DECISION MAKING TOOLS - probolecturing.files.wordpress.com · ;Pengambilan Keputusan dengan Resiko ; Beberapa kondisi alami mungkin terjadi ; Tetapi masingTetapi masing--masing pilihan

© 2008 Prentice Hall, Inc. A – 1

Decision MakingDecision MakingModule based on Module based on Operation Management, 9eOperation Management, 9e

PowerPoint presentation to accompany PowerPoint presentation to accompany HeizerHeizer/Render /Render Lecturer: F. Priyo Suprobo, ST, MTLecturer: F. Priyo Suprobo, ST, MT

Product Design of Product Design of ITATSITATS


PermasalahanPermasalahanKonsultanKonsultan desaindesain HCIDHCID--ITATSITATS bekerjabekerja untukuntuk HealthyHealthy PillowPillowCompanyCompany sedangsedang mengusulkanmengusulkan rancanganrancangan AlasAlas TidurTidur KesehatanKesehatanyangyang mutakhirmutakhir dengandengan beberapabeberapa pilihanpilihan.. BekerjasamaBekerjasama dengandengantenagatenaga pemasaranpemasaran HealthyHealthy PillowPillow dirumuskanlahdirumuskanlah beberapabeberapaalternatifalternatif berikutberikut peluangpeluang keberhasilannyakeberhasilannya sebagaisebagai berikutberikut::

Selanjutnya, terhadap alternatif yang ada, apakah Selanjutnya, terhadap alternatif yang ada, apakah saran Anda sebagai staf HCIDsaran Anda sebagai staf HCID--ITATS untuk Healthy ITATS untuk Healthy Pillow ini?Pillow ini?


OutlineOutline

Proses KeputusanProses KeputusanDasarDasar--Dasar Pengambilan Dasar Pengambilan KeputusanKeputusanTabel KeputusanTabel Keputusan


Outline Outline –– ContinuedContinuedTipe Pengambilan KeputusanTipe Pengambilan Keputusan

Pengambilan Keputusan dalam Pengambilan Keputusan dalam KetidakpastianKetidakpastianPengambilan Keputusan dengan Pengambilan Keputusan dengan ResikoResikoPengambilan Keputusan dalam Pengambilan Keputusan dalam KepastianKepastianExpected Value of Perfect Expected Value of Perfect Information (EVPI)Information (EVPI)


Outline Outline –– ContinuedContinued

Pohon KeputusanPohon KeputusanPohon Keputusan SederhanaPohon Keputusan SederhanaPohon Keputusan yang lebih Pohon Keputusan yang lebih KompleksKompleks


Learning ObjectivesLearning ObjectivesWhen 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 sederhanasederhana

2.2. Membangun tabel keputusanMembangun tabel keputusan3.3. Menjelaskan kapan menggunakan Menjelaskan kapan menggunakan

salah satu tipe dalam pengambilan salah satu tipe dalam pengambilan keputusankeputusan

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


Learning ObjectivesLearning ObjectivesWhen 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 KeputusanKeputusan

7.7. Membuat Pohon Keputusan dengan Membuat Pohon Keputusan dengan penyelesaian berurutanpenyelesaian berurutan


The Decision Process in The Decision Process in OperationsOperations

1.1. Clearly define the problems and the Clearly define the problems and the factors that influence itfactors that influence it

2.2. Develop specific and measurable Develop specific and measurable objectivesobjectives

3.3. Develop a modelDevelop a model4.4. Evaluate each alternative solutionEvaluate each alternative solution5.5. Select the best alternativeSelect the best alternative6.6. Implement the decision and set a Implement the decision and set a

timetable for completiontimetable for completion


Fundamentals of Fundamentals of Decision MakingDecision Making

1.1. Terminologi/IstilahTerminologi/Istilah::a.a. AlternativeAlternative –– Sebuah tindakan atau Sebuah tindakan atau

strategi yang dapat dipilih oleh strategi yang dapat dipilih oleh pengambil keputusanpengambil 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 sekalitidak sama sekali


Fundamentals of Fundamentals of Decision MakingDecision Making

2.2. Symbols Symbols dalam Pohon Keputusandalam Pohon Keputusan::a.a. –– Sebuah titik keputusan dimana Sebuah titik keputusan dimana

terdapat satu atau lebih alternatif terdapat satu atau lebih alternatif yang dapat dipilihyang dapat dipilih

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


Decision Tree ExampleDecision Tree Example

Pasar sesuai harapanPasar sesuai harapan

Pasar tidak sesuai harapanPasar tidak sesuai harapan

Pasar sesuai harapanPasar sesuai harapan

Pasar tidak sesuai harapanPasar tidak sesuai harapan

Desain TPCDesain TPC

Titik KeputusanTitik Keputusan Titik Kondisi AlamiTitik Kondisi Alami

Figure A.1Figure A.1


Decision Table ExampleDecision Table Example

Kondisi AlamiKondisi AlamiAlternativesAlternatives Pasar sesuaiPasar sesuai Pasar TidakSesuaiPasar TidakSesuai

Desain UMPCDesain UMPC $200,000$200,000 ––$180,000$180,000Desain Tablet PCDesain Tablet PC $100,000$100,000 ––$ 20,000$ 20,000Do nothingDo nothing $ 0$ 0 $ 0$ 0

Table A.1Table A.1


DecisionDecision--Making Making EnvironmentsEnvironments

Pengambilan Keputusan dalam KetidakpastianPengambilan Keputusan dalam KetidakpastianKondisi alami tidak dapat diperkirakanKondisi alami tidak dapat diperkirakan

Pengambilan Keputusan dengan ResikoPengambilan Keputusan dengan ResikoBeberapa kondisi alami mungkin terjadiBeberapa kondisi alami mungkin terjadiTetapi masingTetapi masing--masing pilihan tetap berpeluangmasing pilihan tetap berpeluang

Pengambilan Keputusan dalam KepastianPengambilan Keputusan dalam KepastianKondisi alami diketahui pastiKondisi alami diketahui pasti


KetidakpastianKetidakpastian

1.1. MaximaxMaximaxFind the alternative that maximizes Find the alternative that maximizes the maximum outcome for every the maximum outcome for every alternativealternativePick the outcome with the maximum Pick the outcome with the maximum numbernumberHighest possible gainHighest possible gainThis is viewed as an optimistic This is viewed as an optimistic approachapproach



2.2. MaximinMaximinFind the alternative that maximizes Find the alternative that maximizes the minimum outcome for every the minimum outcome for every alternativealternativePick the outcome with the minimum Pick the outcome with the minimum numbernumberLeast possible lossLeast possible lossThis is viewed as a pessimistic This is viewed as a pessimistic approachapproach



3.3. Equally Equally likelylikely (Sama rata)(Sama rata)Find the alternative with the highest Find the alternative with the highest average outcomeaverage outcomePick the outcome with the maximum Pick the outcome with the maximum numbernumberAssumes each state of nature is Assumes each state of nature is equally likely to occurequally likely to occur


Uncertainty ExampleUncertainty ExampleKondisi alamiahKondisi alamiah

Pasar sesuaiPasar sesuai Pasar tidakPasar tidak MaximumMaximum MinimumMinimum RowRowAlternativesAlternatives HarapanHarapan sesuaisesuai in Rowin Row in Rowin Row AverageAverageDesain Desain

UMPCUMPC $200,000$200,000 --$180,000$180,000 $200,000$200,000 --$180,000$180,000 $10,000$10,000DesainDesain

Tablet PCTablet PC $100,000$100,000 --$20,000$20,000 $100,000$100,000 --$20,000$20,000 $40,000$40,000Do nothingDo 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 Design2.2. MaximinMaximin choice is to do nothingchoice is to do nothing3.3. Equally likely choice is to construct a Equally likely choice is to construct a Tablet PCTablet PC

MaximaxMaximax MaximinMaximin Equally Equally likelylikely


BeresikoBeresiko

Each possible state of nature has an Each possible state of nature has an assumed probabilityassumed probabilityStates of nature are mutually exclusiveStates of nature are mutually exclusiveProbabilities must sum to 1Probabilities must sum to 1Determine the expected monetary value Determine the expected monetary value (EMV) for each alternative(EMV) for each alternative


Expected Monetary ValueExpected Monetary Value

EMV (Alternative i) =EMV (Alternative i) = (Payoff of 1(Payoff of 1stst state of state of nature) x (Probability of 1nature) x (Probability of 1stst

state of nature)state of nature)

++ (Payoff of 2(Payoff of 2ndnd state of state of nature) x (Probability of 2nature) 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)last state of nature)


EMV ExampleEMV Example

1.1. EMV(EMV(AA11) = (.5)($200,000) + (.5)() = (.5)($200,000) + (.5)(--$180,000) = $10,000$180,000) = $10,0002.2. EMV(EMV(AA22) = (.5)($100,000) + (.5)() = (.5)($100,000) + (.5)(--$20,000) = $40,000$20,000) = $40,0003.3. EMV(EMV(AA33) = (.5)($0) + (.5)($0) = $0) = (.5)($0) + (.5)($0) = $0

Kondisi AlamiahKondisi AlamiahPasar sesuaiPasar sesuai Pasar tidakPasar tidak

Alternatives Alternatives HarapanHarapan sesuai harapansesuai harapanDesain UMPCDesain UMPC (A1)(A1) $200,000$200,000 --$180,000$180,000Desain Tablet PCDesain Tablet PC (A2)(A2) $100,000$100,000 --$20,000$20,000Do nothing (A3)Do nothing (A3) $0$0 $0$0ProbabilitiesProbabilities .50.50 .50.50

Table A.3Table A.3


EMV ExampleEMV Example

1.1. EMV(EMV(AA11) = (.5)($200,000) + (.5)() = (.5)($200,000) + (.5)(--$180,000) = $10,000$180,000) = $10,0002.2. EMV(EMV(AA22) = (.5)($100,000) + (.5)() = (.5)($100,000) + (.5)(--$20,000) = $40,000$20,000) = $40,0003.3. EMV(EMV(AA33) = (.5)($0) + (.5)($0) = $0) = (.5)($0) + (.5)($0) = $0

Kondisi AlamiahKondisi AlamiahPasarPasar Pasar tidakPasar tidak

Alternatives Alternatives Sesuai HarapanSesuai Harapan Sesuai HarapanSesuai HarapanDesain UMPCDesain UMPC (A1)(A1) $200,000$200,000 --$180,000$180,000Desain Tablet PCDesain Tablet PC (A2)(A2) $100,000$100,000 --$20,000$20,000Do nothing (A3)Do nothing (A3) $0$0 $0$0ProbabilitiesProbabilities .50.50 .50.50

Best Option

Table A.3Table A.3


KepastianKepastian

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)perfect information (EVPI)


Expected Value of Expected Value of Perfect InformationPerfect Information

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

EVPI = EVPI = ––Expected value Expected value

with perfect with perfect informationinformation

Maximum Maximum EMVEMV

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 1of 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 2x (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)x (Probability of last state of nature)


EVPI ExampleEVPI Example1.1. 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” dengandenganpayoff of payoff of $0$0..

Expected value Expected value with perfect with perfect informationinformation

(EVwPI)(EVwPI)

= ($200,000)(.50) + ($0)(.50) = $100,000= ($200,000)(.50) + ($0)(.50) = $100,000


EVPI ExampleEVPI Example2.2. MMaximumaximum EMV is EMV is $40,000$40,000, , yang adalah yang adalah

hasil harapan terbaik tanpa informasi hasil harapan terbaik tanpa informasi sempurna. Sehinggasempurna. Sehingga::

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

EVPI = EVwPI EVPI = EVwPI –– Maximum Maximum EMVEMV

The most the company should pay for The most the company should pay for perfect information is perfect information is $60,000$60,000


Pohon KeputusanPohon Keputusan

Information in decision tables can be Information in decision tables can be displayed as decision treesdisplayed as decision treesA 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 natureand state of natureAppropriate for showing sequential Appropriate for showing sequential decisionsdecisions


Decision TreesDecision Trees


Pohon KeputusanPohon Keputusan1.1. Mendefinisikan MasalahMendefinisikan Masalah2.2. Menggambar Pohon KeputusanMenggambar Pohon Keputusan3.3. Menentukan Peluang bagi Kondisi Menentukan Peluang bagi Kondisi

AlamiahAlamiah4.4. Memperkirakan imbalan bagi setiap Memperkirakan imbalan bagi setiap

kombinasi alternatif keputusan dan kombinasi alternatif keputusan dan kondisi alamiah yang mungkinkondisi 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 masingmelalui perhitungan EMV untuk masing--masing titik kondisi alamiah.masing titik kondisi alamiah.


Decision Tree ExampleDecision 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)

PayoffsPayoffs

$200,000$200,000

--$180,000$180,000

$100,000$100,000

--$20,000$20,000

$0$0

DesainDesainTablet PCTablet PC

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

Pasar tidak sesuaiPasar tidak sesuai (.5)(.5)1

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

Pasar tidak sesuaiPasar tidak sesuai (.5)(.5)2



Complex Complex Decision Decision

Tree Tree ExampleExample



Complex ExampleComplex Example1.1. 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) = $106,400EMV(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 plantbuild the large plant


Complex ExampleComplex Example2.2. 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$87,400EMV(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 plantbuild the small plant


Complex ExampleComplex Example3.3. Compute the expected value of the Compute the expected value of the

market surveymarket survey

EMV(1) = (.45)($106,400) + (.55)($2,400) = $49,200EMV(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 plantno 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) = $10,000EMV(7) = (.5)($100,000) + (.5)(EMV(7) = (.5)($100,000) + (.5)(--$20,000) = $40,000$20,000) = $40,000


The endThe end

Pokok Bahasan Selanjutnya:Pokok Bahasan Selanjutnya:Teknik PeramalanTeknik Peramalan

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

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DECISION MAKING TOOLS - probolecturing.files.wordpress.com · ;Pengambilan Keputusan dengan Resiko ; Beberapa kondisi alami mungkin terjadi ; Tetapi masingTetapi masing--masing pilihan