1. MODELING AND ANALYSIS Pertemuan-4
Mata Kuliah : CSM211, Management Support System
Tahun Akademik : 2014/2015
MODELING AND ANALYSIS Pertemuan-4
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2. Sasaran Pembelajaran Understand the basis concepts of MSS modeling
Describe how MSS models interact with data and the user
Understand of different model classes
Understand how the structure decision making of a few alternatives
Describe how spreadsheets can be used for MSS modeling and solution
Explain what optimization, simulation, and heuristics are, and when and how to use them
Describe how to structure a linear programming model
Become familiar with some capabilities of linear programming and simulation packages
Understand how search methods are used to solve MSS models
Explain what is meant by sensitivity, automatic, what if analysis, and goal seeking
Describe the key issues of model management
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3. Target Pembelajaran
The student can shows relationship between system models management (MBMS) to Decision Support System (DSS)
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4. MSS MODELING IDENTIFICATION OF THE PROBLEM AND ENVIRONMENTAL ANALYSIS
VARIABLE IDENTIFICATION
FORECASTING
MULTIPLE MODELS
MODEL CATEGORIES
MODEL MANAGEMENT
KNOWLEDGE BASED MODELING
CURRENT TRENDS
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5. STATIC AND DYNAMIC MODELS
Static Analysis
Take a single snapshot of a situation
Dynamic Analysis
Time Dependent
Represent or generate trends and patterns over time
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6. MODEL 1. Algorithm Based Model
Create model with algoritm formula to calculate on DSS model
Example : cost estimate formula
2. Statistic Based :
Creat models with statistic formula
Example : forecasting
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7. Create models to determine ”the best” from others choice combination
3. Linier Programming
Create models to determine ”the best” from others choice combination
4. Graphical Model, Quantitative Model, Qualitative Model, Simulation
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8. Influence Diagrams CHARACTERICS :
A Graphical representation of a model used to assist in model design, development, and understanding.
CHARACTERICS :
- Provide representation model graphic and visual
communication
- Framework for expressing a modeler in focusing on
the models
- Explain determining between variable
- Show impact change of analisis what If
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9. Influence Diagrams
Decision
Intermediate or uncontrollable
Variables:
Result or outcome (intermediate or final)
Certainty
Uncertainty
Arrows indicate type of relationship and direction of influence
Amount in CDs
Interest earned
Price
Sales
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10. Influence Diagrams
Random (risk)
Place tilde above variable’s name
~ Demand
Sales
Preference
(double line arrow)
Graduate
University
Sleep all day
Ski all day
Get job
Arrows can be one-way or bidirectional, based upon the direction of influence
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11. An Influence Diagram for the profit model
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12. MSS Modeling with SPREADSHEETS
The most popular end user modeling tool
Qucikly recognized as easy to use implementation sofware
Modeling tool orientation end user
Provide linier programming and Regresi analysis with fiture analysis what If, data management and macro
Consolidated from static and dinamic
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13. Excell spreadsheet Dynamic model example of a simple loan calculation of monthly payment and the effects of pre payments
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14. Decision Table Analysis decision making with multi criteria
Provide fiture :
- Decision variable (choice alternative)
- Uncontrolllable variable (independent)
- Variable as Result
Provide principle of certainly, uncertainty and risk
Show relationship variable with graphics
Multy Criteria
Show relationship complixity
Difficult analysis if there are many alternative
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15. Model Matematical
Tools used to problem solving for the managerial levels
User have to provide resources about activity competity
Determine optimitation specific goals
Linier Programming :
Decision variable, Objective function, and Coefisien, Uncontrol variable (constraint), capacibility and coefisien output
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16. Multiple Goal
Managerial can know how to the goals by simultance can interative
Determine one goals from achieve effectively that complexity issue
Handle by method :
- Utility Theory
- Program Goal
- Linier programming by goal and constraint
- System Point
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17. SENSITIVITY ANALYSIS
Makes prediction and assumptions regarding the input data, many of which the assessment of uncertain futures
The impact of change in external (uncontrolable) variables and parameters on outcome variable
Impact of changes in decision variable on outcome variable
The efect of uncertainly in estimating external variable
The effect of different dependent interactions among variable
The rebustness of decision under changing conditions
Allows adaptation and flexibility to changing conditions
Trial and error simulation
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18. Sensitivity Analysis are used for
Revising models to eliminate too large sensitivities
Adding details about sensitive varible or scenario
Obtaining better estimates of sensitive external variable
Altering the real word system to reduce actual sensitivities
Accepting and using the sensitive real world, leading to continuous and close monitoring of actual results
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19. What If Analysis and Goal Seeking
Makes assessment result based on changing variable and the assumptions
Assuming the appropriate user interface, easy for manager to ask computer model
Goal Seeking
Calculate the values of the inputs to achieve output (goal)
Represent a backward solution approach
Example :
Break Event Point (BEP) Analysis
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20. PROBLEM SOLVING SEARCH METHODS
Search approaches
Analytical Techniques
Algorithms
Blind Search
Heuristic Search
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21. SIMULATION
Technique for conducting experiments (exp : what if) with a computer on a model of a management system
Make the assumption result characterics from the actual
CHARACTERICS :
- Making trial error s (assumption) from the reality
- Provide experiement
- Descriptive (not normative)
- Complexity can increasing, need specific expert
- Handle unstructure problems
- No garantive, result optimal
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22. METHODOLOGY OF SIMULATION
Problem definition
Construction of the simulation model
Testing and validating the model
Design of the experiment
Conduction the experiment
Evaluation the results
Implementation
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23. The Process of Simulation
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24. MODEL BASE MANAGEMENT SYSTEM (MBMS)
MBMS is a software package with capatibilites similar to those of a DBMS
Limited MBMS capabilities are provided by some spreadsheets and other model based DSS tools and languanges
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25. MODEL BASE MANAGEMENT SYSTEM (MBMS)
Control
Flexibility
Feedback
Interface
Redundancy Reduction
Increased Consistency
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26. MODELING LANGUAGE Relational MBMS
Provide virtual file and virtual relation (executive, optimation, sensitivity analysis)
Object Oriented MBMS
Independent logical between based on model based and other DSS component (Data Management, User Interface & KMS)
Model Database and MIS
Data dictionary, Entity Relationship Diagram (ERD) with Case
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27. =============== thanks 4 your attention ============
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