78
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
79
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
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