Mata Kuliah CSM 211 Management Support System Tahun

Mata Kuliah : CSM 211, Management Support System Tahun Akademik : 2014/2015 MODELING AND ANALYSIS Pertemuan-4 4 -1

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 4 -2

Target Pembelajaran The student can shows relationship between system models management (MBMS) to Decision Support System (DSS) 4 -3

MSS MODELING • IDENTIFICATION OF THE PROBLEM AND ENVIRONMENTAL ANALYSIS • VARIABLE IDENTIFICATION • FORECASTING • MULTIPLE MODELS • MODEL CATEGORIES • MODEL MANAGEMENT • KNOWLEDGE BASED MODELING • CURRENT TRENDS 4 -4

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 4 -5

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 4 -6

3. Linier Programming Create models to determine ”the best” from others choice combination 4. Graphical Model, Quantitative Model, Qualitative Model, Simulation 4 -7

Influence Diagrams 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 4 -8

Influence Diagrams Variables: Decision Intermediate or uncontrollable Result or outcome (intermediate or final) Arrows indicate type of relationship and direction of influence Certainty Uncertainty Amount in CDs Interest earned Sales Price 4 -9

Influence Diagrams Random (risk) ~ Demand Place tilde above variable’s name Sales Sleep all day Preference (double line arrow) Graduate University Get job Ski all day Arrows can be one-way or bidirectional, based upon the direction of influence 4 -10

An Influence Diagram for the profit model 4 -11

MSS Modeling with SPREADSHEETS ü The most popular end user modeling tool ü Qucikly recognized as easy to use implementation sofware ü Modeling tool orientation end user ü Provide linier programming and Regresi analysis with fiture analysis what If, data management and macro ü Consolidated from static and dinamic 4 -12

Excell spreadsheet Dynamic model example of a simple loan calculation of monthly payment and the effects of pre payments 4 -13

Decision Table ü Analysis decision making with multi criteria ü Provide fiture : - Decision variable (choice alternative) - Uncontrolllable variable (independent) - Variable as Result ü Provide principle of certainly, uncertainty and risk ü Show relationship variable with graphics ü Multy Criteria ü Show relationship complixity ü Difficult analysis if there are many alternative 4 -14

Model Matematical ü Tools used to problem solving for the managerial levels ü User have to provide resources about activity competity ü Determine optimitation specific goals ü Linier Programming : ü Decision variable, Objective function, and Coefisien, Uncontrol variable (constraint), capacibility and coefisien output 4 -15

Multiple Goal ü Managerial can know how to the goals by simultance can interative ü Determine one goals from achieve effectively that complexity issue ü Handle by method : - Utility Theory - Program Goal - Linier programming by goal and constraint - System Point 4 -16

SENSITIVITY ANALYSIS ü ü ü ü 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 4 -17 Trial and error simulation

Sensitivity Analysis are used for ü Revising models to eliminate too large sensitivities ü Adding details about sensitive varible or scenario ü Obtaining better estimates of sensitive external variable ü Altering the real word system to reduce actual sensitivities ü Accepting and using the sensitive real world, leading to continuous and close monitoring of actual results 4 -18

What If Analysis and Goal Seeking ü What If Analysis Makes assessment result based on changing variable and the assumptions Assuming the appropriate user interface, easy for manager to ask computer model ü Goal Seeking Calculate the values of the inputs to achieve output (goal) Represent a backward solution approach Example : Break Event Point (BEP) Analysis 4 -19

PROBLEM SOLVING SEARCH METHODS • • • Search approaches Analytical Techniques Algorithms Blind Search Heuristic Search 4 -20

SIMULATION ü Technique for conducting experiments (exp : what if) with a computer on a model of a management system ü 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 4 -21 - No garantive, result optimal

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 4 -22

The Process of Simulation 4 -23

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 4 -24

MODEL BASE MANAGEMENT SYSTEM (MBMS) • • • Control Flexibility Feedback Interface Redundancy Reduction Increased Consistency 4 -25

MODELING LANGUAGE ü Relational MBMS Provide virtual file and virtual relation (executive, optimation, sensitivity analysis) ü Object Oriented MBMS Independent logical between based on model based and other DSS component (Data Management, User Interface & KMS) ü Model Database and MIS Data dictionary, Entity Relationship Diagram (ERD) with Case 4 -26

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