and their relationships
ex: Influence diagram : Graphical representations of a model Rectangle = a decision variable Circle = uncontrollable or intermediate variable Oval = result (outcome) variable: intermediate or final
Variables are connected with arrows indicates the direction of influence (relationship)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-3
Major Modeling IssuesMajor Modeling Issues forecasting: predicting the future
It is essential for construction models because when a decision implemented, the results occur in the future.
E-Commerce ( Information about purchases should be analyzed to predict demand)
5 Rights (How to get the right product(s) to the right customer at the right price at the right time in the right format
CRM and RMS rely heavily on forecasting techniques Predict the most profitable customers
use of multiple models: combining them to solve many parts of a complex problem
Each of which represents a different part of the decision – making problem
E.g., the Procter and Gamble supply chain DSS include: Location model to locate distribution centre , a product strategy
model, a demand- forecasting model, cost generation model ,….
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-4
Major Modeling IssuesMajor Modeling Issues use of multiple models
Types of models:
1. Standard : built in to DSS or freestanding soft ware that can interface with a DSS
2. Nonstandard : constructed from scratch. model categories: selecting the right type of model for
the problem or sub-problem (table 4.1)
model management: coordinating a firm’s models and their use
Models like data, must be managed to maintain their integrity and their applicability
Management is done by MBMS knowledge-based modeling: how to take advantage of
human knowledge in modeling DSS use mostly quantitive models, wheres Expert systems use
qualitiative
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-5
Categories of Models Categories of Models Table 4.1Table 4.1
Category Objective Techniques
Optimization of problems with few alternatives
Find the best solution from a small number of alternatives
Decision tables, decision trees
Optimization via algorithm
Find the best solution from a large number of alternatives using a step-by-step process
Linear and other mathematical programming models
Optimization via an analytic formula
Find the best solution in one step using a formula
Some inventory models
Simulation Find a good enough solution by experimenting with a dynamic model of the system
Several types of simulation
Heuristics Find a good enough solution using “common-sense” rules
Heuristic programming and expert systems
Predictive and other models
Predict future occurrences, what-if analysis, …
Forecasting, Markov chains, financial, …
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-6
Static and Dynamic ModelsStatic and Dynamic Models Static Analysis
Single snapshot of the situation, every thing occurs in a single interval
describes relationships among parts of a system at a point in time.
Ex: A decision about buy a product , Annual income statement.
Dynamic Analysis Evaluate scenarios that change over time
Time dependent Ex: In determining how many checkout points should
be open in a supermarket. A 5 year Profit and Loss projection in which input data
(costs, prices, and quantities ) change from year to year
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-7
MSS Modeling with SpreadsheetsMSS Modeling with Spreadsheets Spreadsheet: most popular end-user modeling
tool Flexible and easy to use Powerful functions
Add-in functions and solvers (small programs designed to extend the capabilities of a spreadsheet package)
Programmability (via macros) What-if analysis Goal seeking Simple database management Incorporates both static and dynamic models Examples: Microsoft Excel, Lotus 1-2-3
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-8
Types of Decision Making Types of Decision Making EnvironmentsEnvironments
Decision Making under Certainty Decision Making under Risk
(Decision making with probability) Decision Making Under Uncertainty
(Decision making without probability)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-9
The Six Steps in Decision TheoryThe Six Steps in Decision Theory
1. Clearly define the problem at hand2. List all the possible alternatives (decisions to
be made)3. Identify the possible outcomes (state of
nature) of each alternative4. List the payoff or the profit of each
combination of alternatives and outcomes5. Select one of the mathematical decision
theory models (e.g. Decision Making under Risk)
6. Apply the model and make your decision
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-10
Certainty, Uncertainty and RiskCertainty, Uncertainty and Risk
The Zones of Decision Making
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-11
Decision Making:Treating CertaintyDecision Making:Treating Certainty Certainty Models
Assume complete knowledge All potential outcomes are known May yield optimal solution The decision maker knows exactly what the outcome of
each course of action will be. decision maker is to compute the optimal alternative
or outcome with some optimization criterion in mind. Ex: if the optimization criterion is least cost and you are
considering two different brands of a product, which appear to be equal in value to you, one costing 20% less than the other, then, all other things being equal, you will choose the less expensive brand.
decision making under certainty is rare because all other things are rarely equal.
Linear programming is one of the techniques for finding an optimal solution under certainty
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-12
Decision Making: Uncertainty and Decision Making: Uncertainty and RiskRisk Uncertainty
Several outcomes for each decision Probability of each outcome is unknown Knowledge would lead to less uncertainty Decision under uncertainty is very difficult Managers attempt to avoid uncertainty.
Instead they attempt to obtain more information so it can be treated under certainty Or
Some estimated probabilities are assigned to the outcomes and the decision making is done as if it is decision making under risk.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-13
Decision Making Under Decision Making Under UncertaintyUncertainty Four Criteria MAXIMAX - find the alternative that maximizes the
maximum outcome for every alternative (Optimistic approach ) Ex: stocks
MAXIMIN - find the alternative that maximizes the minimum outcomes for every alternative (Pessimistic approach ) Ex : CDs
EQUALLY LIKELY- find the alternative with the highest average outcome
MINIMAX REGRET- minimizes the maximum regret (regret is the difference between the payoff from the best decision and all the other decision payoffs)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-14
Decision Analysis: A Few Decision Analysis: A Few AlternativesAlternatives
Single Goal Situations 1. Decision tables :organize information and
knowledge in a systmatic ,tabular manner to prepare it for analysis
Multiple criteria decision analysis Features include :
Decision variables: describe alternatives course of variable),
Uncontrollable variables, Parameters : factors that effect the result variables nut not under control of decision maker
Result variables: reflect intermediate outcomes in mathematical models.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-15
Decision Making: RiskDecision Making: Risk Risk analysis (probabilistic decision making)
Several outcomes for each decision Probability of each outcome is known Instead of optimizing the outcomes, the general rule
is to optimize the expected outcome. As an example: if you are faced with a choice
between two actions one offering a 1% probability of a gain of $10000 and the other a 50% probability of a gain of $400, you as a rational decision maker will choose the second alternative.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-16
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
Let us suppose that based on several economic forecasts, the investor is able to estimate
0.50% Solid Growth 0.30% Stagnation 0.20% Inflation
State of natureAlternative Solid Growth
. 50%Stagnation
.30%Inflation
.20%
Bonds 12 6 3
Stocks 15 3 -2
CDs 6.5 6.5 6.5
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-17
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
Risk analysis 1. Compute expected values or Expected payoff (EP) (outcome of first state of nature)*(its prob.) +
(outcome of second state of nature)*(its prob.)+…+ (outcome of last state of nature) * (its prob.) E.g. , In bonds yield = 12(.5)+6(.3)+3(.2) = 8.4 percent
The Best decision is the one with the greatest EP If the payoffs were in terms of costs, the best decision
would be the one with the lowest EP
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-18
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
2. Alternative approach in decision making under risk is to minimize expected opportunity loss (EOL).
Opportunity loss, also called regret EOL for an alternative is sum of all possible regrets of
alternative, each weighted by probability of state of nature for that regret occurring.
EOL (alternative i ) = (regret of first state of nature) x (probability of first state of nature) + (regret of second state of nature) x (probability of second state of nature) + . . . + (regret of last state of nature) x (probability of last state of nature)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-19
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk EOL: Opportunity loss table (=regret table)
State of natureAlternative Solid Growth
. 50%Stagnation
.30%Inflation
.20%EOL
$
Bonds 3 .5 3.5 2.35
Stocks 0 3.5 8.5 2.75
CDs 8.5 0 0 4.25
Min
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-20
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
3. The Maximum Likelihood Criterion Identify the state of nature with the largest Probability. 2. Choose the decisions alternative that has the
largest Payoff
State of natureAlternative Solid Growth
. 50%Stagnation
.30%Inflation
.20%
Bonds 12 6 3
Stocks 15 3 -2
CDs 6.5 6.5 6.5
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-21
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
Expected Value of Perfect Information (EVPI) Is used to place an upper limit on what you should pay for
information that will aid in making a better decision. Is the increase in the EP that could be obtained if it were
possible to learn the true state of nature before making the decision
Is the difference between the expected value under certainty and the expected value under risk
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-22
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
Expected Value of Perfect Information (EVPI) EVPI = A – B A = expected value with perfect information B = expected value without perfect information For A: The optimal values for each value are:
Max Value (A)= 15*.5 +6.5*.3 +6.5*.2 =10.75
State of natureAlternative Solid Growth
. 50%Stagnation
.30%Inflation
.20%
Bonds 12 6 3
Stocks 15 3 -2
CDs 6.5 6.5 6.5
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-23
Investment Example Investment Example Decision Making Under RiskDecision Making Under Risk
Expected Value of Perfect Information (EVPI) B = expected value without perfect information For B: we compute the expected values for each
column first, and then select the max as below:
EVPI = 10.75-8.4 = 2.55 $
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-24
Decision Analysis: A Few Decision Analysis: A Few AlternativesAlternatives
Single Goal Situations Decision trees
Graphical representation of relationships Multiple criteria approach Demonstrates complex relationships Cumbersome, if many alternatives exists
How can a decision tree be used in decision making?By showing the decision maker the possible outcomes that could result from a given choice, the tree gives the decision maker information by which to compare choices
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-25
Decision treesDecision trees The Five Steps
1. Define the problem2. Structure or draw the decision tree3. Assign probabilities to the states of nature4. Estimate the payoffs for each possible combination
of alternative and state of nature Solve the problem by computing expected payoff (EP) for each state of nature node
5. Make your decision
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-26
Decision Tree Example (Self Decision Tree Example (Self Study)Study) This is just a beginning of ADM2302 course and Andrew does not know
if he should attend all classes. He consulted some other students and came to the following conclusions:• chances of passing a course while attending all classes are 80%• chances of passing a course while attending randomly are 50%.It is well known that professor who is teaching that course is giving
second chance to the students who failed. They have to solve a pretty nasty case study.
Again, Andrew estimates that chances of solving this case if he would go to all the classes are 60%, while they drop to just 10% if he would attend classes randomly.
Andrew would be very happy if he passes the course (5 on a happiness scale of 0 - 5). Clearly, he would be very disappointed if he fails (0 on a happiness scale).
Going to a classroom requires an effort and diminished happiness associated with passing the course.
It goes down by 3 points (happiness scale) for attending all classes and 1 point for 39 random attendance.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-27
Optimization Optimization via Mathematical Programmingvia Mathematical Programming Mathematical Programming
A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal
Optimal solution: The best possible solution to a modeled problem Linear programming (LP): A mathematical model
for the optimal solution of resource allocation problems. All the relationships are linear Limited quantity of economic resources Allocation is usually restricted by constraints
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-28
LineLine
Linear Programming StepsLinear Programming Steps 1. Identify the …
Decision variables Objective function Objective function coefficients Constraints
Capacities / Demands
2. Represent the model LINDO: Write mathematical formulation EXCEL: Input data into specific cells in Excel
3. Run the model and observe the results
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-29
Sensitivity, What-if, and Sensitivity, What-if, and Goal Seeking AnalysisGoal Seeking Analysis Sensitivity
Assesses impact of change in inputs on outputs Eliminates or reduces variables Can be automatic or trial and error
1. Automatic Sensitivity analysis is performed in standard quantitative model implementation such as LP
2. Trial and Error Change in any variable or in several Two approaches: What-if and Goal Seeking
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-30
Sensitivity, What-if, and Sensitivity, What-if, and Goal Seeking AnalysisGoal Seeking Analysis
What-if Assesses solutions based on changes in variables
or assumptions (scenario analysis) Ex: What will happen to the total inventory cost if
the cost of carrying inventories increases by 10 percent?
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-31
Sensitivity, What-if, and Sensitivity, What-if, and Goal Seeking AnalysisGoal Seeking Analysis
Goal seeking Backwards approach, starts with goal Determines values of inputs needed to achieve
goal Ex: what annual R&D budget is needed for an annual
growth rate of 15 percent by 2014?
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-32
Sensitivity, What-if, and Sensitivity, What-if, and Goal Seeking AnalysisGoal Seeking Analysis Computing a Break-Even Point Using Goal
Seeking Values that generate zero profit Break –Even= Fixed cost / (selling cost – variable
cost) Where : fixed cost =cost that not change such as tax,
insurance ,.. Selling price: the price that a unit sold for Variable cost : related to production unit.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-33
Problem –Solving Search Problem –Solving Search MethodsMethods
Search methods used in the choice phase of problem solving includes:
Analytical techniques , algorithms , blind searching and heuristic searching
For normative models (Comparing all the outcomes of alternative) :
analytical approach is used For descriptive models(a comparison of a limited number
of alternatives is used) : blindly or heuristic s are used.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-34
Problem –Solving Search Problem –Solving Search MethodsMethods
Analytical Techniques Use mathematical formulas to derive optimal solution Solving structured problems (tactical or operational) Ex: inventory management, resource allocation. Analytical Techniques may use Algorithms
Algorithms Step by step search process Obtaining an optimal solution
Web search engines use algorithms To speed searches and produces accurate results
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-35
Problem –Solving Search Problem –Solving Search MethodsMethods
Blind Searching Problem solving is done by searching through the possible
solutions The first search methods of problem solving Arbitrary search approaches that are not guided Two types:
A complete enumeration : all alternatives are considered to find an optimal solution.
Incomplete (Partial) : continues until a good –enough solution is found
Heuristic Searching Informal judgmental knowledge of an application area that
constitute the rules of the good judgment in the field.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-36
SimulationSimulation
Simulation is a process of designing a model of real system a model of real system
purpose of understanding the behavior for the operation of the behavior for the operation of the system.
Frequently used in DSS tools
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-37
SimulationSimulation System:
Collection of entities, ex: people machines that act and interact towards the accomplishment.
State: Collection of variables necessary to describe a system at a
particular time relative to the objective of study Bank model: Could include number of busy tellers, time of arrival
of each customer, etc System can be 1. Discrete
State variables change instantaneously at separated points in time
Bank model: State changes occur only when a customer arrives or departs
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-38
2. Continuous State variables change that continuously tracks system
response over time Airplane flight: State variables like position, velocity
change continuously
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-39
Imitates reality and capture its richness Technique for conducting experiments Descriptive, not normative tool Often to “solve” very complex problems
Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques
Major Characteristics of Major Characteristics of SimulationSimulation
!!
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-40
Advantages of SimulationAdvantages of Simulation The theory is fairly straightforward Great deal of time compression Experiment with different alternatives The model reflects manager’s perspective Can handle wide variety of problem types Can include the real complexities of
problems Produces important performance measures Often it is the only DSS modeling tool for
non-structured problems
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-41
Limitations of SimulationLimitations of Simulation
Cannot guarantee an optimal solution Slow and costly construction process Cannot transfer solutions and inferences
to solve other problems (problem specific)
Software may require special skills
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-42
Simulation MethodologySimulation Methodology Model real system and conduct repetitive experiments. Steps:
1. Define problem 5. Conduct experiments2. Construct simulation model 6. Evaluate results3. Test and validate model 7. Implement solution4. Design experiments
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall4-43
Visual interactive modeling (VIM)Also called Visual interactive problem solving Visual interactive modeling Visual interactive simulation
Uses computer graphics to present the impact of different management decisions
Often integrated with GIS Users perform sensitivity analysis Static or a dynamic (animation) systems
Visual Interactive Modeling (VIM) Visual Interactive Modeling (VIM) / Visual Interactive Simulation / Visual Interactive Simulation (VIS)(VIS)