CHAPTER 4. Pemodelan dan Analisis

(1)

Pemodelan

dan

Analisis

(2)

Pemodelan MSS

(3)

o Banyak kelas dalam

pemodelan

o Menggunakan teknik khusus

di setiap model

o Memungkinkan pengujian

sering dilakukan untuk setiap

solusi alternatif

(4)

Beberapa model

sering disisipkan

dalam sebuah DSS

(5)

SIMULASI

o Mengeksplorasi permasalahan secara lebih dekat

o Mengidentifikasi solusi alternatif

(6)

Keputusan

Pemanfaatan Lahan

(7)

• Topik pengambilan keputusan yang paling menarik

(8)

8

Seorang pengusaha mempunyai lahan dan dia harus

mengambil keputusan pemanfaatan lahannya.

– Apakah lahannya akan dijual. Jika lahan dijual maka akan

menghasilkan Rp 90 juta.

– Atau ditanami anggrek. Jika diusahakan tanaman anggrek

ada dua kemungkinan: (1) jika beruntung ia akan

memperoleh laba Rp 700 juta; (2) jika tidak beruntung ia

akan rugi Rp 100 juta.

(9)

• Kemungkinan beruntung adalah 25%, dan kemung-kinan

tidak beruntung adalah 75%.

(10)

Data & Decision Tree Results

9/30/2011 ©Prof.Dwi Darmawan 10

• Data disimpan pada file DECISION.DEC. Output

(decision tree results, tree structure) dapat dilihat

pada menu Windows.

(11)

Tree Structure

• Node name:1=start, 2=Bertanam anggrek,

3=Menjual lahan, 4=Beruntung, 5=Tidak

beruntung

(12)

Keputusan terbaik adalah bertanam

anggrek dengan

expected value

Rp 100

juta.

(13)

13

BREAKEVEN/COST-VOLUME ANALYSIS

(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

(15)

Penentuan Titik Impas

pada

Perusahaan

(16)

Kasus

9/30/2011 16

• Perusahaan konfeksi "Krishna" memproduksi dan

menjual kaos oblong. Pada tahun lalu, dengan

mengeluarkan biaya tetap Rp12 juta,- dan biaya

variabel per unit Rp 20.000,-. perusahaan menetapkan

harga jual kaos oblong Rp 35.000,- per potong.

(17)

Berapa jumlah kaos oblong yang harus dijual oleh

perusahaan agar diperoleh

titik impas?

(18)

(19)

Graph of Breakeven Analysis

(20)

(Breakeven result, Graph of Breakeven Analysis)

dapat dilihat pada menu Windows. Breakeven Point

dicapai

pada volume 800 potong

dan cost

Rp 280

(21)

TEKNIK PERAMALAN

KUALITATIF & KUANTITATIF (Cont’d)

• Peramalan kuantitatif menggunakan data historis

dan hubungan kausal (sebab-akibat) untuk

meramalkan 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 bebas

(22)

TAHAP PERAMALAN

– Menentukan penggunaan peramalan itu, apa

tujuannya.

– Memilih hal-hal yang akan diramal.

– Menentukan horison waktunya, jangka

pendek/panjang.

– Memilih model peramalannya.

– Mengumpulkan data yang dibutuhkan untuk

membuat ramalan.

– Membuat ramalan.

– Menerapkan hasilnya.

(23)

PERAMALAN TUGAS MENANTANG

• Asumsi yang beralasan mempengaruhi ketepatan

peramalan yang dibuat manajer.

• Tidak ada metode peramalan yang sempurna untuk

semua kondisi.

• Sekali ditemukan pendekatan yang memuaskan,

manajer masih harus terus memantau dan mengawasi

ramalan-ramalannya agar tidak menambah kesalahan.

• Peramalan adalah bagian dari tugas manajemen yang

(24)

Kasus:

Peramalan Penjualan Sepeda Motor

9/30/2011 24

• Dealer sepeda motor di Denpasar ingin membuat

peramalan akurat penjualannya untuk bulan

berikutnya. Karena pabrik terletak di Jakarta, cukup

sulit 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.

(25)

Penjualan Sepeda Motor Tahun Lalu

(26)

Forecasting Results &Graph

(27)

(28)

(29)

Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

4-29

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.

(30)

4-30

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.

(31)

4-31

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

(32)

4-32

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

(33)

4-33

Simulations

• Explore problem at hand

• Identify alternative solutions

• Can be object-oriented

• Enhances decision making

(34)

4-34

DSS Models

• Algorithm-based models

• Statistic-based models

• Linear programming models

• Graphical models

• Quantitative models

• Qualitative models

• Simulation models

(35)

4-35

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

(36)

(37)

4-37

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

(38)

4-38

Dynamic Model

• Represent changing situations

• Time dependent

• Varying conditions

• Generate and use trends

(39)

4-39

Decision-Making

• Certainty

– Assume complete knowledge

– All potential outcomes known

– Easy to develop

– Resolution determined easily

– Can be very complex

(40)

4-40

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

(41)

4-41

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

(42)

4-42

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

(43)

4-43

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

(44)

4-44

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

(45)

(46)

4-46

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

(47)

(48)

4-48

Decision Tables

• Multiple criteria decision analysis

• Features include:

– Decision variables (alternatives)

– Uncontrollable variables

– Result variables

• Applies principles of certainty, uncertainty,

and risk

(49)

4-49

Decision Tree

• Graphical representation of relationships

• Multiple criteria approach

• Demonstrates complex relationships

• Cumbersome, if many alternatives

(50)

4-50

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.

(51)

4-51

MSS Mathematical Models

• Nonquantitative models

– Symbolic relationship

– Qualitative relationship

– Results based upon

• Decision selected

• Factors beyond control of decision maker

• Relationships amongst variables

(52)

(53)

4-53

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

(54)

4-54

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

(55)

4-55

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

(56)

4-56

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

(57)

4-57

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

(58)

(59)

4-59

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

(60)

4-60

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

(61)

(62)

4-62

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

(63)

4-63

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