Decision Making Prentice Hall, Inc. A 1

Decision

Making

Decision

Making

Module based on

Module based on

Operation Management, 9e

Operation Management, 9e

PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer

Heizer/Render /Render

Lecturer: F. Priyo Suprobo, ST, MT Lecturer: F. Priyo Suprobo, ST, MT

Product Design of Product Design of ITATS ITATS

Permasalahan

Permasalahan

Konsultan

Konsultan desaindesain HCIDHCID--ITATSITATS bekerjabekerja untukuntuk HealthyHealthy PillowPillow Company

Company sedangsedang mengusulkanmengusulkan rancanganrancangan AlasAlas TidurTidur KesehatanKesehatan yang

yang mutakhirmutakhir dengandengan beberapabeberapa pilihanpilihan.. BekerjasamaBekerjasama dengandengan tenaga

tenaga pemasaranpemasaran HealthyHealthy PillowPillow dirumuskanlahdirumuskanlah beberapabeberapa alternatif

alternatif berikutberikut peluangpeluang keberhasilannyakeberhasilannya sebagaisebagai berikutberikut::

Selanjutnya, terhadap alternatif yang ada, apakah Selanjutnya, terhadap alternatif yang ada, apakah saran Anda sebagai staf HCID

saran Anda sebagai staf HCID--ITATS untuk Healthy ITATS untuk Healthy Pillow ini?

Outline

Outline

;

; Proses Keputusan

Proses Keputusan

;

; Dasar

Dasar--Dasar Pengambilan

Dasar Pengambilan

Keputusan

Keputusan

;

Outline

Outline –

– Continued

Continued

;

; Tipe Pengambilan Keputusan

Tipe Pengambilan Keputusan

;

; Pengambilan Keputusan dalam Pengambilan Keputusan dalam Ketidakpastian

Ketidakpastian

;

; Pengambilan Keputusan dengan Pengambilan Keputusan dengan Resiko

Resiko

;

; Pengambilan Keputusan dalam Pengambilan Keputusan dalam Kepastian

Kepastian

;

; Expected Value of Perfect Expected Value of Perfect Information (EVPI)

Outline

Outline –

– Continued

Continued

;

; Pohon Keputusan

Pohon Keputusan

;

; Pohon Keputusan SederhanaPohon Keputusan Sederhana

;

; Pohon Keputusan yang lebih Pohon Keputusan yang lebih Kompleks

Learning Objectives

Learning Objectives

When you complete this module you

When you complete this module you

should be able to:

should be able to:

1.

1. Membuat sebuah pohon keputusan Membuat sebuah pohon keputusan sederhana

sederhana 2.

2. Membangun tabel keputusanMembangun tabel keputusan 3.

3. Menjelaskan kapan menggunakan Menjelaskan kapan menggunakan salah satu tipe dalam pengambilan salah satu tipe dalam pengambilan keputusan

keputusan 4.

4. MenghitungMenghitung expected monetary value expected monetary value (EMV)

Learning Objectives

Learning Objectives

When you complete this module you

When you complete this module you

should be able to:

should be able to:

5.

5. MenghitungMenghitung expected value of perfect expected value of perfect information (EVPI)

information (EVPI) 6.

6. Mengevaluasi titikMengevaluasi titik--titik dalam Pohon titik dalam Pohon Keputusan

Keputusan 7.

7. Membuat Pohon Keputusan dengan Membuat Pohon Keputusan dengan penyelesaian berurutan

The Decision Process in

The Decision Process in

Operations

Operations

1.

1. Clearly define the problems and the Clearly define the problems and the factors that influence it

factors that influence it 2.

2. Develop specific and measurable Develop specific and measurable objectives

objectives 3.

3. Develop a modelDevelop a model 4.

4. Evaluate each alternative solutionEvaluate each alternative solution 5.

5. Select the best alternativeSelect the best alternative 6.

6. Implement the decision and set a Implement the decision and set a timetable for completion

Fundamentals of

Fundamentals of

Decision Making

Decision Making

1.

1. Terminologi/Istilah

Terminologi/Istilah::

a.

a. AlternativeAlternative –– Sebuah tindakan atau Sebuah tindakan atau strategi yang dapat dipilih oleh

strategi yang dapat dipilih oleh pengambil keputusan

pengambil keputusan b.

b. State of State of naturenature/Kondisi Alami/Kondisi Alami –– Sebuah kejadian atau kondisi Sebuah kejadian atau kondisi dimana pengambil keputusan dimana pengambil keputusan

hanya punya sedikit kendali atau hanya punya sedikit kendali atau tidak sama sekali

Fundamentals of

Fundamentals of

Decision Making

Decision Making

2.

2. Symbols

Symbols dalam Pohon Keputusan

dalam Pohon Keputusan::

a.

a. †† –– Sebuah titik keputusan dimana Sebuah titik keputusan dimana terdapat satu atau lebih alternatif terdapat satu atau lebih alternatif yang dapat dipilih

yang dapat dipilih

b.

b. {{ –– sebuah simbol titik kondisi sebuah simbol titik kondisi alami yang mungkin terjadi

Decision Tree Example

Decision Tree Example

Pasar sesuai harapan Pasar sesuai harapan

Pasar tidak sesuai harapan Pasar tidak sesuai harapan

Pasar sesuai harapan Pasar sesuai harapan

Pasar tidak sesuai harapan Pasar tidak sesuai harapan Desain TPC

Desain TPC Titik Keputusan

Titik Keputusan Titik Kondisi AlamiTitik Kondisi Alami

Figure A.1 Figure A.1

Decision Table Example

Decision Table Example

Kondisi Alami Kondisi Alami Alternatives

Alternatives Pasar sesuaiPasar sesuai Pasar TidakSesuaiPasar TidakSesuai Desain UMPC Desain UMPC $200,000$200,000 ––$180,000$180,000 Desain Tablet PC Desain Tablet PC $100,000$100,000 ––$ 20,000$ 20,000 Do nothing Do nothing $ 0$ 0 $ 0$ 0 Table A.1 Table A.1

Decision

Decision--Making

Making

Environments

Environments

;

; Pengambilan Keputusan dalam KetidakpastianPengambilan Keputusan dalam Ketidakpastian

;

; Kondisi alami tidak dapat diperkirakanKondisi alami tidak dapat diperkirakan

;

; Pengambilan Keputusan dengan ResikoPengambilan Keputusan dengan Resiko

;

; Beberapa kondisi alami mungkin terjadiBeberapa kondisi alami mungkin terjadi

;

; Tetapi masingTetapi masing--masing pilihan tetap berpeluangmasing pilihan tetap berpeluang

;

; Pengambilan Keputusan dalam KepastianPengambilan Keputusan dalam Kepastian

;

Ketidakpastian

Ketidakpastian

1.

1. Maximax

Maximax

;

; Find the alternative that maximizes Find the alternative that maximizes the maximum outcome for every

the maximum outcome for every alternative

alternative

;

; Pick the outcome with the maximum Pick the outcome with the maximum number

number

;

; Highest possible gainHighest possible gain

;

; This is viewed as an optimistic This is viewed as an optimistic approach

Ketidakpastian

Ketidakpastian

2.

2. Maximin

Maximin

;

; Find the alternative that maximizes Find the alternative that maximizes the minimum outcome for every

the minimum outcome for every alternative

alternative

;

; Pick the outcome with the minimum Pick the outcome with the minimum number

number

;

; Least possible lossLeast possible loss

;

; This is viewed as a pessimistic This is viewed as a pessimistic approach

Ketidakpastian

Ketidakpastian

3.

3. Equally

Equally likely

likely (Sama rata)

(Sama rata)

;

; Find the alternative with the highest Find the alternative with the highest average outcome

average outcome

;

; Pick the outcome with the maximum Pick the outcome with the maximum number

number

;

; Assumes each state of nature is Assumes each state of nature is equally likely to occur

Uncertainty Example

Uncertainty Example

Kondisi alamiah Kondisi alamiah Pasar sesuai

Pasar sesuai Pasar tidakPasar tidak MaximumMaximum MinimumMinimum RowRow Alternatives

Alternatives HarapanHarapan sesuaisesuai in Rowin Row in Rowin Row AverageAverage Desain Desain UMPC UMPC $200,000$200,000 --$180,000$180,000 $200,000$200,000 --$180,000$180,000 $10,000$10,000 Desain Desain Tablet PC Tablet PC $100,000$100,000 --$20,000$20,000 $100,000$100,000 --$20,000$20,000 $40,000$40,000 Do nothing Do nothing $0$0 $0$0 $0$0 $0$0 $0$0 1.

1. MaximaxMaximax choice is to construct a choice is to construct a UMPC DesignUMPC Design 2.

2. MaximinMaximin choice is to do nothingchoice is to do nothing 3.

3. Equally likely choice is to construct a Equally likely choice is to construct a Tablet PCTablet PC

Maximax

Maximax MaximinMaximin Equally Equally likely likely

Beresiko

Beresiko

;

; Each possible state of nature has an Each possible state of nature has an assumed probability

assumed probability

;

; States of nature are mutually exclusiveStates of nature are mutually exclusive

;

; Probabilities must sum to 1Probabilities must sum to 1

;

; Determine the expected monetary value Determine the expected monetary value (EMV) for each alternative

Expected Monetary Value

Expected Monetary Value

EMV (Alternative i) =

EMV (Alternative i) = (Payoff of 1(Payoff of 1stst _{state of state of}

nature) x (Probability of 1 nature) x (Probability of 1stst

state of nature) state of nature) +

+ (Payoff of 2(Payoff of 2ndnd _{state of state of}

nature) x (Probability of 2 nature) x (Probability of 2ndnd

state of nature) state of nature) +…+

+…+ (Payoff of last state of (Payoff of last state of nature) x (Probability of nature) x (Probability of last state of nature)

EMV Example

EMV Example

1. 1. EMV(EMV(AA₁₁) = (.5)($200,000) + (.5)() = (.5)($200,000) + (.5)(--$180,000) = $10,000$180,000) = $10,000 2. 2. EMV(EMV(AA₂₂) = (.5)($100,000) + (.5)() = (.5)($100,000) + (.5)(--$20,000) = $40,000$20,000) = $40,000 3. 3. EMV(EMV(AA₃₃) = (.5)($0) + (.5)($0) = $0) = (.5)($0) + (.5)($0) = $0 Kondisi Alamiah Kondisi Alamiah Pasar sesuai

Pasar sesuai Pasar tidakPasar tidak Alternatives

Alternatives HarapanHarapan sesuai harapansesuai harapan Desain UMPC

Desain UMPC (A1)(A1) $200,000$200,000 --$180,000$180,000 Desain Tablet PC

Desain Tablet PC (A2)(A2) $100,000$100,000 --$20,000$20,000 Do nothing (A3) Do nothing (A3) $0$0 $0$0 Probabilities Probabilities .50.50 .50.50 Table A.3 Table A.3

EMV Example

EMV Example

1. 1. EMV(EMV(AA₁₁) = (.5)($200,000) + (.5)() = (.5)($200,000) + (.5)(--$180,000) = $10,000$180,000) = $10,000 2. 2. EMV(EMV(AA₂₂) = (.5)($100,000) + (.5)() = (.5)($100,000) + (.5)(--$20,000) = $40,000$20,000) = $40,000 3. 3. EMV(EMV(AA₃₃) = (.5)($0) + (.5)($0) = $0) = (.5)($0) + (.5)($0) = $0 Kondisi Alamiah Kondisi Alamiah Pasar

Pasar Pasar tidakPasar tidak Alternatives

Alternatives Sesuai Sesuai Harapan Sesuai HarapanHarapan Sesuai Harapan Desain UMPC

Desain UMPC (A1)(A1) $200,000$200,000 --$180,000$180,000 Desain Tablet PC

Desain Tablet PC (A2)(A2) $100,000$100,000 --$20,000$20,000 Do nothing (A3) Do nothing (A3) $0$0 $0$0 Probabilities Probabilities .50.50 .50.50 Best Option Table A.3 Table A.3

Kepastian

Kepastian

;

; Is the cost of perfect information

Is the cost of perfect information

worth it?

worth it?

;

; Determine the expected value of

Determine the expected value of

perfect information (EVPI)

Expected Value of

Expected Value of

Perfect Information

Perfect Information

EVPI is the difference between the payoff EVPI is the difference between the payoff under certainty and the payoff under risk under certainty and the payoff under risk

EVPI = EVPI = –– Expected value Expected value with perfect with perfect information information Maximum Maximum EMV EMV

Expected value with Expected value with perfect information perfect information (EVwPI)

(EVwPI)

=

= (Best outcome or consequence for 1(Best outcome or consequence for 1stst _{state state}

of nature) x (Probability of 1

of nature) x (Probability of 1stst _{state of nature)state of nature)}

+

+ Best outcome for 2Best outcome for 2ndnd _{state of nature) state of nature)}

x (Probability of 2

x (Probability of 2ndnd _{state of nature)state of nature)}

+

+ … + Best outcome for last state of nature) … + Best outcome for last state of nature) x (Probability of last state of nature)

EVPI Example

EVPI Example

1.

1. Hasil terbaik untuk kondisi alamiah Pasar Hasil terbaik untuk kondisi alamiah Pasar yang sesuai Harapan adalah Desain

yang sesuai Harapan adalah Desain UMPC dengan

UMPC dengan payoff payoff of of $200,000$200,000. . Hasil Hasil terbaik untuk Pasar yang Tidak sesuai terbaik untuk Pasar yang Tidak sesuai Harapan adalah “

Harapan adalah “do do nothing” nothing” dengandengan payoff of payoff of $0$0.. Expected value Expected value with perfect with perfect information information (EVwPI) (EVwPI) = ($200,000)(.50) + ($0)(.50) = $100,000 = ($200,000)(.50) + ($0)(.50) = $100,000

EVPI Example

EVPI Example

2.

2. MMaximumaximum EMV is EMV is $40,000$40,000, , yang adalah yang adalah hasil harapan terbaik tanpa informasi hasil harapan terbaik tanpa informasi sempurna. Sehingga

sempurna. Sehingga::

= $100,000

= $100,000 –– $40,000 = $60,000$40,000 = $60,000

EVPI = EVwPI

EVPI = EVwPI –– Maximum Maximum EMV

EMV

The most the company should pay for The most the company should pay for

perfect information is

Pohon Keputusan

Pohon Keputusan

;

; Information in decision tables can be Information in decision tables can be displayed as decision trees

displayed as decision trees

;

; A decision tree is a graphic display of the A decision tree is a graphic display of the decision process that indicates decision decision process that indicates decision alternatives, states of nature and their alternatives, states of nature and their respective probabilities, and payoffs for respective probabilities, and payoffs for each combination of decision alternative each combination of decision alternative and state of nature

and state of nature

;

; Appropriate for showing sequential Appropriate for showing sequential decisions

Decision Trees

Decision Trees

Pohon Keputusan

Pohon Keputusan

1.

1. Mendefinisikan MasalahMendefinisikan Masalah 2.

2. Menggambar Pohon KeputusanMenggambar Pohon Keputusan 3.

3. Menentukan Peluang bagi Kondisi Menentukan Peluang bagi Kondisi Alamiah

Alamiah 4.

4. Memperkirakan imbalan bagi setiap Memperkirakan imbalan bagi setiap kombinasi alternatif keputusan dan kombinasi alternatif keputusan dan kondisi alamiah yang mungkin

kondisi alamiah yang mungkin 5.

5. Menyelesaikan permasalahan dengan Menyelesaikan permasalahan dengan mengerjakan dari belakang ke depan mengerjakan dari belakang ke depan melalui perhitungan EMV untuk masing melalui perhitungan EMV untuk masing--masing titik kondisi alamiah.

Decision Tree Example

Decision Tree Example

= (.5)($200,000) + (.5)(

= (.5)($200,000) + (.5)(--$180,000)$180,000)

EMV for node 1

= $10,000

EMV for node 2

= $40,000 = (.5)($100,000) + (.5)(= (.5)($100,000) + (.5)(--$20,000)$20,000) Payoffs Payoffs $200,000 $200,000 --$180,000$180,000 $100,000 $100,000 --$20,000$20,000 $0 $0 Desain Desain Tablet PC Tablet PC

Pasar sesuai harapan Pasar sesuai harapan (.5)(.5)

Pasar tidak sesuai Pasar tidak sesuai (.5)(.5)

1

Pasar sesuai harapan Pasar sesuai harapan (.5)(.5)

Pasar tidak sesuai Pasar tidak sesuai (.5)(.5)

2

Figure A.2 Figure A.2

Complex

Complex

Decision

Decision

Tree

Tree

Example

Example

Figure A.3 Figure A.3

Complex Example

Complex Example

1.

1. Given favorable survey resultsGiven favorable survey results

EMV(2) = (.78)($190,000) + (.22)(

EMV(2) = (.78)($190,000) + (.22)(--$190,000) = $106,400$190,000) = $106,400 EMV(3) = (.78)($90,000) + (.22)(

EMV(3) = (.78)($90,000) + (.22)(--$30,000) = $63,600$30,000) = $63,600

The EMV for no plant

The EMV for no plant = = --$10,000$10,000 so, so, if the survey results are favorable, if the survey results are favorable, build the large plant

Complex Example

Complex Example

2.

2. Given negative survey resultsGiven negative survey results

EMV(4) = (.27)($190,000) + (.73)(

EMV(4) = (.27)($190,000) + (.73)(--$190,000) = $190,000) = --$87,400$87,400 EMV(5) = (.27)($90,000) + (.73)(

EMV(5) = (.27)($90,000) + (.73)(--$30,000) = $2,400$30,000) = $2,400

The EMV for no plant

The EMV for no plant = = --$10,000$10,000 so, so, if the survey results are negative, if the survey results are negative, build the small plant

Complex Example

Complex Example

3.

3. Compute the expected value of the Compute the expected value of the market survey

market survey

EMV(1) = (.45)($106,400) + (.55)($2,400) = $49,200 EMV(1) = (.45)($106,400) + (.55)($2,400) = $49,200

The EMV for no plant

The EMV for no plant = $0= $0 so, given so, given no survey, build the small plant

no survey, build the small plant

4.

4. If the market survey is not conductedIf the market survey is not conducted

EMV(6) = (.5)($200,000) + (.5)(

EMV(6) = (.5)($200,000) + (.5)(--$180,000) = $10,000$180,000) = $10,000 EMV(7) = (.5)($100,000) + (.5)(

The end

The end

Pokok Bahasan Selanjutnya: Pokok Bahasan Selanjutnya:

Teknik Peramalan Teknik Peramalan (F O R E C A S T I N G) (F O R E C A S T I N G)