Analisis Pengaruh Pemodelan dan Eksentrisitas Pelat Buhul ...
SPK - Pemodelan Dan Analisis
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Transcript of SPK - Pemodelan Dan Analisis
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Pemodelan dan Analisis
CHAPTER 4
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Pemodelan MSSMerupakan elemen kunci bagi DSS
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o Banyak kelas dalam pemodelan
o Menggunakan teknik khususdi setiap model
o Memungkinkan pengujiansering dilakukan untuk setiap solusi alternatif
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Beberapa model sering disisipkan dalam sebuah DSS
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SIMULASI
o Mengeksplorasi permasalahan secara lebih dekato Mengidentifikasi solusi alternatif
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Keputusan Pemanfaatan Lahan
DECISION ANALYSIS
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• Topik pengambilan keputusan yang paling menarikadalah decision tree (pohon keputusan).
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8
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.
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• Kemungkinan beruntung adalah 25%, dan kemung-kinantidak beruntung adalah 75%.
• Bagaimana keputusan pengusaha tersebut?
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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
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Tree Structure
9/30/2011 ©Prof.Dwi Darmawan 11
• Node name:1=start, 2=Bertanam anggrek, 3=Menjual lahan, 4=Beruntung, 5=Tidakberuntung
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Keputusan terbaik adalah bertanamanggrek dengan expected value Rp 100 juta.
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BREAKEVEN/COST-VOLUME ANALYSIS
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9/30/2011 ©Prof.Dwi Darmawan 14
• 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.
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Penentuan Titik Impaspada Perusahaan Konfeksi
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Kasus
9/30/2011 16
• 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
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Berapa jumlah kaos oblong yang harus dijual olehperusahaan agar diperoleh titik impas?
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Data & Breakeven/CVA result
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Graph of Breakeven Analysis
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(Breakeven result, Graph of Breakeven Analysis) dapat dilihat pada menu Windows. Breakeven Point dicapai pada volume 800 potong dan cost Rp 280 juta.
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TEKNIK PERAMALANKUALITATIF & KUANTITATIF (Cont’d)
9/30/2011 ©Prof.Dwi Darmawan 21
• 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).
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TAHAP PERAMALAN
9/30/2011 ©Prof.Dwi Darmawan 22
– 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.
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PERAMALAN TUGAS MENANTANG
9/30/2011 ©Prof.Dwi Darmawan 23
• 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.
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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
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Penjualan Sepeda Motor Tahun Lalu
9/30/2011 25©Prof.Dwi Darmawan
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Forecasting Results &Graph
9/30/2011 26©Prof.Dwi Darmawan
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© 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
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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