spk - pemodelan dan analisis

Pemodelan dan Analisis

CHAPTER 4

Pemodelan MSSMerupakan elemen kunci bagi DSS

o Banyak kelas dalam pemodelan

o Menggunakan teknik khususdi setiap model

o Memungkinkan pengujiansering dilakukan untuk setiap solusi alternatif

Beberapa model sering disisipkan dalam sebuah DSS

SIMULASI

o Mengeksplorasi permasalahan secara lebih dekato Mengidentifikasi solusi alternatif

Keputusan Pemanfaatan Lahan

DECISION ANALYSIS

• Topik pengambilan keputusan yang paling menarikadalah decision tree (pohon keputusan).

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Seorang pengusaha mempunyai lahan dan dia harusmengambil keputusan pemanfaatan lahannya.

– Apakah lahannya akan dijual. Jika lahan dijual maka akanmenghasilkan Rp 90 juta.

– Atau ditanami anggrek. Jika diusahakan tanaman anggrekada dua kemungkinan: (1) jika beruntung ia akanmemperoleh laba Rp 700 juta; (2) jika tidak beruntung iaakan rugi Rp 100 juta.

• Kemungkinan beruntung adalah 25%, dan kemung-kinantidak beruntung adalah 75%.

• Bagaimana keputusan pengusaha tersebut?

Data & Decision Tree Results

9/30/2011 10©Prof.Dwi Darmawan

• Data disimpan pada file DECISION.DEC. Output (decision tree results, tree structure) dapat dilihatpada menu Windows.

• Decision Tree Results

Tree Structure

9/30/2011 ©Prof.Dwi Darmawan 11

• Node name:1=start, 2=Bertanam anggrek, 3=Menjual lahan, 4=Beruntung, 5=Tidakberuntung

Keputusan terbaik adalah bertanamanggrek dengan expected value Rp 100 juta.

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BREAKEVEN/COST-VOLUME ANALYSIS


• Dalam menyusun perencanaan penjualan, manajemen membutuhkan informasi– Tingkat penjualan berapa yang harus dicapai agar diperoleh laba

– Pada tingkat penjualan berapa dicapai dicapai titik impas

– Tingkat penjualan berapa perusahaan akan menderita kerugian.

• Alat bantu yang digunakan manajemen adalah analisis Breakeven Analysis (Cost vs Revenue), merupakan bagian dari Cost-Volume Analysis (CVA).

• Dalam analisis Breakeven hanya ada satu biaya tetap, satu biaya variabel, dan satu pendapatan per unit.

• Titik impas (Breakeven Point) menunjukkan volume atau Pendapatan yang hanya bisa menutup total cost.

Penentuan Titik Impaspada Perusahaan Konfeksi

Kasus

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• Perusahaan konfeksi "Krishna" memproduksi danmenjual kaos oblong. Pada tahun lalu, denganmengeluarkan biaya tetap Rp12 juta,- dan biayavariabel per unit Rp 20.000,-. perusahaan menetapkanharga jual kaos oblong Rp 35.000,- per potong.

©Prof.Dwi Darmawan

Berapa jumlah kaos oblong yang harus dijual olehperusahaan agar diperoleh titik impas?

Data & Breakeven/CVA result

Graph of Breakeven Analysis


(Breakeven result, Graph of Breakeven Analysis) dapat dilihat pada menu Windows. Breakeven Point dicapai pada volume 800 potong dan cost Rp 280 juta.

TEKNIK PERAMALANKUALITATIF & KUANTITATIF (Cont’d)


• Peramalan kuantitatif menggunakan data historisdan hubungan kausal (sebab-akibat) untukmeramalkan permintaan yang akan datang.

• Model seri waktu (time series)– Peramalan dengan penghalusan/pemulusan (smoothing):

rata-rata bergerak dan penghalusan eksponensial– Dekomposisi (trend, season, cyclic, random); metode box

jenkins (autoregressive integrated moving average, ARIMA).

• Model kausal, yakni (1) analisis regresi, seperti: regresi linier, curvilinier, dan variabel bebaskualitatif; Structural Equation Modeling (SEM).

TAHAP PERAMALAN


– Menentukan penggunaan peramalan itu, apatujuannya.

– Memilih hal-hal yang akan diramal.

– Menentukan horison waktunya, jangkapendek/panjang.

– Memilih model peramalannya.

– Mengumpulkan data yang dibutuhkan untukmembuat ramalan.

– Membuat ramalan.

– Menerapkan hasilnya.

PERAMALAN TUGAS MENANTANG


• Asumsi yang beralasan mempengaruhi ketepatanperamalan yang dibuat manajer.

• Tidak ada metode peramalan yang sempurna untuksemua kondisi.

• Sekali ditemukan pendekatan yang memuaskan, manajer masih harus terus memantau dan mengawasiramalan-ramalannya agar tidak menambah kesalahan.

• Peramalan adalah bagian dari tugas manajemen yang menantang sekaligus prestesius.

Kasus: Peramalan Penjualan Sepeda Motor

9/30/2011 24

• Dealer sepeda motor di Denpasar ingin membuatperamalan akurat penjualannya untuk bulanberikutnya. Karena pabrik terletak di Jakarta, cukupsulit bagi dealer mengembalikan/memesan motor.

• Dianalisis dengan POM for Windows (prentice-hall.com), pilih modul Forcasting. Data penjualan 12 bulan disimpan pada file FORECAST.FOR. Metode yang digunakan dipilih pada Method Box: Moving Averages.

• Kasus diselesaikan dengan Solve. Jika ada Edit data, klik Edit. Output dapat dilihat pada menu Windows.

• Peramalan penjualan bulan Januari adalah 15 unit.©Prof.Dwi Darmawan

Penjualan Sepeda Motor Tahun Lalu


Forecasting Results &Graph


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Learning Objectives

• Understand basic concepts of MSS modeling.

• Describe MSS models interaction.

• Understand different model classes.

• Structure decision making of alternatives.

• Learn to use spreadsheets in MSS modeling.

• Understand the concepts of optimization, simulation, and heuristics.

• Learn to structure linear program modeling.


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Learning Objectives

• Understand the capabilities of linear programming.

• Examine search methods for MSS models.

• Determine the differences between algorithms, blind search, heuristics.

• Handle multiple goals.

• Understand terms sensitivity, automatic, what-if analysis, goal seeking.

• Know key issues of model management.


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Dupont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette

• Promodel simulation created representing entire transport system

• Applied what-if analyses

• Visual simulation

• Identified varying conditions

• Identified bottlenecks

• Allowed for downsized fleet without downsizing deliveries

http://www.promodel.com/


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MSS Modeling

• Key element in DSS

• Many classes of models

• Specialized techniques for each model

• Allows for rapid examination of alternative solutions

• Multiple models often included in a DSS

• Trend toward transparency

– Multidimensional modeling exhibits as spreadsheet


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Simulations

• Explore problem at hand

• Identify alternative solutions

• Can be object-oriented

• Enhances decision making

• View impacts of decision alternatives


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DSS Models

• Algorithm-based models

• Statistic-based models

• Linear programming models

• Graphical models

• Quantitative models

• Qualitative models

• Simulation models


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Problem Identification

• Environmental scanning and analysis

• Business intelligence

• Identify variables and relationships– Influence diagrams

– Cognitive maps

• Forecasting– Fueled by e-commerce

– Increased amounts of information available through technology


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Static Models

• Single photograph of situation

• Single interval

• Time can be rolled forward, a photo at a time

• Usually repeatable

• Steady state– Optimal operating parameters

– Continuous

– Unvarying

– Primary tool for process design


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Dynamic Model

• Represent changing situations

• Time dependent

• Varying conditions

• Generate and use trends

• Occurrence may not repeat


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Decision-Making

• Certainty

– Assume complete knowledge

– All potential outcomes known

– Easy to develop

– Resolution determined easily

– Can be very complex


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Decision-Making

• Uncertainty

– Several outcomes for each decision

– Probability of occurrence of each outcome unknown

– Insufficient information

– Assess risk and willingness to take it

– Pessimistic/optimistic approaches


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Decision-Making

• Probabilistic Decision-Making

– Decision under risk

– Probability of each of several possible outcomes occurring

– Risk analysis

• Calculate value of each alternative

• Select best expected value


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Influence Diagrams

• Graphical representation of model

• Provides relationship framework

• Examines dependencies of variables

• Any level of detail

• Shows impact of change

• Shows what-if analysis


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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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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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Modeling with Spreadsheets

• Flexible and easy to use

• End-user modeling tool

• Allows linear programming and regression analysis

• Features what-if analysis, data management, macros

• Seamless and transparent

• Incorporates both static and dynamic models


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Decision Tables

• Multiple criteria decision analysis

• Features include:

– Decision variables (alternatives)

– Uncontrollable variables

– Result variables

• Applies principles of certainty, uncertainty, and risk


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Decision Tree

• Graphical representation of relationships

• Multiple criteria approach

• Demonstrates complex relationships

• Cumbersome, if many alternatives


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MSS Mathematical Models

• Link decision variables, uncontrollable variables, parameters, and result variables together– Decision variables describe alternative choices.

– Uncontrollable variables are outside decision-maker’s control.

– Fixed factors are parameters.

– Intermediate outcomes produce intermediate result variables.

– Result variables are dependent on chosen solution and uncontrollable variables.


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MSS Mathematical Models

• Nonquantitative models

– Symbolic relationship

– Qualitative relationship

– Results based upon

• Decision selected

• Factors beyond control of decision maker

• Relationships amongst variables


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Mathematical Programming

• Tools for solving managerial problems

• Decision-maker must allocate resources amongst competing activities

• Optimization of specific goals

• Linear programming

– Consists of decision variables, objective function and coefficients, uncontrollable variables (constraints), capacities, input and output coefficients


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Multiple Goals

• Simultaneous, often conflicting goals sought by management

• Determining single measure of effectiveness is difficult

• Handling methods:

– Utility theory

– Goal programming

– Linear programming with goals as constraints

– Point system


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Sensitivity, What-if, and Goal Seeking Analysis

• Sensitivity– Assesses impact of change in inputs or parameters on solutions

– Allows for adaptability and flexibility

– Eliminates or reduces variables

– Can be automatic or trial and error

• What-if– Assesses solutions based on changes in variables or assumptions

• Goal seeking– Backwards approach, starts with goal

– Determines values of inputs needed to achieve goal

– Example is break-even point determination


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Search Approaches

• Analytical techniques (algorithms) for structured problems– General, step-by-step search

– Obtains an optimal solution

• Blind search– Complete enumeration

• All alternatives explored

– Incomplete • Partial search

– Achieves particular goal

– May obtain optimal goal


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Search Approaches

• Heurisitic

– Repeated, step-by-step searches

– Rule-based, so used for specific situations

– “Good enough” solution, but, eventually, will obtain optimal goal

– Examples of heuristics• Tabu search

– Remembers and directs toward higher quality choices

• Genetic algorithms

– Randomly examines pairs of solutions and mutations


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Simulations

• Imitation of reality• Allows for experimentation and time compression• Descriptive, not normative• Can include complexities, but requires special skills• Handles unstructured problems• Optimal solution not guaranteed• Methodology

– Problem definition– Construction of model– Testing and validation– Design of experiment– Experimentation– Evaluation– Implementation


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Simulations

• Probabilistic independent variables– Discrete or continuous distributions

• Time-dependent or time-independent

• Visual interactive modeling – Graphical

– Decision-makers interact with simulated model

– may be used with artificial intelligence

• Can be objected oriented


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Model-Based Management System

• Software that allows model organization with transparent data processing

• Capabilities

– DSS user has control

– Flexible in design

– Gives feedback

– GUI based

– Reduction of redundancy

– Increase in consistency

– Communication between combined models


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Model-Based Management System

• Relational model base management system

– Virtual file

– Virtual relationship

• Object-oriented model base management system

– Logical independence

• Database and MIS design model systems

– Data diagram, ERD diagrams managed by CASE tools

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