Design And Optimization Of Agro-Scm For Food And Energy Security In Indonesia

CONFERENCE COMMITTEE Coordinator : Yandra Arkeman, Dr. M.Eng

(Departement of Agroindustrial Technology, IPB) Co-Coordinator : Nastiti Siswi Indrasti, Prof. Dr. Ir.

(Head of Departemen of Agroindustrial Technology, IPB) International Experts:

1. Kenneth De Jong, Prof.

(George Mason University, Fairfax, Virginia, USA) 2. Kim Bryceson, Prof.

(School of Agriculture and Food Science , University of Queensland, Australia)

3. Haruhiro Fujita, Prof.

(Department of Information Systems, Niigata University for International and Information Studies Japan)

4. Gajendran Kandamsany, Dr. (United Kingdom)

5. Noel Lindsay, Prof. (University of Adelaide, Australia) Committee Members from IPB:

1. Kudang Boro Seminar, Prof.

(Departement of Mechanical & Bio-system Engineering, IPB) 2. Marimin, Prof.

(Departement of Agroindustrial Technology, IPB) 3. Endang Gumbira Said, Prof.

(Departement of Agroindustrial Technology, IPB) 4. Suprihatin, Prof.

(Secretary of Departement of Agroindustrial Technology, IPB) 5. Ono Suparno, Prof.

(Departement of Agroindustrial Technology, IPB) 6. Agus Buono, Dr.

(Head of Department of Computer Science, IPB)

PROCEEDINGS

2

nd

International Conference on Adaptive and Intelligent Agroindustry (ICAIA)

September 16 – 17, 2013, IPB International Convention Center

Bogor – Indonesia

Organized by :

Departement of Agroindustrial Technology, Faculty of Agricultural Engineering and

Technology Bogor Agricultural University

WELCOMING ADDRESS

Prof. Dr. Ir. Nastiti Siswi Indrasti

Head of Agroindustrial Technology Department Faculty of Agricultural Engineering and Technology

Bogor Agricultural University

On

Second International Conference on Adaptive and Intelligence Agroindustry (2nd ICAIA)

Bogor, September, 16 – 17, 2013 Assalamu’alaikum Warohmatullahi Wabarokatuh

In the name of Allah, the beneficent and the merciful, Distinguish Guest, Ladies and Gentlemen

Let me first thank you all for accepting the invitation to participate in this 2nd International Conference on Adaptive and Intelligence Agroindustry (ICAIA). In particular I would like to thank Rector of IPB (Institut Pertanian Bogor/Bogor Agricultural University) Prof. Herry Suhardiyanto for supporting this event as part of the series academic event in celebrating the 50th _{Anniversary of Bogor Agricultural University.}

In fact, the idea of organizing this conference was the continuation of the International Workshop on Computational Intelligence and Supercomputing Technology for Adaptive Agroindustry held by the Department of Agroindustrial Technology, Bogor Agricultural University last year.

Professor Kenneth A De Jong from George Mason University, US has successfully conducted joint international research with some staff from the Department of Agroindustrial Technology and Department of Computer Science, Bogor Agricultural University. The research aims to develop an integrated and intelligent system (namely SMART-TIN©) for the design of adaptive agroindustrial system in order to achieve a sustainable agroindustry that can mitigate global climate change and at the same time secure food, water, energy and natural medicine supply.

We are certainly proud to have been able to assemble this event in IPB, Bogor. The range of participants and audience at this conference is precisely something I would like to stress. The main goal of the conference is to provide an effective forum for distinguished speakers, academicians, professional and practitioners coming from universities, research institutions, government agencies and industries to share or exchange their ideas, experience and recent progress in Adaptive and Intelligent Agroindustry.

Distinguish Guest, Ladies and Gentlement,

Global climate change is the most challenging problems for us today and in the near future. This global change in our climate can lead to the shortage of the food, water, bioenergy and natural medicine that will affect the quality of human life. Many studies indicate that the threat of food, water, bioenergy and natural medicine crisis due to global climate change still worries our society. This problem can be solved by the development of agroindustry, i.e. an interrelated value chain entities from farming, to agro-processing industry and then to the end-customers. In fact, the design of agroindustry is complex and involves many factors and large data bases and more importantly, needs a good intelligence to process data and information to good decisions. Therefore, the way to design and manage agroindustry should be improved in order to meet the design objectives.

Agroindustries consume quite significant amount of energy on one side, on the other side they generate sizable amount of industrial wastes and its utilization as a captive energy resource is a kind of potential. Based on our study, a plywood industry with the production capacity of 200.000 m3/year could generate 32 percentage of solid waste. If this amount of waste used as an energy alternative, it may result on the saving of 131.037.768.597 rupiah per month. Similar to plywood industry, sugarcane industry with the production capacity of 480 ton per hour could generate 154 ton per hour of waste (bagasse) and this amount of waste contribute to the saving of energy consuming by 19.250 Kwh. Recent study we conducted, indicated that cassava starch industry may contribute to a significant amount of waste. It has also potential usage as an energy resource. Based on our study the conversion of its waste into energy will contribute to the saving of energy usage of 4100 liter biogas per ton material.

The three industries mentioned is only examples of how potential the role of agroindustrial waste as an alternative resource in replacing the conventional energy resource as its presence will be significantly

reduced. The new, incremental energy contributions that can be obtained from waste biomass will depend on future government policies, on the rates of fossils fuel depletion, and on extrinsic and intrinsic economic factors, as well as the availability of specific residues in areas where they can be collected and utilized. All of these factors should be in detail examined to evaluate the development of the industrial waste contribution. Hope this conference will also discuss this issue in more detail as it is an important matter for all of us. We should no more think just how to produce high value product but it is also necessarily important how to keep our live in good quality by understanding following old saying…” only when the last tree has been cut, only when the last fish has been angled, and only when the last river has been polluted, then we realized that we could not eat money”.

I do not to take up any more of your time with these opening remarks. Let me simply thank you once again for sharing your thoughts with us. Here’s wishing every success for the conference. May Allah bless all of us.

Thank you for your kind attention,

AGENDA of

2

nd

International Conference on Adaptive and Intelligent Agroindustry

(ICAIA)

Time Activities Room

Day 1 (16 September 2013)

08.00 – 09.00 (60’)

Registration 09.00 – 10.00

(60’)

Opening Ceremony

 Welcoming Address: Prof. NastitiSiswiIndrasti (Head of Dept TIN, Fateta, IPB)

 Conference Opening: Prof. HerrySuhardiyanto(Rector of IPB)

o ABET Certification announcement and short ceremony o Launching International Double Degree Master Program in

Innovation and Technopreneurship in Cooperation with University of Adelaide, Australia

o Soft-launching Master in Logistik Agroindustri (Agroindustrial

Logistics)

Ballroom

10.00 – 10.45 (45’)

Opening Speeches:

Prof. IrawadiJamaran (Agroindustry Guru, IPB: 25’) Prof. Eriyatno (Industrial and System Engineering, IPB: 20’)

Ballroom

Session 1 10.45 – 11.15

(30’)

Keynote Speech Dr. YandraArkeman (IPB) Ballroom 11.15 – 12.00

(45’)

Keynote Speech Prof. Kenneth De Jong (George Mason University, USA) Ballroom 12.00 – 13.30

(90’)

Lunch Break Session 2

13.30 – 15.15 (105’)

Moderator: Prof. EndangGumbiraSa’id (IPB) Invited Speakers (1-4) (4 x 20 minutes) Discussion (25 minutes)

Tentative Schedule: Prof. Kim Bryceson (Australia), Prof. SyamsulMa’arif (IPB), Prof. KudangBoro Seminar (IPB), Prof. HaruhiroFujita (Japan)

Ballroom

15.15 – 15.45 (30’)

Break 15.45 – 17.30

(105’)

Moderator: Prof. Marimin (IPB) Invited Speakers (5-8) (4 x 20 minutes) Discussion (25 minutes)

Tentative Schedule: Dr. Gajendran (UK), Prof. Noel Lindsay (University of Adelaide), Dr. KuncoroHartoWidodo (UGM), Prof. UtomoSarjonoPutro (ITB)

Ballroom

Day 2 (17 September 2013) 08.00 – 08.30

(30’)

Registration 08.30 – 10.15

(105’)

Moderator: Prof. KudangBoro Seminar (IPB) Invited Speakers (9-12) (4 x 20 minutes) Discussion (25 minutes)

Prof. Egum (IPB), Prof. Marimin (IPB), Dr. AgusBuono (IPB), Dr. HeruSukoco (IPB) 10.15 – 10.30

(15’)

Coffee Break 10.30 – 12.30

(120’)

Parallel Session 1 Moderator: Prof. Fujita

(7 paper @ 15 minutes) Discussion (15 minutes)

Parallel Session 2

Moderator: Prof. Ono Suparno (7 paper @ 15 minutes) Discussion (15 minutes)

Parallel Session

Moderator: Prof. Suprihatin (7 paper @ 15 minutes) Discussion (15 minutes)

12.30 – 13.30 (60’)

Lunch Break 13.30 – 15.00

(90’)

Open Discussion (Open Forum) with Prof. Kenneth De Jong

Topic: Foundations and Applications of Genetic/Evolutionary Algorithms

Ballroom 15.00 – 15.30

(30’)

Conference Closing Ballroom

15.30 – 17.00 (90’)

Indonesian Agroindustry Association (AGRIN) National Congress (PIC: Prof. Suprihatin)

Ballroom 17.00 – 17.45

(45’)

CONTENT LIST

Conference Committee

Welcoming Address ….……….………... i Agenda of 2nd ICAIA 2013... iii Content List …………..………...………... v

A.  SMART-TIN© : An Integrated And Intelligent System For The Design Of

Adaptive Agroindustry (A Conceptual Framework) (Yandra Arkeman)

1‐9. B.  GHG Emission Reduction By Waste Cooking Oil Recycling In Environmental 

Partnership  Program  Of  Bogor  City  (Haruhiro  Fujita,  Atsushi  Yoshimoto,  Katsuyuki Nakano,  Koji Okuhara, Noriaki Koide, Dadang Supriatna) 

10‐16.

C.  Challenge  of  Indonesian  Logistics  Infrastructure  for  Perishables  Products  (Kuncoro Hartoto Widodo, Yandra Rahardian Perdana, Joewono Soemardjito) 

17‐28.

D.  Design Of Grouping Traditional Market  Distribution Using  Fuzzy Clustering And  Design of  Routing of Packaging Cooking Oil  From Distribution Center  To  Traditional Market  Using Traveling Salesperson Problem– Genetic Algorithm in  Indonesia  (Case‐Jakarta)  (Teja  Primawati  Utami,  Syamsul  Maarif,  Yandra  Arkeman, Liesbetini Hartoto) 

29‐43.

E.  Determining the degree of product rightness at the conceptual stage:A Case of  the Low Cost Green Car (Ade Febransyah, Fransisca Tharia) 

44‐53.

F.  Co‐Composting Process Of Bagasse And Sludge From Sugarcane  Industry With  Influence Of Difference Initial C/N Value And Aeration (Andes Ismayana, Nastiti  Siswi Indrasti, Tori Sane) 

54‐62.

G.  Analysis Of The Competitiveness Of The Agribusiness Sector Companies Using  Porter's Five Forces (M. Hudori) 

63‐72.

H.  Design  And  Optimization  Of Agro‐Scm  For Food  And  Energy  Security In  Indonesia (Hoetomo Lembito, Kudang Boro Seminar, Nunung Kusnadi, Yandra  Arkeman) 

73‐83.

I.  Construction Of Patchouli Oil’s Price In West Java : A Structural Equation Model  (Isti Surjandari, Alan Dwi Wibowo, Erlinda Muslim, Nurwan Nugraha) 

84‐90.

J.  Optimization Vehicle Routing Problem With Cross Entropy Method And Branch  & Bound Algorithm (Iwan Aang Soenardi, Budi Marpaung) 

91‐99.

K.  Design  And  Optimization  Of  Agro‐Scm  For  Food  And  Energy  A  Remote  Monitoring System Of Broilers’ Behavior In A Multi‐Agent Broiler Closed House  System (K.B. Seminar, R. Afnan, M. Solahudin, Supriyanto, A.K. Wijayanto, M.Z.  Arifin, A. Fatikunnada) 

100‐112.

L.  Artificial Neural Network Model Based Capacitance Dielectric For Crackers Self‐ Life Prediction (Erna Rusliana Muhamaad Saleh, Erliza Noor, Taufik Djatna,  Irzaman) 

113‐121.

M.  Characterizing Temporal Dynamic Of Weather Variability To Support Decision  Making On Weed Control (Rizky Mulya Sampurno, Kudang B. Seminar, Yuli  Suharnoto, Mohamad Solahudin) 

N.  Risk Management Model in Dairy Product Transportation With Fuzzy Logic  Approach (Winnie Septiani, Yandra Arkeman) 

131‐141.

O.  Strategy to Increase Institutional Capacity of Farmer Groups to Support the  Transportation System Agroindustry Products (Zulfiandri, Yandra Arkeman) 

142‐156.

P.  Adsorption Of Carotenoid From Palm Oil Methyl Ester By Using Attapulgite And  Synthetic Silica Magnesium As Adsorbent (Sapta Raharja, Prayoga Suryadarma,  Zuni Fitriyantini) 

157‐163.

Q.  High Accuracy Agroindustry Environment Monitoring System Based on Photonic  Crystal Sensor Network (Mamat Rahmat, Muhamad Azis, Erus Rustami, Wenny  Maulina, Kudang Boro Seminar, Arief Sabdo Yuwono, Husin Alatas) 

164‐174.

R.  Study on Effect ofTemperatureIncrease on the Productivity of Methanefrom  POME Fermentation (Sarono, E. Gumbira Said, Ono Suparno, Suprihatin, Udin  Hasanudin) 

175‐180.

S.  The Design and Implementation of Geographic Information Systems to Support  Food  and  Energy  Security  (Arif  Purnomo  Muji  Basuki,  Yandra  Arkeman,  Musthofa) 

181‐191.

T.  Study Of White Crystal Sugar Distribution System In Indonesia (Suripto, Yandra  Arkeman) 

192‐199.

U.  Corncob Biodelignification Process Using White Rot Fungi (Liesbetini Hartoto,  Purwoko, Elis Nina Herliyana, Cucu Rina Purwaningrum) 

200‐212. 

V.  Color Stability of Beet Dyes Label During Heating    (Endang Warsiki, Asih  Setiautami) 

213‐219.

W  Precipitation Classification Using LVQ on Dry Season Base on Global Climate  Indices Case Study in Indramayu District (Indra Kelana Jaya, Agus Buono, Yandra  Arkeman) 

220‐225.

X.  Wood Identification Type By Using Support Vector Mechine‐Based Image (A.A.  Gede Rai Gunawan, Sri Nurdiati, Yandra Arkeman) 

226‐232.

Y.  Fuzzy  Logic  Systems  and  Applications  in  Agro‐industrial  Engineering  and  Technology (Marimin, Mushthofa) 

233‐241.

Z.  Adaptive  Learning  Model  of  Hypergame  by  using  Genetic  Algorithm:  an  Application of Value Co‐creation in Service System (Case Study: Airline Service)  (Santi Novani, Utomo Sarjono Putro) 

Design And Optimization Of Agro-Scm For Food And Energy Security

In Indonesia

Hoetomo Lembito

Doctor in Management and Business, Bogor Agricultural Institute (IPB)

Kudang Boro Seminar

Lecturer of Bogor Agricultural Institute (IPB)

Nunung Kusnadi

Lecturer of Bogor Agricultural Institute (IPB)

Yandra Arkeman

Lecturer of Bogor Agricultural Institute (IPB)

ABSTRACT

World palm oil consumption has significantly increased over the years. Palm oil is one of the world's most produced and consumed oils. This cheap, production-efficient and highly stable oil is used in a wide variety of food, cosmetic and hygiene products, and can be used as source for bio-fuel or biodiesel. Global palm oil production is dominated by Indonesia and Malaysia. These two countries together account for around 85 to 90 percent of total global palm oil production. Indonesia is currently the largest producer and exporter of palm oil worldwide.Indonesia has scored a significant achievement on oil palm development by becoming the biggest palm-oil-producing country in the world, both in terms of plantation area and in total national production of palm oil. Crude Palm Oil (CPO) is a commodity which has a strategic value to be developed for Indonesian economy. On the long term, global palm oil demand shows an increasing trend as an expanding global population gives rise to increased consumption of palm-oil based products.This fast-paced growth of the CPO production and the increase of world CPO demand have necessitated extensive supply chains across the country to facilitate sourcing and distribution of production. The purpose of this paper is to design, construct and simulate a model to capture the structure of CPO supply chain system in order to predict demand and supply, production, revenue and cost, with the ultimate purpose of optimizing the potential growth CPO agro-industries for food and energy security in Indonesia The system dynamic model can be deployed to simulate and optimized some pattern of selected components. Model validations were done to improve fitness of the model.

Keywords: supply chain, system dynamic, crude palm oil

1. INTRODUCTION

1.1. Global Demand Growth for Palm Oil

The oil palm, Elaeis guineensis Jacq. is indigenous to West Africa where the main palm belt

ran from Sierra Leone, Liberia, the Ivory Coast, Ghana and Cameroon to the equatorial regions of the Republics of Congo and Zaire. (Teoh, 2002). Its oil, extracted from its fruits, has been used as food and medicine through the ages, the earliest archeological evidence suggests the existence of an earthenware jar containing residues of palm oil in a 5,000-year-old Egyptian tomb. (www.rspo.org) Oil palm plantation produced raw material in the form of fresh fruit brunch (FFB) that were processed in the palm oil mill to become semi-goods namely crude palm oil (CPO) and palm kernel oil (PKO). Semi-goods were furthered processed to become finished goods (edible and non-edible) at downstream manufacturing industry to give financial added value.

Palm oil is the most traded vegetable oil crop in the world. Palm oil used in a vast range of everyday products and traditionally used in manufactured food products, it is an increasingly important ingredient in cosmetic / health products, and is now found in >50% of packaged supermarket products. Palm oil is also the most productive vegetable oil, producing around 10 times more oil per ha than its nearest competitor soybean, and there is a growing demand for its use in bio-diesel and electricity production. (PWC, 2013). Worldwide demand for palm oil has skyrocketed over the past 25 years, and oil palm plantations now cover an area of 11 million hectares. Global demand for palm oil is therefore expected to continue to grow strongly. Indeed, the World Bank forecasts that an additional 6.3 million ha of palm oil plantations will be required to meet global demands by 2020. Much of this land is expected to be in Indonesia which, together with Malaysia, already accounts for around 90% of the world’s palm oil exports. In Indonesia, the area covered by oil palm plantations has reached almost 6 million hectares with the production is the biggest in the world. During the last decade, the palm oil industry in Indonesia has been developing rapidly, and has close to doubled in size (4.2million ha in 2000 increased to 8.0 million ha in 2010).

In Indonesia, oil palm plantations have been progressively developed since 1980s. The palm oil industry in Sumatra and Kalimantan islands has gown rapidly since 1980, and yet it has not transformed itself into to a strong agribusiness system (Pahan, 2012). Agribusiness system was an approach which integrated raw material purchasing (upstream agro-industry), the activity of primary production (on farm), manufacturing process (downstream agro-industry), marketing, and supporting institutions such as financing, land agency, research and development within one system. Oil palm plantation produces raw material in the form of fresh fruit bunches (FFB) that were processed in the palm oil mill to become semi-goods namely crude palm oil (CPO) and palm kernel oil (PKO). Semi-goods are further processed to become finished goods (edible and non-edible) at downstream manufacturing industry to give financial added value. Palm oil export’s contribution to Indonesia economy in the year 2010 was USD 16.4 billions.

The oil palms mostly are planted in Sumatra, with recent expansions move into forested parts of Riau, Jambi, and South Sumatra, as well as into West Kalimantan and South Sulawesi. Most oil palm plantations are owned by Indonesian interests, although by 2002, there were over 600,000 ha of private estates owned by foreign and largely Malaysian companies. (Colin, 2003)

Figure 2: Global Palm Oil Demand 1970-2010 and Avg. Increase in Production Source :USDA 2010

A significant change in the palm oil industry has taken place during the past season, as Indonesia surpassed Malaysia in the production of palm oil to become the current world leader. Indonesia is the biggest CPO producer in the world, accounting for 47 percent of world production. The countries produced 18 million tons of CPO from 7.9 million hectares of plantations in 2008, and produced 22 million tons in 2009 and produced 23.6 million tons in 2010. Malaysia, currently the second-biggest CPO producer, may see its production from 17.7 million tons in 2009 to 18.2 millions in year 2010.

Figure 3 : Indonesian and Malaysia Palm Oil Production Source: USDA, 2012

The Indonesian palm oil Industry has grown very fast and significantly recently. By 2007, planted area and production had increased to 23 and 24.5 times compared to their level in 1980 respectively, and planted area’s growth rate on average 11% from 1980 to 2007, while the production growth on average is 13% per annum. Seventy percent of palm oil production in Indonesia is exported. As a result, the export market contributes important role in the growth of the palm oil industry. By 2007, palm oil export had increased to 23.6 times compare to its level in 1980, with an average growth rate of 28% in terms of quantity and 27% in terms of value annually. (James, 2007)

1.2. Agro-SCM

There have been major changes in the agro supply chain of energy and food products over the last decades. A more liberalized international trade system, globalization and the transition towards a consumer driven market have empowered this development

Such outstanding demand growth of palm oil does not come without a price, however. The intricate web of modern supply chains means that palm oil-growing countries are increasingly

involved in, and affected by, global issues such as food security (driven by increased food demand from growing and more prosperous populations) and energy security (driven by the impacts of the global financial crisis, oil price volatility, and over dependency on fossil fuel) It will require both an increase in yields and more plantations. Much of the required land should be taken from existing stocks of degraded land, but if this is poorly planned and/or ill-managed, it could have serious consequences for the environmental and social capital of the country.

Supply chain is a typical complex, adaptive, and dynamic system with nonlinearities, delays, and networked feedback loops. So it is very difficult for supply chain professionals to clearly understand supply chain operational mechanisms and thus make appropriate decisions within the limited time to adapt to the ever-changing, competitive, and turbulent business environment.

2. METHODOLOGY

Supply Chain Management (SCM) aims at integrating activities of an entire set of organizations from procurement of material and product components to delivery of completed products to the final customer which leads to improvements in channel performance among all channel members and not solely with in one company Kotzab (1999) and is characterized by highly competitive interactions among a large number of entities trying to achieve a multiplicity of objectives. System dynamics modeling is the technique of constructing and running a model of an abstract system in order to study its behavior without disrupting the environment of the real system. The application of system dynamics modeling to supply chain management has its roots in industrial dynamics Forrester (1961) System Dynamics (SD) is a methodology for understanding the behavior of complex, dynamic social-technological-economic-political systems to show how system structures and the policies used in decision making govern the behavior of the system. SD focuses on the structure and behavior of systems composed of interacting feedback loops. Sterman (2000) used industrial dynamics for analyzing business systems depending upon changing information and time. Thus system dynamics is very useful to craft future policies for running businesses in a complex environment. The research stages are of (1) Identifying the supply chain system for CPO Agro-Industry (2) Analyzing the dynamic systems with causal loop diagram and stock flow diagram (3) Developing a computer simulation model of the system (4) Validating the model.

which is expecting to increase performance through service effectiveness and cost efficiency can do it in two ways, namely (1) Reducing the barrier of supply chain integration, and (2) Increasing facilitation of supply chain integration (Richey et. al, 2010). The objective of supply chain integration (of key business processes among a network of interdependent suppliers, plantation, processor , and marketers in order to improve the flow of goods, services, and information from original suppliers to final customers), is reducing system-wide costs while maintaining the required service levels. The supply chain systems in this research focus on 3 sub-model. Production Sub-model (Plantation and Mill). Demand and Supply Sub-model (Domestic and Export Market) and Revenue and Cost Sub-Model (Sales Revenue, Production Cost, Logistics Cost). By understanding the supply chain system in CPO Agro-industry we can analyze the system by using Causal Loop Diagram (CLD) and Stock Flow Diagram (SFD).

Figure 5 : Palm Oil Supply Chain Source : RSPO

2.2. Analyzing with CLD and SFD

There are two structural ways to analyze any system dynamics: CLD and stock and flow diagram SFD. CLD diagrams can be used to show the governing inter-relations among a number of different variables using feedback loops. A positive feedback loop means the dependent variable moves in the same direction as that of the independent variable; as such, the polarities are assigned as a plus (+) sign on the arrowhead of feedback loops. In the case of negative feedback loops, if the independent variable increases, the dependent variable decreases and vice versa. Thus a minus (–) sign is assigned to the arrowhead of the feedback loop.

The other structure of system dynamics is a SFD which is used to explain both variables, i.e. the stocks and flows. Stocks refer to the status of variables at a point/moment of time while flows exist during a period of time. Stocks are accumulated over time through inflows and outflows. Apart from stock and flow variables, another kind of variable called an ‘auxiliary variable’ has been used here. Auxiliary variables are used to connect between stocks and flows as well as among themselves. By using all these three kinds of variables, we can explain dynamic systems more appropriately. Many researchers and authors have used stock and flow diagrams to model and describe supply chain performance variables (Agarwal and Shankar 2005, Campuzano and Mula 2011).

Figure 6 : Causal Loop Diagram Source : Lembito, 2013

Pr ofit

Logist ics Cost

P r oduct ion Cost Ex por t

Sa le s Re v e nue

D om e st ic Sa le s Re v e nue Ex por t

D e m a nd

D e cr e a se in D e m a nd

I ncr e a se in D e m a nd Low CP O

P r ice

High CP O P r ice

CP O D om e st ic

D e m a nd

Cook ing O il D e m a nd

Bio D ie se l D e m a nd

CP O D om e st ic

P r ice D e m a nd

Tot a l CP O

Holding Cost

Tr a nspor t Cost

I ndone sia P opula t ion St ock CP O

CP O P roduct iv it y

Mor t a lit y Ra t e Bir t h Ra t e

P opula t ion Gr ow t h Y ie ld

P a lm O il Mill P r oduct iiv it y

P a lm O il P r oduct ion

P a lm O il A r e a P a lm O il P r oduct iv it y

Ne w P la nt a t ion

P a lm O il D e m a nd

+ -+ + + + + + + + + -+ + + + + + + + + + + + + + + + + -+ P rice Cha nge + + La nd A v a ila bilit y

D e f or e st a t ion

+

+

-D e pula t ion

T o ta l Estat e Ar ea

Exp or t Dem an d

CPO Do m e stic Dem an d

Pr o fit To al Reve nu e

To al Reve nu e T ot al De m a nd

CPO p r o d uct io n

Espo r t Fr act ion

Con su m p t ium p er Cap ita M ill Ut ility Cou nt r y Est at e

Pr o du ct ivit y Pr od u ctiv ity Pr iva te Ow ned

Sm allh older Pr o du ctivity

Ad d itio na l La nd Fr act ion Ad dt io nal Lan d

Fr a ctio n Fr actio n Add it ion al Lan d

CPO Con ve r sio n Fa cto r

Biay a T r an spo r t Dom est ik Ho ld ing Co st

Co ok ing Oil De m a nd Sm a llho lde r Ar ea

Co nv er sion Fact or

Con ve r sion Fa cto r

Co n ver sio n Fact o r

Gov er nm en t Ow ned FFB St o ck Pr iv ate Ow ne d

FFB St ock Sm allh old er FFB

Sto ck

T ot al Pr od uct io n Co st

Do m e stic Sa les Rev en ue Pen j u ala n Eksp or Bala nce Sto ck

Diff er en ce Dem a nd a nd

Pr o du ction T o tal CPO St ock

Gove r nm en t Ow n ed Lan d Pr ivat e Ow n ed La nd

St o r a ge Cost

Ad dit ion al Lan d Go ver m en t

Ow n ed Ad dit io nal Lan d Pr ivat e Ow n ed Ad dit io na l La nd

Sm all h old er s

Po p ula tio n Gr ow th

Po pu lasi Pen d ud uk

Pr ice Ch an ge

Per u bah an Har g a Do m e stik

Dep o p ula tio n

Qua lity Fr act io n Qua lity Fr act io n Sm a llho lde r s

st ock

Pr iv at e Ow n ed CPO St o ck

Go ver n m e nt Ow n ed CPO Sto ck Yie ld Pr iva te

Ow n ed

Yie ld Go ve r nm en t

Ow n ed Yield sm allh old er s

To t al T r an spo r t Cost Exp or t Dem an d

Ex port Tra nsport Cost

St ora ge Cost Ex port

St o r a ge Cost Exp o r t Log istics

Co st

Dom est ic T ot al Tr a nsp or t Co stCPO Dom estic

Dem an d

T ot al St or ag e Co st Dom est ic

Lo g istics Cost T ot al Log ist ics

Cost

T ot al Log ist ics Co st Def or est at io n

environment, and scientific systems. We adopted Powersim Studio 2005 because it allows us to model the major variables – stocks, rates,

auxiliaries, flows and constant of CPO business processs – in the workspace, and connected with arrows. For each variable a number or equation has to be defined. Powersim is a flow-diagram-based modeling tool, which is able to show multiple models simultaneously and connecr separate models to each other.

2.4. Validating the Model

Model validation was undertaken by using the Mean Absolute Error (MAE) technique is applied to assess the overall reliability of each model.In statistic the mean absolute error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. MAE depends upon the units in which the variable is expressed. (Hyndman R, Koehler A. 2006). The magnitudes of the error give any indication of how large the error is, therefore, this error can be assessed only by comparing it with the average size of the variable in question. However, the main advantage of MAE is that it can be decomposed into various components, which show the deviation between the simulated and actual values.

The mean absolute error is an average of the absolute errors ei = | fi – yi |, where fi is the prediction and yi is the true value

2.5. Data Sources and Descriptions

In general, two groups of data were used in this study, namely, palm oil and macro-economic related data. The data sources for CPO included Oil world, 2010, USDA 2010, Indonesia Central Bureau of statistics (BPS) 2010 and palm oil outlook statistics 2010. Macro-economic related data were got from Ministry of Agriculture and BPS, 2010. The data covered the period from 2000 to 2010 hence the analysis was on the yearly basis interval.

3. RESULTS

3.1.CPO Production Sub-model

We capture the growth of plantation area from 3 plantation owner (small holder, private owned and government owned) from the year 2000 to 2010. From the data we analyze the annual growth rate of small holders, private owned and government owned to be is 12%, 8% and 2% (BPS, 2010). And from computer simulation we can predict the growth will continue for another 20 years, with the assumption there is no barrier in preparing the plantation area. In year 2030 the total area will be around 42 millions hectares around 5 times of total compare to area in year 2010. (Lembito, 2013)

Figure 8 : Growth of Plantation Area Source: Lembito , 2013

The production of Fresh Fruit Bunches (FFB) with the fraction of production around 15-17 tons per hectare will also grown significantly. The production of FFB in small holders in year 2010 was 7 millions tons, private owned was 12 millions tons and government owned was 1.5 millions tons. From computer simulation we can predict that the FFB production from small holder in year 2030 is around 61 millions tons, private owned is around 34 millions tons and government is around 1.7 tons millions. Total FFB production is around 97 millions ton. We assumed oil extraction rate of CPO is 20% and palm oil mill productivity rate is 90%. (Lembito, 2013).In the short term, it seems that land expansion will remain the main strategy increasing CPO production capacity. In the medium term, the trend of industry integration will increase, along with the growth of downstream industry for oleochemicals and biofuel. (PWC, 2013)

Figure 9 : Growth of FFB Production Source: Lembito, 2013

3.2.CPO Demand and Supply Sub-model

From the data we analyzed from year 2000-2010, we calculate the total demand domestic demand and export demand. The computation the domestic demand mainly come from the assumption of cooking oil with an average 9 kgs/capita/year cooking oil or equal to 13.5 kgs CPO capita/year. From BPS we find the Indonesan population around 237 millions in year 2010 with the birth rate is 0.011 and dead rate is 0.007 (Kuncoro, 2011). The export demand on year 2010 around 12.8 millions tons of CPO and domestic demand around 3.2 millions tons. With the computer simulation we can predict the demand in year 2030. The Export demand will be around 60 millions tons and domestic will be around 5 millions tons, and the estimated production will be around 80 millions ton . This means that Indonesia can increase the export market since there will be enough stock to sell. And Indonesia will have to handle competition from other country producers especially that of Malaysia.

The majority of CPO producers in Indonesia currently sells overseas. Markets in China and India are predominantly for culinary purposes (including cooking oil), whilst those in the EU are for biodiesel and confectionery manufacturing. The domestic market is mainly for cooking oil. The export profile for 2011 is dominated by CPO ahead of olein and biofuel, whereas the export markets

Figure 10 : Growth of CPO Production and Demand (Domestic + Export) Source : Lembito, 2013

3.3.CPO Revenue and Cost Sub-model

From the yearly price both in Domestic Market price and export market (CIF Rotterdam), the CPO price per ton in year 2000 was Rp. 2 millions domestically, while the CIF Rotterdam price was USD 311. In year 2010 the export price was around US$ 900, slightly going down compared to the price in year 2008 which was US$ 948. (USDA, 2010). The price drop is due to world economic crisis in year 2008. The production cost was estimated USD 400 per ton and we assumed the logistics cost of USD 60- USD 80 per ton. From the computer simulation we can predict the total revenue for Indonesia Palm Oil Industry is around in year 2030 to be around USD 53 billions or more than 3 times larger than that of the year 2010. And the gross profit excluding export tax seems promising still. (Lembito, 2013)

Figure 11 : Total Revenue and Total Cost Source : Lembito, 2013

3.4.Model Validation

For model validation we use data Total Growth Area and Total CPO production from year 2000-2010. The actual data taken from statistics and computation were compared with data simulation computed by Powersim Software studio 2005. From the analysis data we find that the forecast error is 3% and 3.4 %. And the range of accepted errors in MAE is 5 %. Model validations were done to prove the fitness of the model.

Figure 12 : Model Validation for Area Growth Source : Lembito, 2013

Figure 13 : Model Validation for Production Growth Source : Lembito, 2013

4. CONCLUSION

A supply chain is the connected series of value activities concerned with the planning and controlling of raw materials, components and finished products from suppliers to the final customers. Supply chain management provides an opportunity to the firms to look beyond their own organization and collaborate with the value chain partners for the mutual benefits. System dynamics considers the causal relationships among variables and through simulation permits the evaluation of such impact on the operating performance of whole supply chain.

In the practical application this research developing of the System Dynamic model for analyzing the Supply Chain Model would be important especially for production and export of Indonesian CPO Agro-industries. CPO which increases progressively since year 2000 to 2010 is one of the main export commodity for Indonesia. The existing CPO production growth and high growth of domestic consumption and export market since year 2000 to year 2010, based on simulation model the growth will continue to 2030.

Biodiesel from palm oil based appears to be a practicable long-term solution for Indonesia to overcome the drop in world palm oil price for the past four years and to provide an alternative renewable energy source to strengthen the country’s energy security. Although the fuel may offer many advantages as a direct fossil diesel fuel substitute, it also possesses several disadvantages such as higher price compared to mineral diesel fuel and fluctuation in raw material price. It might also accelerate the current tropical deforestation in Indonesia due to rapid development of palm oil

to predict some aspects of its behavior, such as plantations area growth, CPO production growth, demand and price fluctuations, with the purpose of optimizing the potential growth of CPO and challenges faced by of CPO agro-industries in Indonesia. For food and energy security.

REFERENCES

[1] Agarwal A, Shankar R. .2005. Modeling supply chain performance variables, Asian Academy of

Management Journal 10(2): 47-68.

[2] Statistik Indonesia. 2010. Badan Pusat Statistik.

[3] Campuzano F, Mula J. 2011. Supply chain simulation: A system dynamics approach for improving

performance, Springer.

[4] Christopher M. 2005. Logistics and Supply Chain Management: Strategies for Reducing Cost and

Improving Service, Prentice-Hall, London.

[5] Colin B, Zachary Z, RIA G. 2003. The Indonesian Oil Palm Industry

[6] Forrester JW. 1961. Industrial Dynamics. Portland (OR): Productivity Press.

[7] Hyndman R, Koehler A. 2006. Another look at measures of forecast accuracy. Journal of

Forecast-ing 22(4), 679–688

[8] James C. 2007 Indonesia Palm Oil Productions Prospect Continue to grow, Commodity Inteligence

Report, USDA Foreign Agricultural Service

[9] Kuncoro HW, Kharies P, Aang A. 2011. Supply Chain Management Agro Industri yang Berkelanjutan.

Lubuk Agung. Bandung

[10] Kotzab, H. 1999, Improving supply chain performance by Efficient Consumer Response .A critical comparison of existing ECR-approaches, Journal of Business and Industrial Marketing, Vol. 14, No. 5/6, pp. 364-377.

[11] Lembito. H. 2010. Extended Enterprise Resources Planning (ERP) for Palm Oil Industry, in proceeding of AFITA World Conference , The Quality Information for Competitive the Agricultural Based Production System and Commerce, Bogor, 2010

[12] Lembito. H. 2012. Achieving Competitive Advantage for Agribusiness through Supply Chain Management: A System Dynamics Simulation and SCOR model Approach, in proceedings of AFITA World Conference on Computers in Agriculture. Taipei. Taiwan. September 3-6, 2012

[13] Lembito. H. 2013 Designing A Supply Chain System Dynamic Model for Palm Oil Agro Industries, International Journal of Information Technology and Business Management, Vol. 12. No. 1

[14] Outlook Komoditas Pertanian. 2010. Pusat Data dan Informasi Pertanian. Kementerian Pertanian [15] Pahan I. 2005. Panduan Lengkap Kelapa Sawit, Manajemen Agribisnis dari Hulu hingga Hilir, Jakarta,

Penebar Swadaya

[16] Pahan I. 2012. Development of Palm Oil Industrial Cluster in Indonesia, an Analytical Network Process Study, Saarbrucken. Deutschland. Lambert Academic Publishing

[17] PWC.2012.Palm Oil Plantation Industry landscape, regulatory and financial overview, 2012 update [18] Pandey V.C, Garg S. 2009. Analysis of interaction among the anablers of agility in supply chain.

Journal of advances in management research, 16:1, pp.99-114.

[19] Rahmadi.A, Lu Ye. 2003. Biodiesel from Palm Oil as an Alternative fuel for Indonesia: Opportunities and Challenges, International Tecnology Center, Melboune University

[21] Richey Jr. RG, Roath AS. Whipple JN, Fawcett SE. 2010. Exploring a governance theory of supply chain management: Barriers and facilitators to integration. J Bus Log.31 (1): 237-256.

[21] USDA Foreign Agricultural Service. 2010. Commodity Intelligence Report

[22] Sterman J.D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World .McGraw-Hill Higher Education. New York.

Figure 6 : Causal Loop Diagram Source : Lembito, 2013

Figure 7 : Stock Flow Diagram

Source : Lembito, 2013

2.3. Developing Computer Simulation Model

System dynamics software is useful to model and simulate dynamic behaviors of a wide variety of systems such as business, economic market, team dynamics, electrical engineering, natural

Pr ofit

Logist ics Cost

P r oduct ion Cost Ex por t

Sa le s Re v e nue

D om e st ic Sa le s Re v e nue Ex por t

D e m a nd

D e cr e a se in D e m a nd

I ncr e a se in D e m a nd Low CP O

P r ice

High CP O P r ice

CP O D om e st ic

D e m a nd

Cook ing O il D e m a nd

Bio D ie se l D e m a nd

CP O D om e st ic

P r ice D e m a nd

Tot a l CP O

Holding Cost

Tr a nspor t Cost

I ndone sia P opula t ion St ock CP O

CP O P roduct iv it y

Mor t a lit y Ra t e Bir t h Ra t e

P opula t ion Gr ow t h Y ie ld

P a lm O il Mill P r oduct iiv it y

P a lm O il P r oduct ion

P a lm O il A r e a P a lm O il P r oduct iv it y

Ne w P la nt a t ion

P a lm O il D e m a nd

+ -+ + + + + + + + + -+ + + + + + + + + + + + + + + + + -+ P rice Cha nge + + La nd A v a ila bilit y

D e f or e st a t ion

+

+

-D e pula t ion

DOM ESTI C AN D EXPORT DEM AN D SUB M ODEL PRODUCTI ON SUB

M ODEL

SUB M ODEL PEN JUALAN DAN BI AYA

T o ta l Estat e Ar ea

Exp or t Dem an d

CPO Do m e stic Dem an d

Pr o fit To al Reve nu e

To al Reve nu e T ot al De m a nd

CPO p r o d uct io n

Espo r t Fr act ion

Con su m p t ium p er Cap ita M ill Ut ility Cou nt r y Est at e

Pr o du ct ivit y Pr od u ctiv ity Pr iva te Ow ned

Sm allh older Pr o du ctivity

Ad d itio na l La nd Fr act ion Ad dt io nal Lan d

Fr a ctio n Fr actio n Add it ion al Lan d

CPO Con ve r sio n Fa cto r

Pr od uctio n Cost

Biay a T r an spo r t Dom est ik Ho ld ing Co st

Ho ldin g Cost Co ok ing Oil

De m a nd Sm a llho lde r Ar ea

Co nv er sion Fact or

Con ve r sion Fa cto r

Co n ver sio n Fact o r

Gov er nm en t Ow ned FFB St o ck Pr iv ate Ow ne d

FFB St ock Sm allh old er FFB

Sto ck

T ot al Pr od uct io n Co st

Do m e stic Sa les Rev en ue Pen j u ala n Eksp or Bala nce Sto ck

Diff er en ce Dem a nd a nd

Pr o du ction T o tal CPO St ock

Gove r nm en t Ow n ed Lan d Pr ivat e Ow n ed La nd

St o r a ge Cost

Ad dit ion al Lan d Go ver m en t

Ow n ed Ad dit io nal Lan d Pr ivat e Ow n ed Ad dit io na l La nd

Sm all h old er s

Mo rt alit y Ra te Po p ula tio n

Gr ow th Po pu lasi Pen d ud uk

Pr ice Ch an ge

Per u bah an Har g a Do m e stik

Bir th Rat e

Dep o p ula tio n

Qua lity Fr act io n Qua lity Fr act io n Sm a llho lde r s

st ock

Pr iv at e Ow n ed CPO St o ck

Go ver n m e nt Ow n ed CPO Sto ck Yie ld Pr iva te

Ow n ed

Yie ld Go ve r nm en t

Ow n ed Yield sm allh old er s

FFB T o ta l St o ck

To t al T r an spo r t Cost Exp or t Dem an d Ex port Tra nsport

Cost

St ora ge Cost Ex port

St o r a ge Cost Exp o r t Log istics

Co st

Dom est ic T ot al Tr a nsp or t Co stCPO Dom estic

Dem an d

T ot al St or ag e Co st Dom est ic

Lo g istics Cost T ot al Log ist ics

Cost

T ot al Log ist ics Co st

To ta l Co st Def or est at io n

environment, and scientific systems. We adopted Powersim Studio 2005 because it allows us to model the major variables – stocks, rates,

auxiliaries, flows and constant of CPO business processs – in the workspace, and connected with arrows. For each variable a number or equation has to be defined. Powersim is a flow-diagram-based modeling tool, which is able to show multiple models simultaneously and connecr separate models to each other.

2.4. Validating the Model

Model validation was undertaken by using the Mean Absolute Error (MAE) technique is applied to assess the overall reliability of each model.In statistic the mean absolute error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. MAE depends upon the units in which the variable is expressed. (Hyndman R, Koehler A. 2006). The magnitudes of the error give any indication of how large the error is, therefore, this error can be assessed only by comparing it with the average size of the variable in question. However, the main advantage of MAE is that it can be decomposed into various components, which show the deviation between the simulated and actual values.

The mean absolute error is an average of the absolute errors ei = | fi – yi |, where fi is the prediction and yi is the true value

2.5. Data Sources and Descriptions

In general, two groups of data were used in this study, namely, palm oil and macro-economic related data. The data sources for CPO included Oil world, 2010, USDA 2010, Indonesia Central Bureau of statistics (BPS) 2010 and palm oil outlook statistics 2010. Macro-economic related data were got from Ministry of Agriculture and BPS, 2010. The data covered the period from 2000 to 2010 hence the analysis was on the yearly basis interval.

3. RESULTS

3.1.CPO Production Sub-model

We capture the growth of plantation area from 3 plantation owner (small holder, private owned and government owned) from the year 2000 to 2010. From the data we analyze the annual growth rate of small holders, private owned and government owned to be is 12%, 8% and 2% (BPS, 2010). And from computer simulation we can predict the growth will continue for another 20 years, with the assumption there is no barrier in preparing the plantation area. In year 2030 the total area will be around 42 millions hectares around 5 times of total compare to area in year 2010. (Lembito, 2013)

Figure 8 : Growth of Plantation Area

The production of Fresh Fruit Bunches (FFB) with the fraction of production around 15-17 tons per hectare will also grown significantly. The production of FFB in small holders in year 2010 was 7 millions tons, private owned was 12 millions tons and government owned was 1.5 millions tons. From computer simulation we can predict that the FFB production from small holder in year 2030 is around 61 millions tons, private owned is around 34 millions tons and government is around 1.7 tons millions. Total FFB production is around 97 millions ton. We assumed oil extraction rate of CPO is 20% and palm oil mill productivity rate is 90%. (Lembito, 2013).In the short term, it seems that land expansion will remain the main strategy increasing CPO production capacity. In the medium term, the trend of industry integration will increase, along with the growth of downstream industry for oleochemicals and biofuel. (PWC, 2013)

Figure 9 : Growth of FFB Production

Source: Lembito, 2013

3.2.CPO Demand and Supply Sub-model

From the data we analyzed from year 2000-2010, we calculate the total demand domestic demand and export demand. The computation the domestic demand mainly come from the assumption of cooking oil with an average 9 kgs/capita/year cooking oil or equal to 13.5 kgs CPO capita/year. From BPS we find the Indonesan population around 237 millions in year 2010 with the birth rate is 0.011 and dead rate is 0.007 (Kuncoro, 2011). The export demand on year 2010 around 12.8 millions tons of CPO and domestic demand around 3.2 millions tons. With the computer simulation we can predict the demand in year 2030. The Export demand will be around 60 millions tons and domestic will be around 5 millions tons, and the estimated production will be around 80 millions ton . This means that Indonesia can increase the export market since there will be enough stock to sell. And Indonesia will have to handle competition from other country producers especially that of Malaysia.

The majority of CPO producers in Indonesia currently sells overseas. Markets in China and India are predominantly for culinary purposes (including cooking oil), whilst those in the EU are for biodiesel and confectionery manufacturing. The domestic market is mainly for cooking oil. The export profile for 2011 is dominated by CPO ahead of olein and biofuel, whereas the export markets are dominated by India and China.

The drop in the global crude palm oil (CPO) price in 1999 due to oversupply has suggested palm oil plantations to find alternative uses of palm oil besides edible consumption. At the same time, Indonesia is also facing an increase in domestic fossil fuel consumption, while its oil exports have been decreasing for the past five years. To find an alternative fuel other than fossil based fuels would be beneficial for Indonesia in order to be self sufficient in the energy sector. One alternative is to convert crude palm oil to CPO-Methyl Ester, widely known as biodiesel fuel.

Figure 10 : Growth of CPO Production and Demand (Domestic + Export) Source : Lembito, 2013

3.3.CPO Revenue and Cost Sub-model

From the yearly price both in Domestic Market price and export market (CIF Rotterdam), the CPO price per ton in year 2000 was Rp. 2 millions domestically, while the CIF Rotterdam price was USD 311. In year 2010 the export price was around US$ 900, slightly going down compared to the price in year 2008 which was US$ 948. (USDA, 2010). The price drop is due to world economic crisis in year 2008. The production cost was estimated USD 400 per ton and we assumed the logistics cost of USD 60- USD 80 per ton. From the computer simulation we can predict the total revenue for Indonesia Palm Oil Industry is around in year 2030 to be around USD 53 billions or more than 3 times larger than that of the year 2010. And the gross profit excluding export tax seems promising still. (Lembito, 2013)

Figure 11 : Total Revenue and Total Cost

Source : Lembito, 2013

3.4.Model Validation

For model validation we use data Total Growth Area and Total CPO production from year 2000-2010. The actual data taken from statistics and computation were compared with data simulation computed by Powersim Software studio 2005. From the analysis data we find that the forecast error is 3% and 3.4 %. And the range of accepted errors in MAE is 5 %. Model validations were done to prove the fitness of the model.

Figure 12 : Model Validation for Area Growth Source : Lembito, 2013

Figure 13 : Model Validation for Production Growth

Source : Lembito, 2013

4. CONCLUSION

A supply chain is the connected series of value activities concerned with the planning and controlling of raw materials, components and finished products from suppliers to the final customers. Supply chain management provides an opportunity to the firms to look beyond their own organization and collaborate with the value chain partners for the mutual benefits. System dynamics considers the causal relationships among variables and through simulation permits the evaluation of such impact on the operating performance of whole supply chain.

In the practical application this research developing of the System Dynamic model for analyzing the Supply Chain Model would be important especially for production and export of Indonesian CPO Agro-industries. CPO which increases progressively since year 2000 to 2010 is one of the main export commodity for Indonesia. The existing CPO production growth and high growth of domestic consumption and export market since year 2000 to year 2010, based on simulation model the growth will continue to 2030.

Biodiesel from palm oil based appears to be a practicable long-term solution for Indonesia to overcome the drop in world palm oil price for the past four years and to provide an alternative renewable energy source to strengthen the country’s energy security. Although the fuel may offer many advantages as a direct fossil diesel fuel substitute, it also possesses several disadvantages such as higher price compared to mineral diesel fuel and fluctuation in raw material price. It might also accelerate the current tropical deforestation in Indonesia due to rapid development of palm oil plantations. Therefore, a comprehensive national food and energy policy should be formulated. (Rahmadi, 2003)

The growth CPO Agro-industries product yet faces main two risks that may hinder it. First, the unstable economic condition in Europe and USA may impact to world economy, and may reduce the growth of CPO demand. Second, to find an alternative fuel other than fossil based fuels would be beneficial for Indonesia in order to be self sufficient in the energy sector.

This research in CPO in Indonesia describes the complex supply chain for Agro-industrial products. By designing and constructing system dynamic model it will help the agribusiness sector

to predict some aspects of its behavior, such as plantations area growth, CPO production growth, demand and price fluctuations, with the purpose of optimizing the potential growth of CPO and challenges faced by of CPO agro-industries in Indonesia. For food and energy security.

REFERENCES

[1] Agarwal A, Shankar R. .2005. Modeling supply chain performance variables, Asian Academy of Management Journal 10(2): 47-68.

[2] Statistik Indonesia. 2010. Badan Pusat Statistik.

[3] Campuzano F, Mula J. 2011. Supply chain simulation: A system dynamics approach for improving performance, Springer.

[4] Christopher M. 2005. Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, Prentice-Hall, London.

[5] Colin B, Zachary Z, RIA G. 2003. The Indonesian Oil Palm Industry [6] Forrester JW. 1961. Industrial Dynamics. Portland (OR): Productivity Press.

[7] Hyndman R, Koehler A. 2006. Another look at measures of forecast accuracy. Journal of Forecast-ing 22(4), 679–688

[8] James C. 2007 Indonesia Palm Oil Productions Prospect Continue to grow, Commodity Inteligence Report, USDA Foreign Agricultural Service

[9] Kuncoro HW, Kharies P, Aang A. 2011. Supply Chain Management Agro Industri yang Berkelanjutan. Lubuk Agung. Bandung

[10] Kotzab, H. 1999, Improving supply chain performance by Efficient Consumer Response .A critical comparison of existing ECR-approaches, Journal of Business and Industrial Marketing, Vol. 14, No. 5/6, pp. 364-377.

[11] Lembito. H. 2010. Extended Enterprise Resources Planning (ERP) for Palm Oil Industry, in proceeding of AFITA World Conference , The Quality Information for Competitive the Agricultural Based Production System and Commerce, Bogor, 2010

[12] Lembito. H. 2012. Achieving Competitive Advantage for Agribusiness through Supply Chain Management: A System Dynamics Simulation and SCOR model Approach, in proceedings of AFITA World Conference on Computers in Agriculture. Taipei. Taiwan. September 3-6, 2012

[13] Lembito. H. 2013 Designing A Supply Chain System Dynamic Model for Palm Oil Agro Industries, International Journal of Information Technology and Business Management, Vol. 12. No. 1

[14] Outlook Komoditas Pertanian. 2010. Pusat Data dan Informasi Pertanian. Kementerian Pertanian [15] Pahan I. 2005. Panduan Lengkap Kelapa Sawit, Manajemen Agribisnis dari Hulu hingga Hilir, Jakarta,

Penebar Swadaya

[16] Pahan I. 2012. Development of Palm Oil Industrial Cluster in Indonesia, an Analytical Network Process Study, Saarbrucken. Deutschland. Lambert Academic Publishing

[17] PWC.2012.Palm Oil Plantation Industry landscape, regulatory and financial overview, 2012 update [18] Pandey V.C, Garg S. 2009. Analysis of interaction among the anablers of agility in supply chain.

Journal of advances in management research, 16:1, pp.99-114.

[19] Rahmadi.A, Lu Ye. 2003. Biodiesel from Palm Oil as an Alternative fuel for Indonesia: Opportunities and Challenges, International Tecnology Center, Melboune University

[21] Richey Jr. RG, Roath AS. Whipple JN, Fawcett SE. 2010. Exploring a governance theory of supply chain management: Barriers and facilitators to integration. J Bus Log.31 (1): 237-256.

[21] USDA Foreign Agricultural Service. 2010. Commodity Intelligence Report

[22] Sterman J.D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World .McGraw-Hill Higher Education. New York.