Evaluation of Market Operation Policy using System Dynamics
Meilita Tryana Sembiring and Falda Morihasian Pasaribu
Industrial Engineering of University of Sumatera Utara, St. Almamater, Padang Bulan, Medan Baru, Medan, Indonesia, 21055
ABSTRACT ARTICLE INFO
Rice is an important staple food for Indonesian people. Currently, retail rice price has been always above the highest retail price (HRP) set by the government, which is Rp.
9,950/kg. Logistics Affairs Agency (Badan Urusan Logistik BULOG) has an important role in maintaining food availability and stabilizing rice prices. In order to stabilize the prices, BULOG is assigned to carry out market operations. However, the price of rice has remained above the HRP for the last 10 years. For this reason, it is necessary to evaluate the impact of market operations on controlling rice prices. The evaluation is carried out by simulating the implementation of market operations using the System Dynamics with AnyLogic software. Three experimental scenarios were carried out, namely by increasing the absorption of BULOG and market operations by 10%, 30%
and 50%, then simulation was carried out in 60 months. Model verification is done to see if there is an error in the model. The model validation was carried out by means of the Mean Absolute Percentage Error (MAPE) test and the results obtained were 0.908%
so that the model can be said to be valid. Results show the largest decrease in rice prices for a 10% increase is 0.134%, for a 30% increase it is 0.401% and for a 50% increase it is 0.668%. It can be concluded that market operations have no significant impact on the decline in rice prices. The government should look for other alternatives to control rice prices apart from conducting market operations.
Received September 9, 2022 Accepted February 15, 2023 Available online March 19, 2023
*Correspondence Meilita Tryana Sembiring [email protected]
Keywords:
system dynamics; rice; market operation policy; AnyLogic
1. Introduction
Rice is an important staple food for Indonesian people that cannot be separated from people's lives. Indonesian household rice consumption in 2021 is estimated to reach 96.9 kg/capita/year, this amount is far above the consumption of other staple foods such as corn which is only 2 kg/capita/year (Kementrian Pertanian RI, 2017). This will also result in a continued increase in demand for rice along with the increase in population. For this reason, maintaining the availability of rice in the market is essential for the government. Rice supply should be available and accessible to the public in sufficient quantity and quality at any point (Aryani & Sufri, 2019). The lack of rice supplies in the market will result the increasing in rice prices, the increase in rice prices will result the increasing in the price of other necessities and will ultimately affect economic stability (Cahya et al., 2020).
Indonesia has met most of its domestic rice needs but still imports rice to meet domestic needs. Based on data from Badan Pusat Statistik (2020), Indonesia produced 31.3 million tons of rice and imported 356,286.3 tons. From these data, it can be concluded that almost all Indonesian rice is produced domestically. Although most of the rice production comes from domestic sources, the rice supply in various regions has not been stable, causing the availability of rice to be uneven and impacting rice price fluctuations.
Based on Presidential Regulation Number 48 of 2016, concerning Assignments to Logistics Affairs Agency (Perusahaan Umum Badan Usaha Logistik/ Perum BULOG) in the Context of Food Security, Perum BULOG is assigned to maintain food availability and stabilize prices for three basic food commodities, namely rice, corn, and
soybeans. However, in reality, the average price of rice in the market is always above the Highest Retail Price (HRP) set by the government. This can be observed from the average price of rice at the distributor level in Indonesia with medium quality which is still above the HRP for the last 10 years, as shown in the Figure 1.
Figure 1. Comparison between average price of rice and Highest Retail Price (HRP) in Indonesia
Particularly, in the province of North Sumatra, there is a similar phenomenon, where the price of medium quality rice at the retail level for the last 10 years has also been above the HRP.
Figure 2. Comparison between average price of rice and Highest Retail Price (HRP) in North Sumatra
Figure 2 indicates the retail price of rice is always above the HRP, even though according to the Regulation of the Minister of Trade of the Republic of Indonesia No. 57 of 2017 Article 3 states that the selling price of rice cannot be above the HRP, for that the government needs to control rice prices. The task of controlling rice prices is carried out by Perum BULOG. In particular, in Government Regulation No. 13 of 2016 the government continues the assignment to Perum BULOG to carry out its duties and responsibilities in the context of national food security in the form of:
0 2000 4000 6000 8000 10000 12000 14000
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Prices (Rp)
Year(s)
Indonesia Rice Price HRP
0 2000 4000 6000 8000 10000 12000 14000
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Prices (Rp)
Year(s)
North Sumatera Rice Price HRP for North Sumatera
1. Safeguarding the price of staple food for rice at the producer and consumer levels 2. Management of government rice staple food reserves
3. Provision and distribution of staple food rice to certain community groups
4. Implementation of rice imports in accordance with the provisions of the legislation.
In carrying out its duties to maintain food availability and stabilize rice prices, in the Minister of Agriculture Regulation No. 12 of 2017 concerning Market Operations Using Government Rice Reserves in the Context of Price Stabilization, BULOG is assigned to carry out market operations. The role of market operations is very important in maintaining the availability and stability of rice prices in the market so that people can obtain rice easily and at affordable prices. However, the government cannot simply increase the number of market operations that will be distributed to the public because an increase in the number of market operations will increase the government's absorption of the rice produced, this increase in rice absorption will also increase the amount of budget that must be issued by the government. Therefore, it is necessary to evaluate the impact of market operations on controlling rice prices using simulations to see if market operation policies are effective in controlling rice prices in the market without incurring large costs.
Market operations are complex systems and relate to other factors in the agricultural system. In its implementation, market operations involve many agents including rice producers, BULOG and consumers and the behaviour of each agent affects the price and availability of rice in the market. Therefore, this study uses a System dynamics approach which is the most effective model approach to analyze the behaviour of each agent in the system by considering the linear and non-linear relationships of each agent. Modeling using the system dynamics approach can also provide feedback on any changes made to variables so that it can be used as a tool for decision making and policy-making (Amiri et al., 2020).
2. System Dynamics
System dynamics is a method to improve the learning of complex systems. System dynamics is based on the theory of nonlinear dynamics and feedback control developed in mathematics, physics, and engineering. An airline uses flight simulators to help pilots learn, a dynamic system is in part a method of improving the control of flight simulators, usually computer simulation models, to help us learn about dynamic complexity, sources of understanding political resistance, and design more effective policies (Sterman, 2000).
The type of research that will be conducted is descriptive research with quantitative and qualitative approaches, namely research that aims to describe or systematically, factually, and accurately describe the facts and characteristics of a particular object or population with the type used is quantitative data where the data used is numerical data that can be tested by statistical methods and also qualitative data obtained through interviews. The object of this research is the market operation system of Perum BULOG, North Sumatra Regional Office in North Sumatra Province.
The data collected are the characteristics and behaviour of each agent involved and influencing the market operations system from farmers to final consumers, data on rice operational inventory of Perum BULOG North Sumatra in 2018-2020. The supporting data for this research was obtained through the Central Statistics Agency. The data obtained included data on harvest area, rice, and rice production in 2018-2020, North Sumatran rice Distribution pattern in 2018-2020 and population growth in North Sumatra in 2018. Data analysis was carried out using system dynamics approach to analyze the behavior of each agent and its relationship in the market operating system which was represented in a market operating system simulation model built using AnyLogic software. Model verification aims to ensure that the simulation model has been designed correctly. Model verification is done by using the Problems feature in AnyLogic software. If there are no errors or warnings in the Problems menu, then the model made is already possible to perform simulations.
Model validation is the process of determining whether the simulation model that has been created can represent the real system correctly. The model is said to be valid if the comparison results show that the model and the real system are not significantly different. Validation on modelling can be done by comparing the behavior of the model with the real system, namely the MAPE (Mean Absolute Percentage Error) test. MAPE is a relative measure of the percentage error. This test can be used to determine the suitability of the simulation data with actual data. The MAPE formula is shown in this Equation 1 below.
MAPE = Σ|
A-S A|
n ×100% (1)
A = Actual data
S = Simulation result data n = Period/lot of data
Based on Maricar (2019), the criteria for the accuracy of the model with the MAPE test are : MAPE < 10% : Very Good
10% - 20% : Good 20% - 50% : Worth MAPE<50% : Bad
3. Model Development 3.1 Rice producer characteristics
The characteristics of rice producers below illustrate the behavior of rice producers in producing rice through various activities and parties related to them. The characteristics of rice producers can be seen in Table 1 below.
Table 1. Characteristics of Rice Producers
Characteristics Explanation
Activity
1. Preparation for planting, farmers carry out agricultural land processing, purchase fertilizers, seeds and pesticides, check irrigation status, and weather conditions
2. Determination of planting time, for areas that determine the simultaneous planting policy
3. Rice Planting, farmers plant rice on the prepared land 4. Plant care, farmers take care of rice plants through the
application of pesticides and control other pests such as birds and rats
5. Harvesting, the harvesting process is carried out by slicing rice plants, threshing rice, drying and milling
6. Sales, In the sales process there is a price offer between the BULOG task force and the private sector (middlemen) to farmers
7. Milling, the rice is then ground in the mill to be processed into rice
Position Village
Decision
1. Determine the type of seeds, fertilizers and pesticides used 2. Determine the process of harvesting rice, farmers who use
harvesting by bonding/slashing are generally small farmers who do not have harvesting equipment and to cover expenses during the planting period
3. Determine the sale of rice, farmers sell rice to the private sector or the BULOG task force based on the highest purchase price offer
Technical Specifications
1. The average harvested area of North Sumatra for the last three years is 11,323 Ha
2. Production of rice into North Sumatran rice in 2020 is 1.16 million tons.
From the table above, we can see that rice producers include farmers, milling middlemen and BULOG. The characteristics of rice producers will greatly affect the amount of rice produced each month.
3.1.1 Characteristics of BULOG
The characteristics of the BULOG public company describe every activity they carry out related to their task of controlling rice prices which involves farmers, millers to consumers. The characteristics of BULOG can be seen in Table 2 below.
Table 2. Characteristics of BULOG
Characteristics Explanation
Activity
1. Contact the grinder to make a purchase of rice
2. Offering the purchase price of rice to the miller in accordance with Minister of Trade Regulation No. 24 Year 2020
3. If the grinder agrees on the price and quality offered in accordance with Minister of Trade No. 24 of 2020, BULOG will buy rice from the grinder
4. Send trucks to transport the milled rice to the BULOG storage warehouse
5. Receive instructions from the Ministry of Agriculture to carry out market operations
6. Checking the amount of Government Rice Reserves (CBP) 7. Distribute rice according to the number and location of the
target market operation
8. Provide a report on the results of Market Operations to the Ministry of SOEs to be forwarded to the Ministry of Agriculture
9. Provide reports on the use of Government Rice Reserves for Market Operations to the Ministry of Agriculture
Position Village and Province
Decision
1. Determining the purchase price of rice to farmers, the factors that influence the purchase price of rice are the laws and regulations.
2. Determine the amount of rice purchased from the grinder, the factor that affects the number of purchases is the amount of the budget.
Technical Specifications
1. The average amount of BOLUG absorption on farmers' harvests in 2018-2020 is 2.64%
2. The average amount of rice distributed each month for market operations is 1960.78 Tons
From the description above, BULOG has a role in controlling producer and consumer-level prices through BULOG's absorption of crop yields and market operations carried out by BULOG.
3.1.2 Consumer characteristics
Consumer characteristics describe how their behaviour in consuming rice and how they get rice supplies to meet their needs. Characteristics of rice consumers can be seen in Table 3. below.
Table 3. Consumer characteristics
Characteristics Explanation
Activity
1. Contact the rice seller or directly visit the rice seller to buy rice
2. Buy rice according to the consumer's purchasing ability and the type of rice desired
Position Regency
Decision
1. Determining the place to buy rice, the factor that affects the place to buy rice is the distance of the consumer from the place of purchase
Characteristics Explanation
2. Determine the type of rice and the price of rice to be purchased, the factors that influence the purchase of this type of rice are income or the purchasing ability of consumers
Technical Specifications
1. The average consumption of rice per capita of the people of North Sumatra in 2020 is 7.18 kg/month
2. The average per capita expenditure for rice consumption of the people of North Sumatra in 2020 is Rp. 76,863/month
From the characteristics of the consumers above, we can see that rice is an important staple food for the community and the main factors that consumers consider in buying rice are the availability of rice in the vicinity and the price of rice itself.
3.2 System dynamics model 3.2.1 Conceptual model
After knowing the characteristics and behavior of each agent involved, a model is designed based on the relationship between the behavior of each agent. The design of this model is made in the form of a conceptual model.
The conceptual model is a big picture of the model to be made. The conceptual model describes the relationship between several variables of interrelated agent behavior. Figure 3 below shows the conceptual model which is described by a causal loop diagram.
Figure 3. Causal loop diagram
3.2.2 Simulation model
In making the rice price simulation model, it is made by describing the behavior that may occur in the system using stock, flow, link, and dynamic variables in AnyLogic software. The model is simulated from month to month according to the variables in the simulation that dynamically affect rice prices. Rice prices are influenced by rice production, government rice reserves, market operations and consumer demand. The overall appearance of the simulation model can be seen in Figure 4 below.
Figure 4. Stock and flow diagram
3.2.3 Model implementation
After making a simulation model, then the mathematical formulation is prepared using a programming language based on the behavior of the agent and the data that has been obtained previously. The steps taken in formulating the model are as follows:
1. Determining the type of variable distribution
Determination of variable distribution type is done using EasyFit software. The data that has been obtained is inputted into the EasyFit software and then clicked on the "Generate" menu to display possible distribution patterns of the data. Then the type of data distribution with the highest ranking is selected.
2. Parameterization
After obtaining the type of data distribution of the desired variable, then the type of distribution and the parameters of the selected distribution are input into the dynamic variables in AnyLogic software.
Table 4. Dynamic system model formulation
Variable Name Type Remarks
Rice Production
faseV1 dynamic pert(17.49, 67.5, 19.625)
faseV2 dynamic faseV1*pert(0.569185185, 1.051422319, 0.80977) faseGeneratif dynamic (faseV2+faseV1)*weibull(6.602, 0.59918, 0.371403144) luaspanen dynamic (faseGeneratif+faseV2)*normal(0.10368, 0.51079)
ProduktivitasPadi exogeneous 6.01
produksiGKP dynamic luaspanen*ProduktivitasPadi
konversiGKP exogeneous 0.8574
produksiGKG dynamic produksiGKP*konversiGKP
konversiGKG exogeneous 0.6368
produksiBeras dynamic (produksiGKG*konversiGKG*1000)
beraskeluar (uniform(0.0006, 0.0161))*produksiBeras
produksiBerasBersih dynamic produksiBeras-beraskeluar-serapanBULOG berasmasukprovlain dynamic lognormal(1.9471, 0.62419, 2.062)+erlang(32.835, 27,
677.331)
Government Rice Reserve
serapanBULOG dynamic lognormal(6.9383, 1.229, 252.35) pemasukanBULOG dynamic weibull(0.788, 10400, 239.35) PengeluaranBULOG dynamic erlang(11026, 1, 1430.7)
AliranberasBULOG stock serapanBULOG - PengeluaranBOLUG +
pemasukanBULOG - OperasiPasar
StokCBP stock Initial value: 24108.07775;
Variable Name Type Remarks
Rice Consumption
angkakelahiran dynamic uniform(25000, 28000)
kelahiran flow angkakelahiran
jumlahkematian dynamic uniform(7000, 8000)
kematian flow jumlahkematian
jumlahpenduduk stock Initial value: 14262147;
kelahiran- kematian
tingkatkonsumsi dynamic uniform(0.0073892, 0.0074328) konBerasWisatawan exogeneous 0.004484167
wisatawan dynamic uniform(14869, 26609)*konBerasWisatawan
konsumsiberas dynamic (jumlahpenduduk*tingkatkonsumsi) + wisatawan
IHK dynamic beta(691.48, 798.89, 0.99215, 1.000508)
Market
Operation OperasiPasar dynamic beta(1.13, 3.17, 11.74, 7437.86) Rice Price HargaBeras
dynamic (0.00546*OperasiPasar)+(0.000557*StokCBP)+(0.074*konsu msiberas)+(0.000737*produksiBeras)-
(0.01062*serapanBULOG)+2827.55688
3.3 Model verification
Model verification aims to ensure that the simulation model has been designed correctly. Model verification is done by using the Problems feature in AnyLogic Software, as shown in Figure 5.
Figure 5. Model verification by using AnyLogic software
3.4 Model testing
The following is the actual and simulated data on the price of rice for the Province of North Sumatra per month in 2018-2020 which is carried out using the MAPE test.
Table 5. Actual and simulated data of North Sumatran rice prices in 2018-2020 Month Actual Simulation |(A-S)/A|
1 10800 10674.11 0.0117
2 10850 10619.30 0.0213
3 10800 10724.68 0.0070
4 10700 10706.71 0.0006
... .... ....
... .... ....
34 11000 10994.35 0.0005
35 11000 10974.96 0.0023
36 11000 11060.13 0.0055
MAPE 0.00908
%MAPE 0.908%
Based on the MAPE calculation that has been done, the simulation model deviation results are 0.00908 or 0.908%.
The simulated MAPE value below 10% indicates a model with very good criteria, thus it can be said that the model is acceptable (valid).
3.5 Experiments
Experiments on the model are carried out aiming to understand the behavior of the system according to how the system works. Simulation experiments in this study were carried out with several scenarios. The model simulation scenario is carried out by increasing the absorption of BULOG and Market Operations by 10%, 30% and 50% for 60 months (5 years), then observing the effect on the retail price of rice in the market.
Table 6. Results of rice price experiment simulation Month
10% Increase 30% Increase 50% Increase
BULOG Absorption
Market Operation
Rice Price
BULOG Absorption
Market
Operation Rice Price BULOG Absorption
Market Operation
Rice Price
1 7314.82 6646.73 10670.40 8644.79 7855.22 10662.96 9974.75 9063.72 10655.53
2 11102.21 6397.27 10611.85 13120.80 7560.41 10596.95 15139.38 8723.54 10582.04
3 4419.19 1095.90 10721.06 5222.68 1295.16 10713.81 6026.17 1494.41 10706.57
4 1268.42 1136.94 10706.17 1499.04 1343.66 10705.08 1729.66 1550.38 10703.99
5 1636.98 1337.99 10728.73 1934.62 1581.26 10727.18 2232.25 1824.53 10725.62
6 3551.67 2302.91 10732.88 4197.43 2721.62 10728.64 4843.19 3140.33 10724.39
7 480.31 3298.14 10778.77 567.63 3897.80 10781.48 654.96 4497.47 10784.20
8 660.98 2641.89 10761.15 781.16 3122.23 10762.90 901.34 3602.57 10764.66
9 1284.23 1018.08 10815.50 1517.73 1203.19 10814.51 1751.22 1388.29 10813.52
10 4160.40 688.27 10838.06 4916.83 813.41 10831.29 5673.27 938.55 10824.51
11 ... ... ... ... ... ... ... ... ...
12 ... ... ... ... ... ... ... ... ...
13 ... ... ... ... ... ... ... ... ...
14 ... ... ... ... ... ... ... ... ...
54 639.52 1660.97 11269.86 755.80 1962.96 11273.28 872.07 2264.96 11276.71
55 586.03 4570.06 11272.40 692.58 5400.98 11278.88 799.13 6231.90 11285.36
56 1260.11 2336.98 11302.73 1489.22 2761.88 11305.74 1718.33 3186.79 11308.75
57 358.31 2382.73 11317.01 423.46 2815.95 11321.84 488.61 3249.17 11326.68
58 432.22 2431.48 11396.68 510.80 2873.57 11401.46 589.39 3315.65 11406.25
59 544.25 2638.27 11318.42 643.20 3117.96 11323.23 742.16 3597.65 11328.03
60 1346.03 718.16 11364.49 1590.77 848.74 11365.93 1835.50 979.31 11367.36
4. Discussion
Agents are components that represent actors in real systems such as individuals, goods or organizations which are programmed to interact with each other, both between agents and the computing environment. The activities carried out by agents at each part of the rice supply chain system in North Sumatra Province are producing rice, absorbing rice produced, distributing rice and consuming rice. Each of these behaviors causes changes in the price of rice according to the law of supply and demand.
Causal loops are used to help modelers understand the behaviour of each agent in the system, as well as provide an overview of the causal relationships between agents in the system. The factors of planting area, harvested area, rice production, rice prices, supply availability and price stabilization of BULOG and rice consumption are interrelated. The causal loop was then developed into a simulation model using stock, flow, and dynamic variables to determine the behavior of each agent and its dynamics as well as the influence of rice production factors, CBP stock, BULOG absorption, market operations and rice consumption on rice prices.
MAPE value generated by the model that has been made in this study is 0.908%, which means that there is a deviation of 0.908% between the simulation results and the actual data. MAPE value < 5% can be categorized as very precise and acceptable (valid).
Experiments have been carried out on simulation models with several scenarios to see the effect of increasing BULOG absorption and market operations on rice prices in the market. This Figure 5 below shows the results of experimental simulation.
Figure 6. Results of experimental simulation (Note: y-axis – retail price, x-axis – simulation time-step)
From the experimental simulation results that have been carried out, it can be seen that the scenario carried out does not have a significant impact on retail rice prices in North Sumatra Province. From the calculation results, the biggest decrease in rice prices for a 10% increase is 0.134%, for a 30% increase it is 0.401% and for a 50% increase it is 0.668%. For this reason, it can be concluded that the strategy to increase BULOG's absorption and market operations is not effective in reducing retail rice prices in the market. This also indicates that the increase in rice prices controlling rice prices at the consumer level is not effective enough to be carried out and further research needs to be carried out to thoroughly see what are the main factors causing rice prices to be above the HET set by the government.
5. Conclusion
This study aims to evaluate the impact of market operations on controlling rice prices using simulations to see if market operation policies are effective in controlling rice prices in the market without incurring large costs. Using a System dynamics approach which is the most effective model approach to analyze the behaviour of each agent in the system by considering the linear and non-linear relationships of each agent and providing feedback on any changes made to variables so that it can be used as a tool for decision making and policy making. Verification is carried out using the Problem menu in the AnyLogic software and shows that the model has no errors and can be run. Then the model is said to be valid because the MAPE (Mean Absolute Percentage Error) value of the simulation model is < 5% (0.908%). Experimental simulations were carried out using 3 scenarios, namely increasing the absorption of BULOG and market operations by 10%, 30%, and 50% and seeing their effect on retail rice prices. From the experimental results, it is found that the scenario carried out does not have a significant effect on rice prices with the largest decrease in rice prices for an increase of 10% is 0.134%, for an increase of 30% is 0.401% and for an increase of 50% is 0.668%. Further research can enrich the variables used so as to increase the accuracy of the simulations that will be produced.
Declaration of Conflicting Interests. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
References
Amiri, A., Mehrjerdi, Y. Z., Jalalimanesh, A., & Sadegheih, A. (2020). Food system sustainability investigation using system dynamics approach. Journal of Cleaner Production, 277, 124040. https://doi.org/10.1016/j.jclepro.2020.124040
Aryani, D. and Sufri, M. (2019). The impact of the highest retail price on rice price and rice availability at the traditional market.
Journal of Social and Agricultural Economics, 12(3), 49–58.
Badan Pusat Statistik. (2020). Statistik luas panen dan produksi padi. Berita Resmi Statistik, 2(16), 1–12.
Cahya, M. R., Wibowo, A. S., & Bukhari, A. (2020). Keberlanjutan ketersediaan beras di Kabupaten Pandeglang Provinsi Banten.
Jurnal Agribisnis Terpadu, 11(2), 181. https://doi.org/10.33512/jat.v11i2.5095
Kementerian Pertanian Republik Indonesia. (2017). Peraturan Menteri Pertanian Republik Indonesia Nomor 12 Tahun 2017 Tentang Operasi Pasar Menggunakan Cadangan Beras Pemerintah Dalam Rangka Stabilisasi Harga.
Sterman, J. (2000). Business dynamics: Sytems thinking and modeling for a complex world. Irwin/McGraw-Hill.
Maricar, M. A. (2019). Analisa perbandingan nilai akurasi moving average dan exponential smoothing untuk sistem peramalan pendapatan pada perusahaan XYZ. Jurnal Sistem dan Informatika, 13(2), 36–45.
To Cite This Article: Sembiring, M. T. and Pasaribu, F. M. (2023). Evaluation of market operation policy using system dynamics. Journal of Industrial Engineering and Education, 1(1), 46-56.
