untuk Pemodelan Kebijakan: Suatu Pengantar

08.00 – 10.00 Sesi 3: Inventory Simulation Game 10.00 – 10.15 Rehat kopi 10.15 ... from the basic physics of systems: Insufficient inventory may cause...

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Metodologi System Dynamics (Dinamika Sistem) untuk

Pemodelan Kebijakan: Suatu Pengantar Dr. Muhammad Tasrif Ina Juniarti Hani Rohani Fauzan Ahmad Eva Intan Nurwendah Nurika Lestari Waspada

Pelatihan Analisis Kebijakan Menggunakan Model

System Dynamics Hotel Bumi Sawunggaling Bandung, 14 - 18 Desember 2015

JADWAL ACARA Senin, 14 Desember 2015 07.30 – 08.00 Pendaftaran dan Pembukaan 08.00 – 10.00 Sesi 1: Fenomena 10.00 – 10.15 Rehat kopi 10.15– 12.15 Sesi 2: Struktur, Perilaku dan Analisis Kebijakan 12.15 – 13.00 Ishoma 13.00 – 15.00 Sesi 2: Struktur, Perilaku dan Analisis Kebijakan (lanjutan) 15.00 – 15.15 Rehat kopi 15.15 – 17.15 Sesi 2: Struktur, Perilaku dan Analisis Kebijakan (lanjutan) Selasa 15 Desember 2015 08.00 – 10.00 Sesi 3: Inventory Simulation Game 10.00 – 10.15 Rehat kopi 10.15 – 12.15 Sesi 3: Inventory Simulation Game (lanjutan) 12.15 – 13.00 Ishoma 13.00 – 15.00 Sesi 4: Systems Thinking dan System Dynamics 15.00 – 15.15 Rehat kopi 15.15 – 17.15 Sesi 5: Feedback Loop, Delay dan Nonlinearity Rabu, 16 Desember 2015 08.00 – 10.00 Sesi 5: Feedback Loop, Delay dan Nonlinearity (lanjutan) 10.00 – 10.15 Rehat kopi 10.15 – 12.15 Sesi 6: Perangkat Lunak Simulasi - Powersim Studio 12.15 – 13.00 Ishoma 13.00 – 15.00 Sesi 7: Latihan Simulasi 15.00 – 15.15 Rehat kopi 15.15 – 17.15 Sesi 7: Latihan Simulasi (lanjutan) Kamis, 17 Desember 2015 08.00 – 10.00 Sesi 8: Model 10.00 – 10.15 Rehat kopi 10.15 – 12.15 Sesi 8: Model 12.15 – 13.00 Ishoma 13.00 – 15.00 Sesi 9: Model 15.00– 15.15 Rehat kopi 15.15 – 17.15 Sesi 9: Model

Ketersediaan (Availability) Ketersediaan (Availability) (lanjutan) Energi 1 Energi 1 (lanjutan)

Jumat, 18 Desember 2015 08.00 – 10.00 Sesi 10: Model Energi 2 10.00 – 10.15 Rehat kopi 10.15 – 11.15 Sesi 11: Model Energi 2 (lanjutan) 11.15 – 13.00 Ishoma 13.00 – 14.00 Sesi 12: Model Energi 2 (lanjutan) 14.00 – 15.00 Diskusi 15.00 – 15.30 Penutupan

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Sesi 1 Fenomena

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Outcomes Pada akhir sesi ini, peserta dapat: • memahami konsep suatu fenomena dan sistem; • memahami perbedaan antara fenomena sosial (social phenomenon) dengan fenomena alam atau fenomena fisik (physical phenomenon); dan • mendefinisikan suatu persoalan. 2

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1.1 Fenomena • Fenomena adalah sesuatu yang dapat kita lihat, alami dan rasakan atau something experienced: a fact or occurrence that can be observed.

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Fenomena • Fenomena fisik adalah fenomena yang tidak melibatkan campur tangan manusia atau keputusan manusia (fenomena alam atau fenomena yang dibuat manusia berdasarkan hukum alam)[a natural phenomenon involving the physical properties of matter and energy (physical law)]. • Fenomena sosial adalah segala sesuatu yang dipengaruhi oleh kegiatan atau aktivitas manusia yang diwujudkan oleh keputusan-keputusannya (proses pegambilan keputusan) [anything that influences or is influenced by organisms sufficiently alive to respond to one another (desicion making)]. 4

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Fenomena Alam (Fisik)

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Fenomena Sosial

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Catatan historis dinamika (behavior) perberasan nasional Tabel Produksi dan Konsumsi Beras Nasional Tahun

Produksi

Konsumsi

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1996 1997

25932 24006 26542 27014 27253 28340 29072 29366 29047 31356 31318 33216 31206

24679 25460 26092 26738 27392 28053 28723 29410 30121 30838 31375 33461 33911

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Tabel Produksi dan Konsumsi Beras Nasional Tahun

Produksi

Konsumsi

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

31118 31294 32130 31891 32130 32950 33940 34120 34600 36350 38078 40656

34667 35033 35400 35877 36382 36500 36000 35850 35739 35900 37100 37400 24

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Dinamika (Perilaku) Produksi dan Konsumsi Beras Nasional 45000 40000

35000

Ribu Ton

30000 25000

Produksi

20000

Konsumsi

15000 10000 5000

0

Tahun 25

Suatu fenomena menyangkut 2 hal (aspek): Perilaku (behavior) (2)

(1) Struktur (structure) (unsur pembentuk fenomena dan pola keterkaitan antar unsur tersebut)

(perubahan suatu besaran/variabel dalam suatu kurun waktu tertentu, baik kuantitatif maupun kualitatif)

A Produksi padi (ton/tahun)

D

B

C

Tahun

Fenomena sosial : struktur fisik; dan struktur pembuatan keputusan. Pemahaman hubungan struktur dan perilaku sangat diperlukan dalam mengenali suatu fenomena.

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1.2 Sistem Suatu sistem adalah suatu fenomena yang strukturnya telah diketahui [A phenomenon which its structure has been defined]. Atau Suatu sistem merupakan suatu gabungan dari beberapa bagian yang bekerja untuk tujuan bersama. Suatu sistem dapat terbentuk dari sejumlah orang dan/atau sejumlah komponen fisik [A system means a grouping of parts that operate together for common purpose. A system may include people as well as physical parts]. 27

1.3 Persoalan (Problem) • Suatu fenomena yang kehadirannya tidak diinginkan, contoh: produktivitas padi yang terus menurun, tingkat pengangguran yang terus bertambah. • Suatu fenomena yang ingin diwujudkan. Contoh: suatu target surplus beras yang ingin dicapai pada tahun 2014. • (secara praktis) Suatu kesenjangan (gap) antara keadaan sebenarnya (actual state) dengan keadaan yang diinginkan (goal). 28

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1.4 Contoh Kasus

(Sumber: Tesis Emmy Farha Binti Alias, Universiti Putra Malaysia, 2013)

To achieve the intended level of rice production (higher level of self-sufficiency in rice production, currently is about 65-75% of domestic consumption), Malaysia implements a wide range of market intervensions. The policy instruments include among others: • A guaranteed minimum price for paddy; • Various cash and input subsidies to farmers and millers; • Import monopoly; and • Price control for rice. 29

Figure 1.4.1 Planted Paddy area (‘000 ha) and Productivity (t/ha/year) [Source: DoS(2010)]

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Figure 1.4.2 SSL(%) and Fertilizer Subsidy (RM mn/year), 1990-2008 [Source: MoA(2010)]

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For the future, Malaysia needs to dismantle all the policy instruments to comply with the WTO agreement.

Figure 1.4.3 The Global Model

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Sesi 2 Struktur, Perilaku, dan Analisis Kebijakan

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Outcomes Pada akhir sesi ini, peserta dapat: • memahami 2 aspek suatu fenomena (struktur dan perilaku); • mengenali struktur fisik dan struktur pembuatan keputusan; • memahami konsep kompleksitas; dan • memahami prinsip suatu analisis kebijakan (policy analysis). 2

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2.1 Dua Aspek Suatu Fenomena (1) Struktur (structure)

Perilaku (behavior) (2)

(unsur pembentuk fenomena dan pola keterkaitan antar unsur tersebut)

(perubahan suatu besaran/variabel dalam suatu kurun waktu tertentu, baik kuantitatif maupun kualitatif)

A Produksi padi (ton/tahun)

D

B

C

Tahun

Fenomena sosial : struktur fisik; dan struktur pembuatan keputusan. Pemahaman hubungan struktur dan perilaku sangat diperlukan dalam mengenali suatu fenomena.

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A Picture of an Economy •

Households, firms, and governments make economic decisions. Households decide how much of their labor, land, capital, and entrepreneurial ability to sell or rent in exchange for wages, rent, interest, and profits. They also decide how much of their income to spend on the various types of goods and services available. Firms decide how much labor, land, and capital to hire and how much of the various types of goods and services to produce. Governments decide which goods and services they will provide and the taxes that households and firms will pay.



These decisions by households, firms, and governments are coordinated in markets— the goods market and factor markets— that are regulated by rules that governments establish and enforce. In these markets, prices constantly adjust to keep buying and selling plans consistent. 4

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Contoh : Pengisian air ke dalam gelas sampai penuh. (Sumber: “The Fifth Discipline”, Peter M. Senge, 1990)

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Kemungkinan Perilaku A. Jika sumber air mencukupi 8

Volume air dalam gelas yang diinginkan

7 6

Volume air dalam gelas

5 4 3

Aliran air ke dalam gelas

2 1 0 0

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Kemungkinan Perilaku B. Jika sumber air terbatas 8

Volume air dalam gelas yang diinginkan 7 6

Volume air dalam gelas

5 4 3

Aliran air ke dalam gelas

2 1 0 0

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Struktur Fenomena Volume air dalam gelas yang diinginkan

Posisi keran delay

Sumber air

Gap yang dirasakan

Aliran air ke dalam gelas

nonlinier

Volume air dalam gelas

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Struktur Fenomena Struktur Fisik Volume air dalam gelas yang diinginkan

Posisi keran delay

Sumber air

Gap yang dirasakan

Aliran air ke dalam gelas

nonlinier

Volume air dalam gelas

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Struktur Fenomena Struktur Fisik Volume air dalam gelas yang diinginkan

Posisi keran delay

Sumber air

Gap yang dirasakan

Struktur Pembuatan Keputusan

Aliran air ke dalam gelas

nonlinier

Volume air dalam gelas

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Struktur: (1) pembuatan keputusan; dan (2) fisik. Struktur pembuatan keputusan Keadaan yang diinginkan(goal) Lainnya

Proses pembuatan keputusan

Kaidah (rule)

Informasi

Proses pembuatan keputusan (Teori pembuatan keputusan) Struktur fisik

Keputusan (aksi) Keadaan (actual state)

Hukum alam 11

• Struktur terdiri atas struktur fisik (stok dan jaringan aliran materi) dan struktur pengambilan keputusan (decision-making structure) bermacam aktor di dalam sistem. • Struktur pengambilan keputusan di sini dimaksudkan sebagai kaidah-kaidah pembuatan keputusan dan sumber informasi yang digunakan untuk pembuatan keputusan tersebut. • Oleh karena itu, model untuk analisis kebijakan dalam kasus suatu fenomena sosial haruslah merupakan suatu model dinamik dan mampu merepresentasikan secara relatif cukup rinci (detail) aras-mikro (micro-level) individu dan industri (perusahaan), relasi-relasi fisik dan teknik, dan proses-proses pengambilan keputusan yang digunakan oleh aktor-aktor di dalam sistem. 12

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2.2 Pola Karakteristik Perilaku Sistem • Exponential Growth

• Goal Seeking • S-Shaped Growth • Oscillation • Growth with Overshoot

• Overshoot and Collapse

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2.3 Konsep Kompleksitas (Sterman, J.D., Business Dynamics: Systems Thinking and Modeling for a Complex World, 2004, Mc Graw HIll)

(1) Struktur (unsur pembentuk fenomena dan pola keterkaitan antar unsur tersebut)

Perilaku (2) (perubahan suatu besaran/variabel dalam suatu kurun waktu tertentu, baik kuantitatif maupun kualitatif) Orang Miskin (A)

A

D

B

C

Waktu

Fenomena Sosial: Struktur fisik; dan struktur pembuatan keputusan. 14

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• Detail complexity Complexity in terms of the number of elements (components) in a phenomenon (system), or the number of combinations one must consider in making a decision. • Dynamic complexity (Kompleksitas Dinamis) Arises from the relationships (interactions) among the agents (elements) over time.

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Kompleksitas dinamis muncul karena fenomena mempunyai karakteristik: • Dynamic Heraclitus said, “All is change.” What appears to be unchanging is, over alonger time horizon, seen to vary. Change in systems occurs at many time scales, and these different scales sometimes interact. A star evolves over billions of years as it burns its hydrogen fuel, then can explode as a supernova in seconds. Bull markets can go on for years, then crash in a matter of hours.

• Tightly coupled The actors in the system interact strongly with one another and with the natural world. Everything is connected to everything else. As a famous bumper sticker from the 1960s proclaimed, “You can’t do just one thing.” 16

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• Governed by feedback Because of the tight couplings among actors, our actions feed back on themselves. Our decisions alter the state of the world, causing changes in nature and triggering others to act, thus giving rise to a new situation which then influences our next decisions. Dynamics arise from these feedbacks.

• Nonlinier Effect is rarely proportional to cause, and what happens locally in a system (near the current operating point) often does not apply in distant regions (other states of the system). Nonlinearity often arises from the basic physics of systems: Insufficient inventory may cause you to boost production, but production can never fall below zero no matter how much excess inventory you have. Nonlinearity also arises as multiple factors interact in decision making: Pressure from the boss for greater achievement increases your motivation and effort-up to the point where you perceive the goal to be impossible. Frustration then dominates motivation and you give up or get a new boss. 17

• History-dependent Taking one road often precludes taking others and determines where you end up (path dependence). Many actions are irreversible: You can’t unscramble an egg (the second law of thermodynamics). Stocks and flows (accumulations) and long time delays often mean doing and undoing have fundamentallydifferent time constants: During the 50 years of the Cold War arms race the nuclear nations generated more than 250 tons of weapons-grade plutonium (239Pu)T. he half life of 239Pu is about 24,000 years.

• Self Organizing The dynamic of systems arise spontaneously from their internal structure. Often, small, random pertubations are amplified and molded by feedback structure, generating patterns in space and time and creating path dependence.The pattern of stripes on a zebra, the rhythmic contraction of your hearth, the persistent cycles in the real estate market, and structures such as sea shells and markets all emerge spontaneously from the feddbacks among the agents and elements of the system. 18

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• Adaptive The capabilities and decision rules of the agents in complex systems change over time. Evolution leads to selection and proliferation of some agents while others become extinct. Adaptation also occurs as people learn from experience, especially as they learn new ways to achieve their goals in the face of obstacles. Learning is not always beneficial, however.

• Counterintuitive In complex systems cause and effect are distant in time and space while we tend to look for causes near the events we seek to explain. Our attention is drawn to the symptoms of difficulty rather than the underlying cause. High leverage policies are often not obvious.

• Policy resistant The complexity of the systems in which we are embedded over whelms our ability to understand them. The result: Many seemingly obvious solutions to problems fail or actually worsen the situation. 19

• Characterized by trade-offs Time delays in feedback channels mean the long-run response of a system to an intervention is often different from its short-run response. High leverage policies often cause worse-before-better behavior, while low leverage policies often generate transitory improvement before the problem grows worse.

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2.4 Pertanyaan Terhadap Perilaku (Fenomena) (a) Berapakah nilai (angka) besaran itu pada suatu titik waktu yang akan datang? [point prediction] (prakiraaan, prediksi masa depan)

(b) Mengapa perubahan besaran tersebut seperti itu? (why ?) Dan dengan cara bagaimanakah mengubahnya? (how ?) [behavior prediction] (menyusun strategi dan memformulasikan kebijakan, analisis kebijakan atau policy analysis) 21

Contoh: Dinamika Produksi Gula di Indonesia

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2.5 Strategi dan Kebijakan diwujudkan dengan Strategi diimplementasikan melalui berbagai Program direalisasikan dengan melaksanakan Aktivitas

Menciptakan kondisi & iklim yang mendukung perwujudan dan pelaksanaan

Tujuan

Kebijakan

diungkapkan dalam bentuk

•Policy statements •Policy instruments •Policy measures

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• Strategi (strategy) Sebuah rencana (metode) aksi untuk mencapai suatu tujuan tertentu [a plan (method) of action to achieve a particular goal (aim)]

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Kebijakan Petunjuk-petunjuk (directives) yang dikeluarkan dan disebarluaskan (oleh pemerintah) dengan tujuan: • menciptakan serta membangun iklim dan kondisi yang perlu untuk mendukung (to facilitate) pelaksanaan strategi; • memberikan kepastian kepada unsur-unsur dunia usaha, masyarakat luas, dan peyelenggara pemerintahan; tentang arah, ruang lingkup, dan tingkat keleluasaan masing-masing di dalam memilih upaya yang berkaitan dengan strategi tersebut. 25

Pelaksanaan Kebijakan Untuk melaksanakan kebijakan, setelah mengeluarkan kebijakan (pernyataan), policy measures harus dibentuk: • bentuk, rumuskan, dan keluarkan instrumen-instrumen kebijakan (hukum, peraturan, petunjuk-petunjuk); • bentuk dan dirikan badan-badan administratif dan prosedurprosedur untuk mencatat (to administer) kegiatan-kegiatan yang berkaitan dengan pelaksanaan kebijakan; dan • alokasikan sumberdaya (dana, manusia, fasilitas) untuk mendukung badan administratif di atas.

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Proses Pendekatan Perumusan Kebijakan Keinginan ( desired ) yang ingin dicapai 'TUJUAN' [T] Deskriptif Orientatif Arahan dasar bagi tindakan untuk mencapai tujuan

Arahan-arahan yang perlu untuk mendukung S & P

informasi

'KEBIJAKAN' [K] Orientatif, Preskriptif

Informasi yang relevan (Basis informasi untuk mengidentifikasi dan memformulasikan T,S, P, dan K)

informasi

'STRATEGI' [S] Preskriptif Kegiatan dan rencana untuk merealisasikan strategi 'PROGRAM' [P] Preskriptif

informasi Pengamatan analitik tentang dunia nyata (deskriptif)

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Kebijakan Publik [Wibawa, Samodra (2011), Politik Perumusan Kebijakan Publik, Graha Ilmu – Yogyakarta]

Kebijakan publik adalah keputusan suatu “sistem politik” negara, provinsi, kabupaten dan desa, atau RW dan RT untuk/dalam/guna mengelola suatu masalah (persoalan) atau memenuhi suatu kepentingan publik, di mana pelaksanaan keputusan itu membutuhkan dikerahkannya sumberdaya milik semua warga (publik) sistem politik tersebut. [UUD, Keppres, Permen, Perdes (Peraturan desa), ataupun peraturan RT (Rumah Tangga)] 28

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2.6 Logical framework (approach) • Fenomena Gunung Es (The Iceberg Phenomenon) Fenomena gunung es (the iceberg) ini menggambarkan bahwa sturktur yang sistematis merupakan fondasi terbentuknya suatu pola (patterns) dan kejadian (events). Namun struktur sistematis tersebut sulit untuk dilihat. Sering kali kita hanya melihat kejadiannya saja (puncak dari gunung es), dan hal tersebut menjadi dasar pengambilan keputusan. Padahal kejadian (events) hanyalah merupakan akibat (hasil) suatu struktur. Sehingga keputusan yang dibuat berdasarkan kejadian (events) tidak akan menyelesaikan suatu persoalan. Events

Patterns

Structure

Ability to intervene (create changes) (Sumber: Innovation Associates) 29

• Tingkatan Pemahaman (Levels of understanding) Tindakan Kejadian

Pola

Struktur

Reaktif

Waktu Saat ini

Mengamati kejadian

Mengamati pola perubahan kejadian

Adaptif

Perubahan

Cara Pemahaman

Masa depan

Causal loop diagrams dan metode systems thinking lainnya

Pertanyaan yang dapat diajukan “Bagaimana cara tercepat untuk merespon kejadian ini?” “Seperti apa kecenderungan dan pola dari kejadian tersebut, apakah terdapat pengulangan?” “Struktur seperti apakah yang menyebabkan terbentuknya pola tersebut?”

Sumber : Anderson, Virginia and Lauren Johnson, 1997: Systems Thinking Basics: From 30 Concepts to Causal Loops, Pegasus Communications, Inc. MA USA.

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2.7 Model Untuk Analisis Kebijakan

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Kerangka Pemikiran (Pendekatan) • Pemodelan kebijakan (policy modelling) Policy (intervention)?

Real world (fenomena) Unknown process

Real world decisions

Real world history

Model structure

Model behavior Simulation

Model

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• Model suatu fenomena adalah deskripsi (penjelasan atau gambaran) struktur fenomena tersebut yang dinyatakan (diungkapkan) menggunakan bentukbentuk media yang dapat dikomunikasikan. • Iconic model (patung dan maket), graphical model (grafik dan gambar), mathematical model (persamaan matematik), tabular model (tabel inputoutput/tabel I-O yang menyatakan transaksi antarindustri dalam suatu perekonomian), dan computer model (model matematik yang dapat dioperasikan atau disimulasikan). 33

• Setiap manusia secara naluriah menggunakan suatu model untuk membuat suatu keputusan (kebijakan), model mental. Model mental tidak lengkap dan kabur. Konsep sistem dan interpretasi terhadap hubungan-hubungan yang ada di dalam sistem, tidak kita miliki secara lengkap. Selanjutnya, model mental sering kali tidak adaptif terhadap konsekuensikonsekuensi dinamis yang muncul. • “....... the human mind is not adapted to interpreting how social systems behave. Our social systems belong to the class called multiloop nonlinear feedback systems.” (Forrester, 1970) 34

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Keputusan berdasarkan model mental,

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hasil yang tidak diharapkan!

Dibutuhkan suatu model eksplisit ??? 36

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Prinsip-Prinsip Pemodelan Kebijakan • Model yang memenuhi syarat dan mampu dijadikan sarana analisis untuk merumuskan (merancang) kebijakan haruslah merupakan suatu wahana untuk menemukan jalan dan cara intervensi yang efektif dalam suatu sistem (fenomena). • Melalui jalan dan cara intervensi inilah perilaku sistem yang diinginkan dapat diperoleh (perilaku sistem yang tidak diinginkan dapat dihindari). • Model yang dibentuk untuk tujuan seperti di atas haruslah memenuhi syarat-syarat berikut: 37

• karena efek suatu intervensi (kebijakan), dalam bentuk perilaku, merupakan suatu kejadian berikutnya; maka untuk melacaknya, unsur (elemen) waktu perlu ada (dynamic); • mampu mensimulasikan bermacam intervensi dan dapat memunculkan perilaku sistem karena adanya intervensi tersebut; • memungkinkan mensimulasikan suatu intervensi yang efeknya dapat berbeda secara dramatik: (1) dalam konteks waktu (efek jangka pendek vs jangka panjang, trade offs in time), dan (2) dalam konteks sektoral (efek memperbaiki performance suatu sektor yang berakibat memperburuk performance sektor yang lain, trade offs between sectors); disebut dengan istilah dynamic complexity (kompleksitas dinamik); • perilaku sistem di atas dapat merupakan perilaku yang pernah dialami dan teramati (historis) ataupun perilaku yang belum pernah teramati (pernah dialami tetapi tidak teramati atau belum pernah dialami tetapi kemungkinan besar terjadi); dan • mampu menjelaskan mengapa (why) suatu perilaku tertentu (transisi yang sukar misalnya) dapat terjadi. 38

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Prinsip-Prinsip Membuat Model Dinamik (Sterman, 1981) • • • •

• •

Keadaan yang diinginkan dan keadaan yang terjadi harus secara eksplisit dinyatakan dan dibedakan di dalam model; Adanya struktur stok dan aliran dalam kehidupan nyata harus dapat direpresentasikan di dalam model; Aliran-aliran yang secara konseptual berlainan cirinya harus secara tegas dibedakan di dalam menanganinya; Hanya informasi yang benar-benar tersedia bagi aktor-aktor di dalam sistem yang harus digunakan dalam pemodelan keputusankeputusannya; Struktur kaidah pembuatan keputusan di dalam model haruslah sesuai (cocok) dengan praktek-praktek manajerial; dan Model haruslah robust dalam kondisi-kondisi ekstrem. 39

Kesahihan (validity) Model • Dalam hubungannya dengan kesahihan (validity) model, suatu model haruslah sesuai (cocok) dengan kenyataan empirik (realitas) yang ada. • Model merupakan hasil dari suatu upaya untuk membuat tiruan kenyataan tersebut (Burger, 1966). • Upaya pemodelan haruslah memenuhi (sesuai dengan) metode ilmiah. Saeed (1984) telah melukiskan metode ilmiah ini berdasarkan kepada konsep penyangkalan (refutation) Popper (1969). • Metode ini menyaratkan bahwa suatu model haruslah mempunyai banyak titik kontak (points of contact) dengan kenyataan (reality) dan pembandingan yang berulang kali antara model dengan dunia nyata (real world) melalui titik-titik kontak tersebut haruslah membuat model menjadi robust. 40

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Metode Ilmiah (Saeed, 1984) Decision rules experienced

Patterns recognized Unknown process

Induction

Real world decisions

(epistemological: how our knowledge claims could be justified)

Real world history Contact point for comparison

Model structure

Model behavior Deductive logic 41

Usaha pertama dari penyelidikan ilmiah adalah upaya untuk memahami bagaimana suatu perilaku dunia nyata muncul dari strukturnya. Karena tidak ada cara langsung yang dapat digunakan untuk mengetahuinya, suatu model yang mewakili struktur dunia nyata itu harus dikonstruksikan dan perilakunya kemudian diperoleh melalui logika deduktif. Struktur model ini didapat melalui suatu proses induksi yang didasarkan kepada pengetahuan empirik tentang dunia nyata tersebut. Pembandingan-pembandingan baik struktur maupun perilaku model dengan struktur dan perilaku dunia nyata akan menegakkan kepercayaan dalam model, dan pada gilirannya kepercayaan itu akan menjadi dasar kesahihan model tersebut (Kemeny, 1959). 42

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Analisis FENOMENA

Dengan melakukan

MEMANFAATKAN

ANALISIS

METODOLOGI MENGOPERASIKAN

diketahui

STRUKTUR •unsur pembentuk •pola keterkaitan

FUNGSI-FUNGSI YANG DAPAT DITEGAKKAN Dapat dilacak dan diketahui

POLA LAKU (behavior pattern)

MENGENDALIKAN

Dapat dilacak dan digagaskan cara untuk

MEMBENTUK ATAU MENCIPTAKAN STRUKTURSTRUKTUR BARU DGN MENGUBAH STRUKTUR ATAU MENSINTESIS DGN STRUKTUR LAIN

Sumber: Sasmojo (2000)

FENO MENA LAIN

43

Dua (2) kesukaran: 1) menentukan batas-batas model (model boundary); dan 2) menentukan struktur pembuatan keputusan.

Saeed (1982): • Pendekatan kotak hitam (black box approach), hubunganhubungan struktural biasanya dicari melalui suatu proses deduksi dari data historis tentang perilaku sistem. Penentuan variabel-variabel yang penting yang harus masuk dalam model ditentukan melalui pengujian-pengujian statistik berdasarkan data historis perilaku sistem tersebut. Menurut Black (1982), pendekatan ini sering menimbulkan kesalahan-kesalahan spesifikasi dan identifikasi struktur sistem; karena adanya penyimpangan (bias) data. 44

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Alternatif lain adalah memodelkan struktur proses pembuatan keputusan aktor-aktor dalam sistem (fenomena) berdasarkan struktur informasi sistem yang di dalamnya terdapat aktor-aktor, sumber-sumber informasi, dan jaringan aliran informasi yang menghubungkan keduanya. • analogi fisik, sumber informasi merupakan suatu tempat penyimpanan (storage/stock), sedangkan keputusan merupakan aliran yang masuk ke atau keluar dari tempat penyimpanan itu. • analogi matematik, sumber informasi dinyatakan sebagai variabel keadaan (state variable), sedangkan keputusan merupakan turunan (derivative) variabel keadaan tersebut. 45

Proses Pembuatan Keputusan

Informasi lingkungan

Informasi yang terakumulasi dalam sistem

Pembaharuan informasi

Informasi baru yang muncul Aktor-aktor

Aksi-aksi

46

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Struktur: (1) pembuatan Keputusan; dan (2) fisik. Struktur pembuatan keputusan Keadaan yang diinginkan(goal) Lainnya

Proses pembuatan keputusan

Kaidah (rule)

Informasi

Proses pembuatan keputusan (Teori pembuatan keputusan) Struktur fisik

Keputusan (aksi) Keadaan (actual state)

Hukum alam 47

Struktur fisik dan struktur pembuatan keputusan Proses pembuatan keputusan menyangkut fenomenafenomena yang dinamis. Fenomena dinamis ini dimunculkan oleh adanya struktur fisik dan struktur pembuatan keputusan yang saling berinteraksi. • Struktur fisik dibentuk oleh akumulasi (stok) dan jaringan aliran orang, barang, energi, dan bahan. • Struktur pembuatan keputusan dibentuk oleh akumulasi (stok) dan jaringan aliran informasi yang digunakan oleh aktor-aktor (manusia) dalam sistem yang menggambarkan kaidah-kaidah proses pembuatan keputusannya. 48

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Sesi 3 Inventory Simulation Game

1

Outcomes Memahami bahwa struktur (fisik dan pengambilan keputusan) menentukan perilaku

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3.1 The Inventory Game (1) • The Inventory Game is one of a number of management simulators developed at MIT's Sloan School of Management for these purposes. The game was developed by Sloan's System Dynamics Group in the early 1960s as part of Jay Forrester's research on industrial dynamics. Its has been played all over the world by thousands of people ranging from high school students to chief executive officers and government officials. • The game is played by teams of at least four players, often in heated competition, and takes from one to one and a half hours to complete. A debriefing session of roughly equivalent length typically follows to review the results of each team and discuss the lessons involved. 3

The Inventory Game (2) • The purpose of the game is to understand the distribution side dynamics of a multi-echelon supply chain used to distribute a single item. The aim is to meet customer demand of goods through the distribution side of a multi-stage supply chain with minimal expenditure on back orders and inventory. • Players can see each other's inventory but only one player sees actual customer demand. Verbal communication between players is against the rules so feelings of confusion and disappointment are common. • Players look to one another within their supply chain frantically trying to figure out where things are going wrong. Most of the players feel frustrated because they are not getting the results they want. • Players wonder whether someone in their team did not understand the game or assume customer demand is following a very erratic pattern as backlogs mount and/or massive inventories accumulate. 4

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The Inventory Game (3) • Suatu ilustrasi yang memperlihatkan bahwa perilaku suatu fenomena (sistem) ditentukan terutama oleh struktur internalnya.

Flow of Goods 5

• Setiap policy-maker mempunyai kebebasan sepenuhnya untuk menentukan ordering policy dalam aturan-aturan sebagai berikut ini. 1. Pengiriman barang harus memenuhi semua order, sepanjang stok barang dalam inventory memungkinkan untuk keperluan itu. 2. Diberlakukan struktur biaya (cost): o Carrying cost adalah $ 0.50 per unit/period; dan o Out-of stock costs adalah $ 2.00 per unit/period.

• Agar biaya total minimum, setiap sektor dalam sistem harus berupaya menjaga agar stok dalam inventory-nya seminimum mungkin, tetapi cukup untuk dapat memenuhi permintaan yang boleh jadi berubah. • Bila stok lebih kecil dari kebutuhannya, order harus lebih besar dari tingkat penjualan (pengiriman) rata-rata. Sebaliknya, bila stok lebih besar dari kebutuhannya, order harus lebih kecil dari penjualan (pengiriman) rata-rata. 6

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• Setiap policy-maker harus dapat menjawab 2 (dua) pertanyaan berikut ini. 1. Apakah stok yang dimiliki dalam inventory cukup untuk memenuhi permintaan? (kenyataan) 2. Berapa banyak barang yang harus dipesan ke pemasok dan cukup untuk menghindari terjadinya out-of-stock? (kebijakan atau policy)

7

3.2 Langkah-Langkah Permainan Langkah 1 a.

Kirim barang dari inventory ke sektor sebelah kiri, sesuai dengan permintaan yang tertera di order backlog. Barang diletakkan pada bagian kanan shipping delay sektor sebelah kiri. Bila barang yang harus dikirim tidak tersedia simpan secarik kertas sebagai penanda. (Untuk sektor retailer simpan barang terkirim pada customers decks, sedangkan sektor factory kirim seluruh barang yang berada di inventory ke distributor). b. Jika pengiriman sesuai dengan permintaan, buang catatan pesanan dari order backlog. Jika pengiriman tidak sesuai dengan permintaan, catat kekurangan pengiriman dengan menambahkannya pada order backlog. Langkah 2: Catat jumlah inventory dan order backlog pada formulir yang telah disediakan. Langkah 3: Pindahkan barang-barang pada bagian kirim shipping delay ke inventory. Langkah 4: Pindahkan barang-barang dari sebalah kanan ke sebelah kiri dari shipping delay [termasuk memindahkan barang pada sektor factory: 4(a) and 4(b)].

Langkah 5:Tentukan jumlah barang yang akan dipesan, dan simpan pada bagian kiri dari mail box di sektor sebelah kanan.

Langkah 6: Catat pesanan pada formulir yang disediakan. Langkah 7: Ambil pesanan di mail box, tambahkan dengan jumlah yang tertera di order backlog. ( Sektor retailer ambil pesanan dari orders deck, sedangkan sektor factory langsung memproduksi barang sesuai dengan permintaan dan simpan pada bagian atas dari kotak goods in process).

Langkah 8: Pindahkan slip order dari kiri delay bagian kanan delay mail box. Langkah 9: Kembali ke langkah pertama.

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2 (Record backlog)

5 (Order rate)

7 Orders deck

Order backlog 1(b) Order discard rate 2 (Record inventory)

6 (Record order)

4 3

1(a) Inventory Customers deck

Shipping delay

RETAILER 9

2 (Record backlog)

8

5 (Order rate)

7 Order backlog

1(b) Order discard rate

Mail delay

2 (Record inventory)

6 (Record order)

4

3

1 (a) Inventory Shipments to retailer

Shipping delay

WHOLESALER 10

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2 (Record backlog)

8

5 (Order rate)

7 Order backlog

1(b) Order discard rate

Mail delay

2 (Record inventory)

6 (Record order)

4 3

1 (a) Inventory

Shipments to wholesaler

Shipping delay

DISTRIBUTOR 11

8 7 Orders from distributor

Mail delay

Goods In process

4 (b) 2 (Record inventory) 1 (a) Inventory 4 (a)

Shipments to distributor

FACTORY 12

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Sesi 4 Systems Thinking & System Dynamics

1

Outcomes Pada akhir sesi ini, peserta dapat: • mengenali hubungan sebab akibat; • memahami metodologi pemodelan system dynamics.

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Pengisian air ke dalam gelas sampai penuh (Sumber: “The Fifth Discipline”, Peter M. Senge, 1990)

3

Kemungkinan Perilaku A. Jika sumber air mencukupi 8

Volume air dalam gelas yang diinginkan

7 6

Volume air dalam gelas

5 4 3

Aliran air ke dalam gelas

2 1 0 0

1

2

3

4

5

6

7

8

9

10

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Kemungkinan Perilaku B. Jika sumber air terbatas 8

Volume air dalam gelas yang diinginkan 7 6

Volume air dalam gelas

5 4 3

Aliran air ke dalam gelas

2 1 0 0

1

2

3

4

5

6

7

8

9

10

5

Struktur Fenomena Volume air dalam gelas yang diinginkan

Posisi keran delay

Sumber air

Gap yang dirasakan

Aliran air ke dalam gelas

nonlinier

Volume air dalam gelas

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Struktur Fenomena Struktur Fisik Volume air dalam gelas yang diinginkan

Posisi keran delay

Sumber air

Gap yang dirasakan

Aliran air ke dalam gelas

nonlinier

Volume air dalam gelas

7

Struktur Fenomena Struktur Fisik Volume air dalam gelas yang diinginkan

Posisi keran delay

Sumber air

Gap yang dirasakan

Struktur Pembuatan Keputusan

Aliran air ke dalam gelas

nonlinier

Volume air dalam gelas

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Metodologi Pemodelan Systems Thinking dan System Dynamics Struktur

Perilaku

unsur pembentuk pola keterkaitan antar unsur :

(1) feedback (causal loop) (2) stock (level) dan flow (rate) (3) delay (4) nonlinearity (ontological: the ways reality itself could be)

Pendekatan Struktural Systems Thinking System Dynamics 9

4.1 System Dynamics Methodology

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A. Source: System Dynamics Home Page.htm

11

System Dynamics Methodology • System dynamics is a methodology for studying and managing complex feedback systems, such as one finds in business and other social systems. • In fact it has been used to address practically every sort of feedback system. • While the word system has been applied to all sorts of situations, feedback is differentiating descriptor here. • Feedback refers to the situation of X affecting Y and Y in turn affecting X perhaps through a chain of causes and effects. • One cannot study the link between X and Y and, independently, the link between Y and X and predict how the system behave. Only the study of the whole system as a feedback system will lead to correct results. 12

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What is the relationship of Systems Thinking to System Dynamics? • Systems thinking looks at the same type of problems from the same perspective as does system dynamics. • The two techniques share the same causal loop mapping techniques. • System dynamics takes the additional step of constructing computer simulation models to confirm that the structure hypothesized can lead to the observed behavior and to test the effects of alternative policies on key variables over time. 13

B. Source: Richardson, George P. & Alexander L. Pugh III (1981), Introduction to System Dynamics Modeling with Dynamo, MIT Press/Wright-Allen series in system dynamics.

14

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Overview of the System Dynamics Approach • The system dynamics approach to complex problems focuses on feedback processes. It takes the philosophical position that feedback structures are responsible for the changes we experience over time. The premise is that dynamic behavior is consequence of system structure and will become meaningful and powerful. At this point, it may be treated as a postulate, or perhaps as a conjecture yet to be demonstrated. • As both a cause and a consequence of the feedback perspective, the system dynamics approach tends to look within a system for the sources of its problem behavior. Problems are not seen as being caused by external agents outside the system. 15

• Inventories are not assumed to oscillate merely because consumers periodically vary their orders. A ball does not bounce merely because someone drops it. A pendulum does not oscillate merely because it was displaced from the vertical. The system dynamicist prefers to take the point of view that these systems behave as they do for reasons internal to each system. A ball bounces and a pendulum oscillates because there is something about their internal structure that gives them the tendency to bounce or oscillate. • In practice, this internal point of view results in models of feedback system that bring external agents inside the system. Customers orders become endogenous to a production system, part of the feedback structure of the system. Orders affect production; production affects orders. Part and parcel with the notion of feedback, the endogenous point of view helps to characterize the system dynamics approach. 16

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The are roughly seven stages in approaching a problem from the system dynamics perspective: (1) problem identification and definition; (2) system conceptualization; (3) model formulation; (4) analysis of model behavior; (5) model evaluation; (6) policy analysis; and (7) model use or implementation. 17

• The process begins and ends with understandings of a system and its problems, so it forms a loop, not a linear progression. Figure 4.1 shows these stages and the likely progression through them, together with some arrows that represent the cycling, iterative nature of the process. At a number of stages along the way one’s understanding of the system and the problem are enhanced by the modeling process, and that increased understanding further aids the modeling effort. • Figure 4.1 shows that final policy recommendations from a system dynamics study come not merely from manipulations with the formal model but also from the additional understandings one gains about the real system by iterations at a number of stages in the modeling process. Done properly, a system dynamics study should produce policy recommendations that can be presented, explained, and defended without resorting to the formal model. The model is a means to an end, and that end is understanding. 18

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Policy implementation Understanding of a system

Policy analysis

Problem definition

Simulation

System conceptualization Model formulation

Figure 4.1 Overview of the system dynamics modeling approach 19

Guidelines for Causal-loop Diagrams The apparent simplicity of causal-loop diagram is deceptive. It is easy for would-be modelers to go astray with them. The following suggestion may help to prevent the more common difficulties. 1.

Think of variables in causal-loop diagrams as quantities that can rise or fall, grow or decline, or be up or down. But do not worry if you can not readily think of existing measures of them. Corollaries: a) b) c)

Use nouns or noun phrases in causal-loop diagrams, not verbs. The actions are in the arrows (see Figure 4.2). be sure it is clear what is means to say a variable increases or decreases. (Not attitude toward crime”, but “tolerance for crime”.) Do not use causal-links to mean “and then…..” 20

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Rising orders

Falling inventory

Lengthening delivery delay

Shortening delivery delay

Rising inventory

Falling orders

Not:

But rather:

Inventory

Orders Delivery delay

-

Figure 4.2 Loops illustrating that the action in causal-loop diagram is best left to the arrows 21

2. Identify the units of the variables in causalloop diagram, if possible. If necessary, invent some: some psychological variables might have to be thought of in “stress units” or “pressure units”, for example. Units help to focus the meaning of a phrase in a diagram. 3. Phrase most variables positively (“emotional state” rather than “depression”. It is hard to understand what it to say “depression increases” when testing link and loop polarities. 22

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4. If a link needs explanation, disaggregate it – make it a sequence of links. For example, a study of heroinrelated crime claimed a positive link from heroin price to heroin-related crime. The link is clear if disaggregated as in Figure 4.3 into the sequence of positive links from heroin price to money required per addict, frequency of crimes per addict, and finally heroin-related crime. Some might feel a high price deters addict and so lowers the number of addicts as it well might, but that is another link (see Figure 4.3). 5. Beware of interpreting open loops as feedback loops. Figure 4.3, for example, does not show a feedback loop. 23

Money needed to support habit +

+ Frequency of crimes per addict

Heroin price

+ Heroin-related crime Addicts

+

-

Figure 4.3 Links relating heroin price and crime 24

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4.2 Systems Thinking

25

Systems Thinking (Anderson, Virginia and Lauren Johnson, 1997: Systems Thinking Basics: From Concepts to Causal Loops, Pegasus Communications, Inc. MA USA)

In general, systems thinking is characterized by these principles: (1) thinking of the “big picture”; (2) balancing short-term and long-term perspective; (3) recognizing the dynamic, complex, and interdependent nature of system; (4) taking into account both measurable and non measurable factors; and (5) remembering that we are all part of the systems in which we function, and that we each influence those systems even as we are being influenced by them.

26

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Linear Thinking vs Systems Thinking (Kim, Daniel H., 1997: Introduction to Systems Thinking, Pegasus Communications, Inc. MA USA)

Linear Thinking A

B

C

D

Systems Thinking A

B

C

D

27

Prinsip systems thinking (Senge, 1990) : • To observe the interdependent relationship (influenced and influence or feedback or interdependent), not a direct cause-effect relationships; • To observe the processes of change (the process continues, ongoing processes), not just portraits. 28

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Model yang dibangun melalui suatu analisis struktural (structural analysis), berdasarkan pendekatan systems thinking, dimungkinkan untuk mempunyai titik kontak yang banyak. Dalam paradigma systems thinking, struktur fisik ataupun struktur pengambilan keputusan diyakini dibangun oleh unsur-unsur yang saling-bergantung (interdependent) dan membentuk suatu lingkar tertutup (closed-loop atau feedback loop). Hubungan unsur-unsur yang saling bergantung itu merupakan hubungan sebab-akibat umpan-balik dan bukan hubungan sebab-akibat searah (Senge, 1990). Lingkar umpan-balik ini merupakan blok pembangun (building block) model yang utama. 29

30

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31

4.3 Persediaan Dan Aliran (Teori makroekonomi – edisi ke 5 oleh N. Gegori Mankiw, Harvard University – Penerbit Erlangga 2003, hal 18)

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• Banyak variabel ekonomi mengukur jumlah sesuatu─ jumlah uang, jumlah barang, dan seterusnya. Para ahli ekonomi membedakan antara dua jenis variabel jumlah: persediaan (stocks) dan aliran (flows). Persediaan (stocks) adalah jumlah yang diukur pada titik waktu tertentu, sedangkan aliran (flow) adalah jumlah yang diukur per unit waktu. • Bak mandi, ditunjukkan pada Gambar 4.3.1, adalah contoh klasik yang digunakan untuk menggambarkan persediaan dan aliran. Jumlah air di dalam bak adalah persediaan: yaitu jumlah air di bak mandi pada titik waktu tertentu . Jumlah air yang keluar dari kran adalah aliran: yaitu jumlah air yang sedang ditambahkan ke bak per unit waktu. Catat bahwa kita mengukur persediaan dan aliran dalam unit yang berbeda. Kita berkata bahwa bak mandi berisi 50 galon air, tetapi air yang keluar dari kran adalah 5 galon per menit.

33

Gambar 4.3.1 Persediaan dan Aliran Aliran

Persediaan

Jumlah air di bak mandi adalah persediaan: jumlahnya diukur pada titik waktu tertentu. Jumlah air yang keluar dari kran adalah aliran: jumlahnya diukur per unit waktu.

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• 

  

GDP mungkin adalah variabel aliran paling penting dalam perekonomian: GDP menyatakan berapa banyak uang yang mengalir mengelilingi aliran sirkuler perekonomian per unit waktu. Ketika Anda mendengar seseorang berkata GDP AS adalah $10 triliun, Anda seharusnya mengerti, ini berarti bahwa GDP adalah $10 triliun per tahun. (Demikian pula, kita bisa mengatakan bahwa GDP AS adalah $17.000 per detik.) Persediaan dan aliran seringkali berkaitan. Dalam contoh bak mandi, hubungan ini jelas. Persediaan air di bak menunjukkan akumulasi dari aliran yang keluar dari kran, dan aliran air menunjukkan perubahan dalam persediaan. Ketika membangun teori untuk menjelaskan variabel-variabel ekonomi, seringkali berguna untuk menentukan apakah variabel-variabel itu adalah persediaan atau aliran dan apakah ada hubungan di antara keduanya. Inilah beberapa contoh persediaan dan aliran yang akan kita pelajari dalam bab-bab berikutnya: Kekayaan seseorang adalah persediaan; pendapatan dan pengeluarannya adalah aliran. Jumlah orang yang menganggur adalah persediaan; jumlah orang yang kehilangan pekerjaan mereka adalah aliran. Jumlah modal dalam perekonomian adalah persediaan; jumlah investasi adalah aliran. Utang pemerintah adalah persediaan; defisit anggaran pemerintah adalah aliran. 35

4.4 FOUR-SECTOR FEEDBACK MODEL OF HUMAN LIFESUPPORT SYSTEM (Duncan, 1991)

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SECTOR I. ECOSYSTEM This is the earth’s natural environment comprising all land, water, air, energy & material resources, plants and animals.



SECTOR II. TECHNOLOGY This is the human industrial and consumption system comprising all technology used for agriculture, physical production, transportation, et cetera.



SECTOR III. GOVERNING SYSTEM This is the social regulatory system comprising all human institutions and processes: economic, financial, governmental, judicial, military, educational, religious, et cetera.



SECTOR IV. HUMAN BEINGS This is the global population comprising billions of individual human beings. Genes process hereditary information. Brains process cultural information.

• • • • •

SOLID ARROW : Materials & energy flow DASHED ARROW : Information flow DOTTED ARROW : Human behavior or institutional action ARROW x : Genetic Influence ARROW y : Cultural Influence 37

4.5 Indeks Pembangunan Manusia (IPM) IPM

PENDIDIKAN

KESEHATAN

AMH, Lama sekolah

EKONOMI

UHH

DAYA BELI

AKABA

AKB

AKI

AKK

(ANGKA

(ANGKA

(ANGKA

(ANGKA

KEMATIAN

KEMATIAN

KEMATIAN

KEMATIAN

BALITA)

BAYI)

IBU)

KASAR)

PELAYANAN

LINGKUNGAN

PERILAKU

GENETIK

KESEHATAN 20%

45%

30%

5%

38

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R2 +

IPM

+

+ +

Pendidikan +

Kesehatan +

R5

+

+

Daya beli (perekonomian) + +

+ R1

Perilaku manusia

+

R4

R3

Kualitas SDM + B2

B1

-

+ Lingkungan

Causal loop diagram IPM 39

4.6 Peran beberapa bidang (field) dalam metodologi system dynamics Feedback theory and cybernetics

Principles of selecting information

Computer simulation

Low-cost computation

Principles of structure

MODEL

Traditional management and political leadership

Dynamic behavior and improvement of policies

Information, experience, judgment

40

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Manajemen tradisional (traditional management) beserta pengalamannya tentang dunia nyata merupakan sumber informasi yang mendasar untuk membuat struktur model suatu sistem. Karena semua informasi yang terkandung dalam suatu model mental tidak dapat dimasukkan ke dalam suatu model eksplisit, informasi itu perlu dipilih berdasarkan tingkat kepentingannya dalam fenomena atau gejala yang dianalisis. Teori umpan-balik beserta sibernetika (feedback theory dan cybernetics) memberikan prinsip-prinsip untuk memilih informasi yang relevan dan menyingkirkan informasi yang tidak mempunyai hubungan dengan dinamika-dinamika persoalan. 41

Sekali suatu model dapat diformulasikan, perilaku dinamisnya dapat dipelajari menggunakan simulasi dengan komputer. Simulasi ini sangat membantu dalam upaya kita untuk membandingkan struktur model beserta perilakunya dengan struktur dan perilaku sistem yang sebenarnya, yang pada gilirannya akan meningkatkan keyakinan kita terhadap kemampuan model di dalam mendeskripsikan sistem yang diwakilinya. Keyakinan ini menjadi dasar bagi kesahihan model. Bila kesahihan model telah dapat dicapai, simulasi selanjutnya dapat digunakan untuk merancang kebijakankebijakan yang efektif.

42

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Pada mulanya Forrester menerapkan metodologi system dynamics untuk memecahkan persoalan-persoalan yang terdapat dalam industri (perusahaan). Model-model system dynamics pertama kali ditujukan kepada permasalahan manajemen yang umum seperti fluktuasi inventori, ketidakstabilan tenaga kerja, dan penurunan pangsa pasar suatu perusahaan (lihat Forrester 1961). Perkembangannya terus meningkat semenjak pemanfaatannya dalam persoalan sistem-sistem sosial yang sangat beragam, yang antara lain dapat disimak dari tulisan Forrester dan Hamilton (Forrester 1969, Hamilton et al. 1969, dan Forrester 1971). 43

4.7 Perancangan suatu model System Dynamics Policy evaluation

Principle of feedback loops

Concept from written literature

Policy changes Purpose

Alternative behavior

Model

Mental and written information

Structure

Parameter Behavior Miscellaneous numerical data Discrepancies in behavior

Time-series data

Comparison of model behavior and real-world behavior

44

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empirical evidence

reference mode

dynamic hypothesis

behavior validation

structure validation comparison and reconciliation

comparison and reconciliation model

computer simulation

(methodological: the procedures employed to arrive to such knowledge claims)

policy design 45

4.8 Tests for Building Confidence in System Dynamics Model (Forrester and Senge 1980, Richardson and Pugh 1981):



Test of Model Structure

1. Structure Verification

(Is the model structure consistent with relevant descriptive knowledge of the system?)

2. Parameter Verification

(Are the parameters consistent with relevant descriptive [and numerical, when available] knowledge of system?)

3. Extreme Conditions

(Does each equation make sense even when its inputs take on extreme values?)

4. Structure Boundary Adequacy

(Are the important concepts for addressing the problem endogenous of the model?)

5. Dimensional Consistency

(Is each equation dimensionally consistent without the use of parameters having no real-world counterpart?) 46

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1.

Test of Model Behavior Behavior Reproduction (Does the model endogenously generate the symptoms of

the problem, behavior modes, phasing, frequencies, and other characteristics of the behavior of the real system?) 2.

Behavior Anomaly (Does anomalous behavior arise if an assumption of the model is deleted?)

3. Family Member (Can the model reproduce the behavior of other examples of the systems in the same class as the model?) 4.

Surprise Behavior (Does the model point to the existence of a previously unrecognized mode of behavior in the real system?) 47

5. Extreme Policy (Does the model behave properly when subjected to extreme policies or test inputs?)

6. Behavior Boundary Adequacy (Is the behavior of the model sensitive to the addition or alteration of structure to represent plausible alternative theories?)

7. Behavior Sensitivity (Is the behavior of the model sensitive to plausible variations in parameters?)

8. Statistic Character (Does the output of the model have the same

statistical

character as the “output” of the real system?) 48

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Test of Policy Implications

1. System Improvement (Is the performance of the real system improved through use of the model?)

2. Behavior Prediction (Does the model correctly describe the results of a new policy?)

3. Policy Boundary Adequacy (Are the policy recommendations sensitive to the addition or alteration of structure to represent plausible alternative theories?)

4. Policy Sensitivity (Are the policy recommendations sensitive to plausible variations in parameters?) 49

4.9 Archetypal Structures (System Archetypes) in System Dynamics

(E. F. Wolstenholme: “Towards the definition and use of a core set of archetypal structures in system dynamics” in System Dynamics Review Vol. 19, No. 1, (Spring 2003): 7-26)

System archetypes are introduced as a formal and freestanding way of classifying structures responsible for generic patterns of behavior over time, particularly counter-intuitive behavior. Such “structures” consist of intended actions and unintended reactions and recognize delays in reaction time. System archetypes have an important and multiple role to play in systemic thinking. System archetypes are first and foremost a communications device to share dynamic insights. 50

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4.10 System Dynamics for policy design in terms of System Archetypes • System dynamics consists of five (5) components of system “structure”: (1) processes, created using stock-flow chains; (2) information feedback; (3) policy; (4) time delays; and (5) boundaries. (E. F. Wolstenholme: “Using generic system archetypes to support thinking and modeling” in System Dynamics Review Vol. 20, No. 4, (Winter 2004): 341-356)

51

• Boundaries in system archetypes 1. Organisations are by definition very bounded entities in terms of disciplines, functions, accounting, power, and culture (the existence of boundaries as basic elements of organisational structure). 2. Boundaries are the one facet of organisations that are perhaps changed more often than any other. 3. They are often changed in isolation from strategy and process on the whim of a new top team or political party, usually to impose their own people. 52

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4. Different types of boundaries: a) they may be between the organisation and its environment; b) they may be very physical accounting boundaries between different functional parts of the same organisation; and c) they may be between management teams or indeed mental barriers within individuals. 5. The existence and importance of boundaries within organisations, as a determinant of organisational evolution over time, has to be represented in system archetypes. 53

6. The superimposition of organisational boundaries on system archetypes helps explain why systemic management is so difficult. a) Organisational boundaries highlight dramatically that action and reaction are often instigated from separate sources within organisation. b) Organisational boundaries imply that reactions are often “hidden” from the “view” of the source responsible for the actions. c) Organisational boundaries force system actors to actively confront the need to share information and collaborate to achieve whole system objectives. 54

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4.11 The characteristics of a totally generic two-loop system archetype • The basic structure of a totally generic two-loop system archetype (Figure 4.11.1)

Figure 4.11.1 The basic structure of a totally generic two-loop system archetype

55

• The characteristics of the archetype: 1. It is composed of an intended consequence (ic) feedback loop which results from an action initiated in one sector of an organisation with an intended consequence over time in mind. 2. It contains an unintended consequence (uc) feedback loop, which results from a reaction within another sector of the organisation or outside. 3. There is a delay before the unintended consequence manifests itself. 4. There is an organisational boundary that “hides” the unintended consequence from the “view” of those instigating the intended consequences. 5. That for every “problem” archetype, there is a “solution” archetype. 56

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• Problem archetypes: 1. A problem archetype is one whose net behavior over time is far from that intended by the people creating the ic loop. 2. It should be noted that reactions can arise from the same system participants who instigate the original actions (perhaps due to impatience with the time taken for their original actions to have effect). 3. The reaction may also arise from natural causes. 4. It is more often the case that the reaction comes from other individuals, groups or sectors of the same organisation or from external sources. 5. Almost every action will be countered by a reaction in some other part of the system and hence no one strategy will ever dominate (systems are dynamic, self-organising, and adaptive). 57

• Solution archetypes: 1. The closed-loop solution archetype is to minimise any side effects (a generic two-loop solution archetype is also shown on Figure 4.11.1). 2. The key to identifying solution archetypes lies in understanding both the magnitude of the delay and the nature of the organisational boundary present. 3. Solutions require that system actors, when instigating a new action, should attempt to remove or make more transparent the organisational boundary masking the side effect. 4. Collaborative effort on both sides of the boundary can then be directed at introducing new “solution” feedback loops to counter or unblock the uc loop in parallel with activating the ic loop. 5. The result is that the intended action should be much more robust and capable of achieving its purpose. 58

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4.12 Four generic problem/solution archetypes • Initiating actions for change can be condensed down to one of two kinds. • These are actions that attempt to improve the achievement of an organisation by initiating reinforcing feedback effects and those that attempt to control an organisation by introducing balancing feedback effects. • Reactions can also be condensed to one of the same two kinds. • There are only four totally generic two-loop archetypes possible, arising from the four ways of ordering the two basic types of feedback loops (balancing and reinforcing):

59

(1) Underachievement, intended achievement fails to be realised (Figure 4.12.1).

Figure 4.12.1 Underachievement archetype 60

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(2) Out of control, intended control fails to be realised (Figure 4.12.2).

Figure 4.12.2 Out of control archetype 61

(3) Relative achievement, achievement is only gained at the expense of another (Figure 4.12.3).

Figure 4.12.3 Relative achievement archetype 62

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(4) Relative control, control is only gained at the expense of others (Figure 4.12.4).

Figure 4.12.4 Relative control archetype 63

4.13 Mapping existing semi-generic problem archetypes onto four generic problem archetypes 







Semi-generic archetypes that can be mapped onto the generic underachievement archetype (Figure 4.12.1) are Limits to success, Tragedy of the commons, and Growth and underinvestment. Semi-generic archetypes that can be mapped onto the generic out of control archetype (Figure 4.12.2) are Fixes that fail, Shifting the burden, and Accidental adversaries. The semi-generic archetype which can be mapped onto the generic relative achievement archetype (Figure 4.12.3) is Success to the successful. The semi-generic archetypes which can be mapped onto the generic relative control archetype (Figure 4.12.4) are Escalation and Drifting goals. 64

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4.14 Problem Sederhana: Tangkapan Ikan

65

66

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Lingkungan +

Stok ikan

B2

Densitas Harga energi

+

Tangkapan + ikan +

+

B3

+

+ Peralatan tangkap

-

B1

-

+

Teknologi

R1

+ + Pertambahan Ikan alamiah

+

Peningkatan usaha

R2

Usaha

Biaya

Penghasilan

+

+

+

Keuntungan

Harga ikan

+

-

Struktur fenomena penangkapan ikan 67

Environtment

+ + Fish growth rate Intrinsic growth + +

+

Technology B2

R1

B1

+ +

+

Fish density

Revenue +

Price of fish

-

Flexibility

+ Catchability coefficient

Fish catch +

Fish growth

Effect of density

+

Fish stock

R2

Carrying capacity (fish stock maximum) ++

+

- Profit

R3 Effort

+ +

Change of effort +

Energy price

Cost Cost per trip

+

B3

Fish stock dynamics

68

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References 1. 2.

9.

Burger, Peter L., T. Lockman (1966), The Social Construction of Reality, Allen lane. Dornbusch, Rudiger and Fischer, Stanley (1997). Mulyadi, Julius A. (Alih Bahasa). Makro-ekonomi (Edisi Keempat). Penerbit Erlangga. Duncan, Richard C. (1991), “The Life-Expectancy of Industrial Civilization”, SYSTEM DYNAMICS ’91 Proceedings of the 1991 International System Dynamics Conference, Bangkok-Thailand, August 2730, 1991. Forrester, Jay W. (1961), Industrial Dynamics, Cambridge, Mass.: MIT Press. Forrester, Jay W. (1969), Urban Dynamics, Cambridge, Mass.: MIT Press. Forrester, Jay W. (1971), World Dynamics, Cambridge, Mass.: Wright-Allen Press. Forrester, Jay W. and Peter M. Senge (1980), “Test for Building Confidence in System Dynamics Models”, TIMS Studies in the Management Sciences. Hamilton, H.R., et al. (1969), Systems Simulation for Regional Analysis, Cambridge, Mass.: MIT Press. Kemeny, John G. (1959), A Philosopher Looks at Science, D.van Nostrand.

10.

Parkin, Michael (1996). Macroeconomics (third edition). Addison - Wesley Publishing Company, Inc..

11. 12.

Popper, Karl R. (1969), Conjectures and Refutations, Routledge and Kegan Paul. Richardson, G.P. & A.L. Pugh III (1981), Introduction to System Dynamics Modeling with Dynamo, The MIT Press, Cambridge, Massachusetts. Saeed, K. (1984), Policy-Modelling and the Role of the Modeller, Research Paper, Industrial Engineering & Management Division, Asian Institute of Technology, Bangkok. Sasmojo, Saswinadi (2004), Sains, Teknologi, Masyarakat dan Pembangunan, Program Pascasarjana Studi Pembangunan ITB. Senge, Peter M. (1990), The Fifth Discipline : the art and practice of the learning organization, Doubleday/Currency, New York. Sterman, John D. (1981), The Energy Transition and The Economy: A System Dynamics Approach, PhD Thesis, Cambridge : MIT. 69

3. 4. 5. 6. 7. 8.

13. 14. 15. 16.

70

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71

72

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Sesi 5 Feedback Loop, Delay, dan Nonlinearity

1

Outcomes Pada akhir sesi peserta dapat: • mengkonsepsualisasikan sebuah fenomena menggunakan causal loop diagram (CLD); • memahami konsep feedback loops (positif dan negatif); • memahami konsep stock, flow, delay, dan nonlinearity; dan • menjelaskan teori-teori yang mendasari pembuatan CLD. 2

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5.1 Hubungan Kausal (Sebab-Akibat) • Suatu struktur umpan –balik harus dibentuk karena adanya hubungan kausal (sebab-akibat). Dengan perkataan lain, suatu struktur umpan-balik adalah suatu causal loop (lingkar sebab-akibat). • Struktur umpan-balik ini merupakan blok pembentuk model yang diungkapkan melalui lingkaran-lingkaran tertutup. Lingkar umpan-balik (feedback loop) tersebut menyatakan hubungan sebab-akibat variabel-variabel yang melingkar, bukan manyatakan hubungan karena adanya korelasi-korelasi statistik. • Hubungan sebab-akibat antar sepasang variabel (variabel sebab terhadap variabel akibat), dalam suatu fenomena, harus dipandang dengan suatu konsep bahwa hubungan variabel lainnya terhadap variabel akibat dianggap tidak ada. 3

Sedangkan suatu korelasi statistik antara sepasang variabel, dalam suatu fenomena, diturunkan dari data kedua variabel tersebut yang diperoleh dalam keadaan (kondisi) semua variabel yang terdapat dalam fenomena itu berhubungan satu dengan yang lainnya dan kesemuanya berubah secara simultan. Ada 2 macam hubungan kausal, yaitu: • hubungan kausal positif; dan • hubungan kausal negatif. Ada 2 macam lingkar umpan-balik, yaitu: • lingkar umpan-balik positif (growth);dan • lingkar umpan –balik negatif (goal seeking). 4

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5

6

3

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Causal-loop diagram (CLD)

+

Kelahiran +

(+)

Populasi -

+ (-)

Kematian

7

5.2 Level (Stock) dan Rate (Flow) • Dalam merepresentasikan aktivitas dalam suatu lingkar umpan-balik, digunakan dua jenis variabel yang disebut sebagai level dan rate. • Level menyatakan kondisi sistem pada setiap saat. Dalam kerekayasaan (engineering) level sistem lebih dikenal sebagai state variable system. Level merupakan akumulasi di dalam sistem. • Persamaan suatu variabel rate merupakan suatu struktur kebijakan (policy) yang menjelaskan mengapa dan bagaimana suatu keputusan (action) dibuat berdasarkan kepada informasi yang tersedia di dalam sistem. Rate inilah satusatunya variabel dalam model yang dapat mempengaruhi level. (rate disebut juga sebagai decision point) 8

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Level (Stock) and Rate (Flow) • Levels and rates as loop sub-substructure  A feedback loop consists of two distinctly different types of variables, the levels (states) and the rates (actions). Except for constants, these two are sufficient for represent a feedback loop. Both are necessary.

• Levels are integrations  The level integrate (or accumulate) the result of action in a system. The level variables can not change instantaneously.

• Level are changed only by the rates  A level variable is

computed by the change, due the rate variables, that alters the previous value of the level. The earlier value of the level is carried forward from the previous period. It’s altered by rates that flow over the intervening time interval. The present value of a level variable can be computed without the present or previous values of any other level variables.

• Levels and rates not distinguised by units of measure

The units of measure of a variable do not distinguish between a level and a rate. The identification must recognize the difference between a variable created by integration and one that is a policy statement in the system. 9

Level (Stock) and Rate (Flow) • Rates not instantaneously measurable No rate of flow

can be measured except as an average over a period of time. No rate can, in priciple, control another rate without an intervening level variable.

• Rates dapend only on levels and constants No rate

variable depends directly on any other rate variable. The rate equations (policy statements) of system are of simple algebraic form; they don’t involve time or the solution interval; they are not dependent on their own past value.

• Level variables and rate variables must alternate

Any path through the structure of a system encounters alternating level and rate variables.

• Levels completely describe the system condition

Only the values of the level variables are needed to fully describe the condition of a system. Rate variables are not needed because they can be computed from the levels. 10

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5.3 Delay

11

5.4 Nonlinearity

[nonlinier]

12

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Sesi 6 Simulation Software: Powersim Studio

1

Outcomes At the end of this session, participants will be able to: • construct flow diagram based on a CLD • simulate the model using Powersim Studio software (Saving Model).

2

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6.1 Powersim Studio Windows

3

Powersim Studio General View

Document Tools, contain of component and format for documentation, view , tools and help

Modeling Tools, contain of button for building stock and flow diagram and view the simulation results

Diagram View, tp place for building the models Diagram Tools, contaiin of model sheet diagram

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Powersim Studio Ready to be Used Title Bar

Menu

Sketch Tools

Output File Window Main Toolbar

Build (Sketch) Window

Status Bar

Analysis Tools

5

6.2 Causal Loop Diagram Saving Model

6

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6.3 Model Building Step 1 1. Open Powersim Studio, and click “New Model” in Main Toolbar. 2. Click next button in the new project wizard window

7

Step 2 3.

Choose the language, then click next

8

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Step 3 4.

Choose Studio 10 file format, then click next

9

Step 4 5.

Choose Enforce Time Unit Consistency, then click next

10

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Step 5 6.

Choose calendar interdependent simulation, then choose next

11

Step 6 7.

Choose use as default in all new projects, then click next

12

6

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Step 7 8.

Choose Use as default in all projects, then click next

13

Step 8 9.

Choose time step 0.25, then click next

14

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Step 9 10. Click Finish, so ready to build model flow diagram

15

Step 10 11. Ready to Build Saving Model Flow Diagram

16

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6.4 Building Saving Model • •

Sketch tools Activate tools for creating a level from sketch tools, than put in the sketch window or diagram view,



Type the name of the level  Saving in editing box, then enter,



Activate tools for creating a Flow with Rate,



than connect it to the “saving” level and naming it Interest,

• •

Activate tools for creating a Constant and naming it Interest Rate, Activate tools for creating a Link and link the savang level and Interest Rate constant to the Interest auxilary,

17

6.5 Inputting Model Equations (1) • Double click the Saving level • input 100000 in definition windows • input rupiah for the unit measure • Click apply, then ok

• If rupiah not yet defined this message will appear, and click yes

• click next, then apply and finish

18

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6.5 Inputting Model Equations (2) Double click the Interest Rate constant • input 0.05 in definition windows • input 1/year for the unit measure • Click apply, then ok 

Double click Interest auxiliary • input equation Interest Rate*Saving • Activate unit measure v • Click apply, then ok •

19

Saving Model Flow Diagram • Saving Model ready to be simulated

20

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6.6 Model Simulation (1) •

Activate tool for creating a Time Graph for simulating the saving and interest behavior.



Activate tool for creating a Time Table for simulating the saving and interest behavior.

21

6.6 Model Simulation (2) Drag Saving level to the Time Graph and the Time Table Drag Interest rate to the Time Graph and the Time Table Click the simulation button to run the Saving Model simulation rupiah 15,000,000

year 0

Saving Interest

Saving

20 10,000,000

5,000,000

0

0

20

40

60

80

Saving (rupiah) 100,000.00 270,148.49

40

729,802.09

60

1,971,549.35

80

5,326,110.88

100

Interest Rate

14,388,408.35

100

rupiah/year

year 0

600,000

Interest

• • •

400,000

200,000

Interest (rupiah/year) 5,000.00

20

13,507.42

40

36,490.10

60

98,577.47

80

266,305.54

100

719,420.42

0 0

20

40

60

80

100

22

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23

24

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Sesi 7 Latihan Simulasi

1

Outcomes At the end of this session, participants will be able to: • Develop Stock and flow diagram based on Causal Loop Diagram; • Simulate the Model Using Powersim Studio (positive feedback, negative feedback, non linearity, Population model, investment model, and inventory model). 2

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7.1 Positive Model (Population)

3

Causal Loop Diagram Population +

Population Growth

Population

+ +

+

Population Growth Fraction

4

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Population Model

Level_1 Rate_1

Constant_1 5

Population Model

6

3

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Population Model Behaviour

7

7.2 Negative Feedback (Investment Model)

8

4

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Causal Loop Diagram Investment Model

Investment

- +-

Time to Adjust Capital

-

+

Capital

Desired Capital

9

Investment Model

10

5

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Investment Model Behaviour Million_Dollars 8.000

6.000 Kapital

NEGATIVE FEEDBACK AND GOAL SEEKING

Desired Kapital

4.000

2.000

Kapital Investment

0

10

20

30

40

50

Million_Dollars/ye ar

1.500

Desired Kapital

Investment

Time to adjust kapital

1.000

500

0

0

10

20

30

40

50

11

7.3 Positive and Negative Feeback (Population Model)

12

6

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Causal Loop Diagram Population Model +

+ Birth Rate

+

Population -

-

+

Death Rate +

+

Fertility

Mortality

13

Population Model

14

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Population Model Behaviour

15

7.4 Delay (Employee Model)

16

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• CLD Without Delay

+ +

Pengangkatan Pegawai +

-

Pegawai

Pensiun

-

-

Pegawai yang Dibutuhkan

Pertambahan Pegawai dari Kebutuhan +

17

• CLD Dengan Delay + Pengangkatan Pegawai + +

+

Pegawai

-

Pensiun

-

-

Pertambahan Pegawai dari Kebutuhan

Pegawai yang Dibutuhkan

+ + Pensiun Rata-Rata

+ Pertambahan Pegawai dari Pensiun +

18

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• Non-Delay Employee Model

19

• Non-Delay Employee Model Behaviour MODEL TANPA DELAY Masa kerja

Jumlah Pegawai Pengangkatan

Pensiun

jiwa 7.000

6.000

Waktu pengangkatan

Pertambahan pegawai yg dibutuhkan

Pegawai dibutuhkan

5.000 Jum lah Pe gawai

4.000

Pe gawai dibutuhk an

3.000

2.000

1.000

0

5

10

15

20

25

20

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• Delayed Employee Mode Behaviour jiwa 7.000

MODEL DENGAN DELAY

6.000

Pensiun rata-rata 2

Waktu meratakan pensiun 2

Pertambahan pegawai dari pensiun 2

5.000 Jum lah Pe gawai 2

4.000

Pe gawai dibutuhk an 2

3.000

2.000

Jumlah Pegawai 2 Pengangkatan 2

Pensiun 2

1.000 0

Masa kerja 2

5

10

15

20

25

jiwa/year

Pertambahan pegawai yg dibutuhkan 2

Pegawai dibutuhkan 2

Pengangkatan 2

2.000

Waktu pengangkatan 2

1.500

1.000

500

0

5

10

15

20

25

21

7.5 Nonlinearity (Pollution Model)

22

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• Linear CLD

+

+ Pollution Growth Rate

Pollution Absorption Rate -

-

Pollution -

Pollution Absorption Time

23

• Non-Linear CLD +

+ Pollution Growth Rate

Pollution

-

+ Pollution Ratio

Pollution Absorption Rate -

+ Pollution Absorption Time +

24

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• Linear Pollution Model Pollution Pollution growth

Pollution absorption rate

Pollution absortion time

Pollution growth rate constant

Ton_C O 2 20

Pollution

15

10

5

0 0

2

4

6

8

10

25

• Non Linear Model Pollution Pollution growth

Pollution growth rate

Pollution absorption rate

Ton_CO 2

Pollution ration Pollution Absortion Time

800 700

Pollution Standar

Pollution

600 500 400

300 200

100 0 0

20

40

60

80

100

26

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7.6 Investment Model

27

Causal Loop Diagram Investment +

Capital -

-

Gov Spending

Investment +

-

-

+ Normal Time to Adjust Capital

Income +

Consumption + Propensity to Consume

+

+

+

Depreciation

-

+

+

-

+

//

+

+

Smoothed Income -

Life of capital

Desired Capital + + KOR

Time to Smooth Income 28

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Causal Loop Diagram Investment Model

+

Gov Spending +

+

Income Consumption

Investment + - -

+

+

+ Depreciation

-

Capital

-

Normal Time to Adjust Capital

+

Life of Capital

Desired Capital + + KOR

+

+

+ -

+ Smoothed Income

Propensity to Consume

Time to Smooth Income

29

Investment Model Iinitial GE Government Expenditure

Change in GE GE Trend

Consumption

Normal Time to Adjust Kapital-NTAK

Income Capital Investment

Depreciation

Prospensity to consume Life of capital

Desired Kapital Capital Output Ratio -KOR Smoothed income Time to smoothed income

Initial Smoothed Income

30

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Investment Model Behaviour (Equilibrium) Million_Dollars/ye ars 1.000

Gove rnm e nt Ex pe nditure C onsum ption

500

Incom e Inve stm e nt

0 0

20

40

60

80

100

31

Investment Model Behaviour (GE Growth 1 %/year) Million_Dollars/years 2.500

2.000

1.500 Governm ent Ex penditure Consum ption Incom e Investm ent 1.000

500

0 0

20

40

60

80

100

32

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7.7 Model Inventory

33

ABC Manufacturing Inventory (1) SCENARIO • ABC Manufacturing would like to use simulation to better understand the interaction between the amounts of merchandise the public orders and their own inventory and production levels. • Since the company often experiences oscillations in their inventory and production levels, they think the first step in solving this problem is to build a model that would explain the relevant interactions. • They know that their production policy concists of two components increasing or decreasing the inventory to match an optimal or desired level of inventory and keeping inventory high enough to cover what they expect their demand will be in the future. • To be safe, they like to keep four times as much inventory on hand as they think will be needed to cover demand. • In addition, production is set so that one-sixth of the discrepancy between the desired and actual inventory is 34 corrected every week.

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ABC Manufacturing Inventory (2) • Their assumptions about future demand are based on the current order rate. • The current order rate constitutes the real demand that the company faces. • Their policy formulating their expected demand is simple. • They want to correct one-eighth of the difference between their real and expected demands every week. • When their beliefs about future demand change, this affects their desired level of inventory and the rate at which they produce widgets, according to the production policy described above.

35



ABC Manufacturing Inventory (3) • When widgets are produced, they go straight to warehouse to be stored as inventory. • No Product can go from the production line straight to the customer; it must go into the inventory first. • Shipments are made only from inventory. Because the company keeps four times as much inventory as they think they will need at any time, they believe they are able to ship the necessary products to fulfill every order. • Therefore, they are not now concerned with backlogs and their effects (although a negative inventory while meaningless in reality, can here be constructed to represent a backlog). 36

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CLD Inventory Model +

Inventory

Time to Correct Inventory

-

Production

Inventory Coverage

-

-

+

Shipment +

-

+

Desired Inventory - +

-

Order Rate +

Expected Demand +

-

-

+

Change in Expected Demand Time to Change Expectations 37

Powersim Studio

38

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Setting The Simulation Time (1)

39

Setting The Simulation Time (2)

40

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Model Building (1)

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Model Building (2)

42

21

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Model Building (3)

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Model Building (4)

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Model Building (5)

45

Model Building (6)

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23

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Model Building (7)

47

Model Building (8)

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24

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Model Building (9)

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Model Building (10)

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Model Building (11)

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Model Building (12)

By click right mouse

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Model Building (13)

;

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Model Building (14)

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Model Building (15)

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Model Building (16)

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Model Building (17)

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Model Building (18)

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Model Building (19)

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Model Building (20)

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Model Building (21)

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Model Building (22)

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Model Building (23)

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Model Building (24)

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Model Building (25)

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Model Building (26)

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Model Building (27)

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Model Behavior (1)

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Model Behavior (2)

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Model Behavior (3)

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Model Behavior (4)

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Model Behavior (5)

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Conclusion • The purpose of this session was to ilustrate how to build a simple model in Powersim Studio. Although the model is simple, it shows how to use Studio to create simulation models, and gives you a brief introduction to the technical aspect of modelling using the software. • We recommend you to take a closer look at some of the other tutorials as well, and also to study the sample models. These shows various business cases as well as features in Studio. We would also encourage you to refer to Help for Studio, where you can find answers to a wide range of questions. 73

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Sesi 8 Model Ketersediaan (Availability)

1

Outcomes At the end of this session, participants will be able to: • understand some concepts of availability that are widely used in modeling using the system dynamics approach; • understand the differences among the availability concepts; and • build a model based on each of availability concepts using Powersim Studio software. 2

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Simulation Objectives • Participants are able to understand the concept of availability that was widely used in modeling using the system dynamics approach. • Participants are able to build a model that is based on each of availability concepts using the software Powersim Studio. • Participants are able to understand the differences among the availability concepts. 3

8.1 Availability Concept • Availability of a resource means a resource that is committable, operable, or usable upon demand to perform its designated or required. • If Availability >= 1, means available; if Availability < 1 means not available • Availability concept (cash) – Cash Availability = Cash Adequacy Time /Targeted Cash Adequacy Time – Cash Availability = Cash/Desired Cash – Cash Availability = Maximum Outcome/Desired Outcome – Implicit Cash Availability; Maximum Outcome = Cash/Targeted Cash Adequacy Time 4

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Availability Concept 1 Availability Concept 1 Desired Expenditure 1

Cash 1

Income 1

Expenditure 1 Cash Availability 1 Cash Availability Effect 1

Cash Adequacy Time 1 Targeted Cash Adequacy Time 1 Cash Availability = Cash Adequacy Time / Targeted Cash Adequacy Time

Note: Can not be used when the desired expenditure is zero

5

Availability Concept 2 Availability Concept 2 Desired Expenditure 2

Cash 2

Income 2

Expenditure 2

Cash Availability 2

Cash Availability Effect 2

Desired Cash 2 Targeted Cash Adequacy Time 2 Cash Availability = Cash / Desired Cash

Note: Can not be used when the desired expenditure is zero

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Availability Concept 3 Availability Concept 3 Desired Expenditure 3

Cash 3

Income 3

Expenditure 3

Cash Availability Effect 3 Maximum Expenditure 3

Cash Availability 3

Targeted Cash Adequacy Time 3 Cash Availability = Maximum Expenditure / Desired Expenditure

Note: Can not be used when the desired expenditure is zero

7

Availability Concept 4 Availability Concept 4 Desired Expenditure 4

Cash 4

Income 4

EXpenditure 4

Max Expenditure 4

Targeted Cash Adequacy Time 4 (Implicit Cash Availability)

Masimum Expenditure = Cash / Targeted Cash Adequacy Time

Note: Can be used when the desired expenditure is zero

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8.2 Simulation Results • Although it is using the four different model structures but will produce the same dynamic behavior. rupiah 2.000.001,0

2,0

1,5

2.000.000,5

C ash 1 C ash 2

2.000.000,0

C ash Availability 1

1,0

C ash Availability 2

C ash 3 C ash 4

1.999.999,5 1.999.999,0

C ash Availability 3 0,5

0,0 0

5

10

0

5

10

rupiah/ye ar 1.000.001,0 Incom e 1 Ex pe nditure 1

1.000.000,5

Incom e 2 Ex pe nditure 2

1.000.000,0

Incom e 3 Ex pe nditure 3

999.999,5

Incom e 4 EXpe nditure 4

999.999,0 0

5

10

9

Ina Juniarti +628164210120

10

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Ina Juniarti +628164210120

11

Ina Juniarti +628164210120

12

6