Strategy Design Dynamic System-Based Circular Supply Chain Management on Waste Management in Sleman Regency, Yogyakarta

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5936 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 Strategy Design Dynamic System-Based Circular Supply Chain Management on Waste Management in Sleman Regency, Yogyakarta Dwinaz Fadzil Muhammad1, Elisa Kusrini2 1,2 Master of Industrial Engineering, Islamic University of Indonesia, Yogyakarta, Indonesia ABSTRACT: This study aims to design a dynamic system-based Circular Supply Chain Management (CSCM) strategy for waste management in Sleman Regency, Yogyakarta. The increase in population and economic activity has led to a significant increase in waste volume, necessitating a sustainable and efficient management model. The CSCM approach is used because it integrates circular economy principles through the 5R concept (Reduce, Reuse, Recycle, Recover, and Redesign) to create a closed-loop supply chain system for waste management. The research method uses a system dynamics approach. Modeling with qualitative and quantitative analysis stages through the creation of Causal Loop Diagrams (CLD) and Stock and Flow Diagrams (SFD). Research data were obtained from field observations, interviews with the Environmental Agency, and supporting literature. Simulation results show that the existing waste management model in Sleman has implemented some CSCM principles but is still semi-linear. Through the development of four simulation scenarios, Scenario 4, which integrates the Organic Processing Center (OPC), the Plastic Shredding 3RWPS (Reduce, Reuse, Recycle (3R) Waste Processing Site), and the Compost IWPS (Integrated Waste Processing Site), is considered the most optimal in reducing waste generation and increasing management efficiency. This scenario successfully forms a multi-loop circular chain system that minimizes waste, maximizes resource utilization, and shifts the paradigm from waste management to resource management. The resulting model is expected to be a strategic reference for local governments in formulating long-term policies towards sustainable waste management. KEYWORDS: Circular Supply Chain Management, Dynamic System, Waste Management, 3RWPS, IWPS, Scenario INTRODUCTION Rapid economic growth and population growth at urban areas have led to a significant increase in waste generation, which has implications for various environmental and public health problems (Zhou et al., 2019; Tirkolaee et al., 2020). In developing countries, including Indonesia, waste management is a major challenge due to limited landfill space and a centralized management system (Azevedo et al., 2019; Kuznetsova et al., 2019). This situation demands a more efficient and sustainable management system that integrates all processes, from collection and transportation to waste processing (Mohammadi et al., 2019). Sleman Regency is one of the regions facing a waste management crisis due to the permanent closure of the Piyungan Landfill by the Yogyakarta Special Region Government (jogjaprov.go.id, 2024). The decentralized waste management policy encourages each district/city to be independent in managing its waste through a sorting and processing initiative at the source (SE No. 030 of 2022). However, the implementation of this policy still faces obstacles, such as low community participation, a lack of processing facilities, and suboptimal coordination between the local government and the community. The resulting impacts include the accumulation of waste in various locations and a decline in environmental quality around densely populated areas (prambanan.slemankab.go.id, 2024). The implementation of decentralization in waste management in Yogyakarta, including in Sleman Regency, has not been running well. The closure of the Piyungan Landfill has had significant negative impacts, such as the accumulation of waste in various places and roads due to the lack of available final disposal sites. The participation of the Environmental Agency in providing adequate facilities and supervision also seems to be less than optimal, resulting in ineffective coordination and execution of waste management policies. So that disposal at the Piyungan Landfill is still carried out but is limited, waste disposal at the Piyungan Landfill is carried out because unprocessed waste still remains despite the Decentralization policy being established, waste disposal at the Piyungan Landfill is carried out according to policies and procedures by the DIY Provincial government, not the Regency government. The Piyungan Landfill is also undergoing a development process to become the Piyungan IWPS, this is certainly a very significant improvement in processing waste that was previously disposed of in landfills into a 3R waste site downstream. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5937 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 In a global context, the concepts of Circular Economy (CE) and Circular Supply Chain Management (CSCM) have emerged as strategic approaches to creating a sustainable waste management system. CE seeks to transform the linear "take-use-dispose" paradigm into a closed-loop system based on the 5R principles reduce, reuse, recycle, recover, and redesign to minimize waste and maximize resource value (Lacy et al., 2020;Zaenafi Ariani, 2022). CSCM is the application of CE principles in an integrated supply chain, from product design and reverse logistics to recycling processes, to simultaneously generate economic value and environmental efficiency (Genovese et al., 2017). Previous research has shown that a circular supply chain approach combined with system dynamics methods can be used to comprehensively evaluate the sustainability of waste management systems through simulations and policy scenarios (Theeraworawit et al., 2022; Vega et al., 2024; Vegter et al., 2023). Based on this, this study aims to design a system dynamics-based Circular Supply Chain Management strategy for waste management in Sleman Regency. This model is expected to provide a visual representation of interactions between variables, predict long-term policy impacts, and serve as a basis for strategic decision-making to improve the effectiveness and sustainability of the regional waste management system. METHOD This research method section details the research workflow and its explanation. This section explains the methods used in the research to achieve the objectives of the problem formulation. This method includes a discussion of the research object and subject, research scope, population and sample, variables and operational definitions, research instruments, data collection, data analysis, and research procedures. Furthermore, there is a research flow, which systematically describes the steps taken by the researcher from the beginning to the end of the research process. Figure 1 Research Flow A. Waste Management Concept The definition of waste management includes various human activities related to waste processing activities, namely participation in programs, both residential, institutional, industrial, with various collection methods, including door-to-door International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5938 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 and selective waste collection from commercial areas, according to Fores et al., by optimizing economic and environmental aspects and the application of various network entities: municipal waste bins, curbside collection, self-delivery, and Contract or delegated services are fulfilled. Figure 2 General Waste Management & Waste Management Hierarchy B. Supply Chain From a supply chain perspective, in municipal waste management, the supply chain is considered a strategic supply chain issue, because the supply chain includes the generation, collection, separation, distribution, processing, and disposal of waste. Therefore, it is very important to consider the entire supply chain when considering a waste management system, because the implementation of appropriate supply chain management techniques can improve the efficiency of municipal waste management (Ahmad et al., 2016). C. Circular Economy Circular Economy (CE) is identified as an alternative model to the linear economy (make, use, and dispose). The CE philosophy is based on effective driving forces that have great potential to support industrial performance in a sustainable and economical manner (Hobson 2018). CE is defined as a regenerative system as it is shown in the following Figure. Figure 3 Linear economy & Circular economy This idea has developed into a powerful driving force for sustainability both in practice and writing (Hobson et al,2016) and can help industry achieve innovation in sustainability. D. Circular Supply Chain Management The resources of the planet Earth are limited so the introduction of the term circular supply chain management (CSCM) is becoming increasingly important in this era, as well as environmental degradation a factor that shifts the economy and value chains towards sustainable and circular practices. In a linear supply chain, raw materials are taken from the environment and End of Life (EOL) waste materials are disposed of in landfills as shown in the figure. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5939 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 Figure 4 Linear and Closed Supply Chain (Faroouqe et al) CSCM aims for a full reflection on environmental degradation and resource utilization. It helps minimize the impact of the supply chain on manufacturing industry management and resource utilization. It introduces new supply chain design from a sustainability perspective. E. Dynamic System System dynamics is a method for enhancing learning in complex systems, and in part, is a method for constructing a management flight simulator, a computer simulation model, to assist in studying dynamic complexity, understanding sources of policy resistance, and designing more effective policies (Sterman, 2000, p. 4).Shown a series of processes in system dynamics described by Jay Forrester in his journal, “System Dynamics, System Thinking and Soft OR ”: Figure 5 Dynamic System Process (Forrester 1994) The main focus of dynamic systems methodology is gaining understanding of a system, so that problem solving steps provide feedback on system understanding. a. Causal Loop Diagram Loop diagrams are an important tool for representing the feedback structure of a system. They are useful for (Sterman, 2000, p. 137). This is shown in the figure below: International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5940 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 Figure 6 How to Write a Cause-Effect Loop Diagram & Circular Procurement The variables are related in a causal way, as indicated by the arrows in the example above, the birth rate is determined by the population and the fractional birth rate. Each causal relationship is determined by a polarity, either positive (+) or negative (-) which indicates how variable A, which depends on variable B, changes when variable B changes. b. Flow Diagram ( Stock and Flow Diagram ) Loop diagrams have several limitations and can easily be misused. One of the most important limitations of cause-and-effect diagrams is their inability to capture the stock and flow structure of the system. Figure 7 How to Write a Flowchart (Sterman 2000) The image above illustrates how to write a flowchart in a dynamic system, with the following explanations. Stock is represented by a rectangle. Inflow is represented by a pipe with an arrow pointing toward the stock, meaning it increases stock. Outflow is represented by a pipe pointing away from the stock, meaning it decreases stock. c. Dynamic Structure and Behavior The behavior of a system emerges from its structure. A structure consists of feedback loops, stocks and flows, and nonlinearities created by the interaction of the physical and institutional structure of the system with the decision-making processes of the agents acting within it. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5941 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 Figure 8 General behavioral model (Sterman 2000) & Key Behavioral Validation formula The three basic forms of dynamical system behavior are exponential growth, goal seeking, and oscillation. Each of these three behaviors is shaped by a simple feedback structure: growth arises from positive feedback, goal seeking arises from negative feedback, and oscillation arises from negative feedback with a time delay in the loop. RESULT AND ANALYSIS A. Data collection The process of collecting data and information relevant to research is called data collection. The purpose of data collection is to gather data that can be used in the data processing process. Furthermore, as the basis for the study, data collection also serves as an initial foundation for the research to proceed to the next stage. B. Waste Management in Sleman Regency In managing waste, the people of Sleman Regency have implemented a Decentralized system of "reduce from source, sort, and process" which previously used a system of collecting, transporting, and disposing of waste from the source to the landfill. Waste sources come from community activities ranging from residential areas, schools, hospitals, markets and others. Some waste from the source is managed independently by the community and the rest is handled by the Environmental Agency (EA) and the Regional Technical Implementation Unit (RTIU) of Sleman Regency to be transported to the 3R Waste Processing Site (3RWPS), the Integrated Waste Processing Site (IWPS) and the Final Waste Disposal Site (Landfill). C. Existing Waste Management Model in Sleman Regency Existing model represents the actual condition of the waste management system in Sleman Regency, built on empirical data. This model serves to depict the actual situation before interventions or improvement simulations are implemented, and also serves as the basis for developing the proposed model. a. Causal Loop Diagram (CLD)&Stock Flow Diagram (SFD)of Existing Waste Management Model in Sleman Regency Describe cause-and-effect interactions in existing models waste management in Sleman Regency. Figure 9 Casual Loop Diagram (CLD) & Stock Flow Diagram (SFD) of the Existing Waste Management Model in Sleman Regency 24 25 26 27 28 29 30 31 32 33 500.000 1.000.000 1.500.000 Population (Individu) To tal W aste (Ton) 3RW P S (Ton) IW P S (To n) La ndfill (T o n ) Non-commercial use only! Time Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Open Dumping (Ton) 01 Jan 2024 01 Jan 2025 01 Jan 2026 01 Jan 2027 01 Jan 2028 01 Jan 2029 01 Jan 2030 01 Jan 2031 01 Jan 2032 01 Jan 2033 01 Jan 2034 1.318.086,00 1.346.293,00 1.375.104,00 1.404.531,00 1.434.588,00 1.465.288,00 1.496.645,00 1.528.674,00 1.561.388,00 1.594.801,00 1.628.929,00 219.654,00 226.133,55 231.025,28 235.970,83 241.020,61 246.178,46 251.446,65 256.827,58 262.323,82 267.937,62 273.671,37 20.791,00 21.608,05 22.354,08 22.986,17 23.560,09 24.108,00 24.647,22 25.187,10 25.732,72 26.286,94 26.851,37 13.359,00 14.361,54 14.865,81 15.213,04 15.542,54 15.875,68 16.215,49 16.562,51 16.916,94 17.278,97 17.648,75 9.198,00 13.092,76 14.865,46 15.618,54 16.046,39 16.341,04 16.575,57 16.784,75 16.986,51 17.190,30 17.400,99 9.198,00 9.657,90 10.312,54 11.055,81 11.836,74 12.639,06 13.456,11 14.284,89 15.124,13 15.973,45 16.832,97 Non-commercial use only! Population Total Waste Immigration Birth Emigration Death Immigration Rate Birth Rate Death Rate Emigration Rate Number of Individuals Waste Input Average Waste per Individual 3RWPS Waste Processed at 3RWPS Processed Waste at 3RWPS IWPS Waste Processed at IWPS Processed Waste at IWPS 3RWPS Level IWPS Level Waste Output Average Waste Generation Annual Decrease Fraction Waste per Year Average Waste Received at 3RWPS Average Waste Processed at 3RWPS Average Waste Received at IWPS Average Waste Processed at IWPS Average Waste Received at IWPS RDF Average Waste Processed at IWPS RDF Landfill Waste Received at Landfill 3RWPS Efficiency Efficiency IWPS RDF Average Waste Received at Landfill Waste Reduction Percentage 3RWPS Waste Management Performance Ratio Efficiency IWPS IWPS Waste Management Performance Ratio IWPS Rate Number of IWPS Facilities Waste Entering Landfill Unprocessed Waste Residual Waste from IWPS and 3RWPS Open Dumping Waste in Open Dumping Number of 3RWPS Facilities 3RWPS Rate Total Waste at Landfill International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5942 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 24 25 26 27 28 29 30 31 32 33 500.000 1.000.000 1.500.000 Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Non-commercial use only! Time Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Open Dumping (Ton) 01 Jan 2024 01 Jan 2025 01 Jan 2026 01 Jan 2027 01 Jan 2028 01 Jan 2029 01 Jan 2030 01 Jan 2031 01 Jan 2032 01 Jan 2033 01 Jan 2034 1.318.086,00 1.346.293,00 1.375.104,00 1.404.531,00 1.434.588,00 1.465.288,00 1.496.645,00 1.528.674,00 1.561.388,00 1.594.801,00 1.628.929,00 219.654,00 226.133,55 231.025,28 235.970,83 241.020,61 246.178,46 251.446,65 256.827,58 262.323,82 267.937,62 273.671,37 20.791,00 21.608,05 22.354,08 22.986,17 23.560,09 24.108,00 24.647,22 25.187,10 25.732,72 26.286,94 26.851,37 13.359,00 14.361,54 14.865,81 15.213,04 15.542,54 15.875,68 16.215,49 16.562,51 16.916,94 17.278,97 17.648,75 9.198,00 13.092,76 14.865,46 15.618,54 16.046,39 16.341,04 16.575,57 16.784,75 16.986,51 17.190,30 17.400,99 9.198,00 9.657,90 10.312,54 11.055,81 11.836,74 12.639,06 13.456,11 14.284,89 15.124,13 15.973,45 16.832,97 Non-commercial use only! Population Total Waste Immigration Birth Emigration Death Immigration Rate Birth Rate Death Rate Emigration Rate Number of Individuals Waste Input Average Waste per Individual 3RWPS Waste Processed at 3RWPS Processed Waste at 3RWPS IWPS Waste Processed at IWPS Processed Waste at IWPS 3RWPS Level IWPS Level Waste Output Average Waste Generation Annual Decrease Fraction Waste per Year Average Waste Received at 3RWPS Average Waste Processed at 3RWPS Average Waste Received at IWPS Average Waste Processed at IWPS Average Waste Received at IWPS RDF Average Waste Processed at IWPS RDF Landfill Waste Received at Landfill 3RWPS Efficiency Efficiency IWPS RDF Average Waste Received at Landfill Waste Reduction Percentage 3RWPS Waste Management Performance Ratio Efficiency IWPS IWPS Waste Management Performance Ratio IWPS Rate Number of IWPS Facilities Waste Entering Landfill Unprocessed Waste Residual Waste from IWPS and 3RWPS Open Dumping Waste in Open Dumping Number of 3RWPS Facilities 3RWPS Rate Total Waste at Landfill The figure above shows a causal diagram depicting the relationship between variables in the population system and waste management in Sleman Regency. Population size is influenced by births, deaths, immigration, and emigration. Births and immigration have a positive effect on population size, while deaths and emigration have a negative effect. After designing a conceptual model using CLD, the existing system model was formulated using SFD. b. Exiting Simulation Results Based on the results of running the existing model, the results which can be seen in the image below. Figure 10 Simulation Results of the Existing Waste Management Model in Sleman Regency The simulation results in the figure show the capacity of the landfill and landfill open dumping in 2034 is > 10,000 tons. The maximum waste capacity that can be accommodated by the Piyungan Landfill is 10,000 tons from Sleman Regency (After the regional waste decentralization policy was established). The population increases according to population growth data along with the increase in waste every year based on the results of existing simulations. 3RWPS is only able to handle 26,851 tons of waste in 2034, while IWPS can process 17,648 tons of waste in 2034, the remaining waste is processed by 1,144 unit waste banks spread throughout Sleman and 13,210 neighborhood unit/community unit scale composting throughout Sleman, not used in this research process. Anticipation needs to be done to prevent the full capacity of the Piyungan Landfill in 2034. c. Verification and Validation of Existing Models Model verification is carried out by identifying whether when running the model there are any discrepancies that cause errors in the coding or model formula. Figure 10 Simulation Results of the Existing Waste Management Model in Sleman Regency Validation Total population Amount of Waste 3RWPS IWPS Landfill Total Year 2024 Manual (Xm) 1,318,086.00 219,653.64 21,822.57 13,359.00 91,198.00 1,664,119.21 Existing Simulation (Xs) 1,318,086.00 219,654.00 20,791.00 13,361.54 91,198.00 1,663,090.54 Error (Xm-Xs) 0 −0.36 1,031.57 -2.54 0 1,028.67 Absolute error (|Xm – Xs|) 0 0.36 1,031.57 2.54 0 1,028.67 Square of Error (Xm – Xs)² 0 0.1296 1,064,138.67 6.45 0 1,058,187.07 Relative error (|Xm – Xs| / Xm) 0 0.00000164 0.0473 0.00019 0 0.000618 Total (Xm) = 3,328,238.4 Total Error (Xm-Xs) = 2,057.34 Total (Xs) = 3,326,181.0 Total Sq Err (Xm – Xs)² = 2,122,332.3 Total (|Xm – Xs|) / absolute error = 2,063.14 Total Rl Er (|Xm – Xs|/Xm)= 0.04810 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5943 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 MAD (Mean Absolute Deviation) MAD calculates the average of the absolute differences between the predicted value and the actual value. =1 n ∑ |Xm−Xs| n t=1 = 1 6(2,057.34) = 342.89 (MAD) MSE (Mean Squared Error) MSE calculates the average of the squared differences between the predicted and actual values. = 1 n∑(Xm−Xs)2n t=1 = 1 6(2,122,332.3) = 353,722.05 (MSE) MAPE (Mean Absolute Percentage Error) MAPE calculates the average of the percentage difference between the predicted value and the actual value. = 1 n(∑|Xm−XS| Xm n t=1 )x100=1 6 (0,04810)x100= 0.0080 (MAPE) Existing system validation test where Xm is the average manual calculation data of the Number of Population, Number of Waste, 3RWPS, IWPS and Number of Waste in 2024 of 554,706.40, Xs is the capacity data of the simulation results of the average Number of Population, Number of Waste, 3RWPS, IWPS and Number of Waste in 2024 of 554,363.51. MAD (Mean Absolute Deviation) is the Average Absolute Deviation, based on the calculation results obtained the MAD value is 342.89 and the MSE (Mean Square Error) value is the average Square Error of the calculation results obtained is 353,722.05. While MAPE (Mean Absolute Percentage Error) is the Average Absolute Percentage Error, based on the calculation obtained the value is 0.80%, this value is below the limit of the validity provisions of the MAPE calculation. Where the MAPE value < 10% indicates very good forecasting model capability (Maricar2019). D. Waste Processing Scenario in Sleman Regency There are four alternative scenarios for waste management in the midstream and downstream areas to anticipate the Piyungan Landfill being filled by waste from Sleman Regency. The following are alternative scenarios for waste management in Sleman Regency.as follows: a. Scenario 1 : Addition of Organic Waste Processing Center (OPC) This scenario focuses on reducing the accumulation of unprocessed waste at the 3RWPS and IWPS by adding adaptive Organic Processing Center (OPC) units downstream based on need. This means that as population growth increases, so does waste generation. Causal Loop Diagram (CLD) Scenario 1 POO Management and Stock Flow Diagram (SFD) Model Scenario 1 The causal interaction in scenario 1 is described as follows: Figure 11 Casual Loop Diagram (CLD) & Stock Flow Diagram (SFD) Model Scenario 1 Waste Management in Sleman Regency Overall, the CLD and SFD scenarios for Scenario 1 demonstrate a combination of a positive (reinforcing) loop that increases waste generation as population and waste flows to landfills grow, and a negative (balancing) loop that arises from the limited capacity of the 3RWPS and IWPS. If capacity is not increased, the reinforcing loop will dominate, increasing the burden on landfills. The SFD model was developed from specific relationships between components in the CLD. The SFD model was built based on assumptions, limitations, and current data and conditions of waste management in Sleman Regency. After 24 25 26 27 28 29 30 31 32 33 500.000 1.000.000 1.500.000 Popula tion (I ndivid u) To ta l W a s te (T o n) 3R W P S (To n ) IW PS (Ton) La ndfill (Ton) Non-commercial us e only! Time Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Landfill Open Dumping (Ton) 01 Jan 2024 01 Jan 2025 01 Jan 2026 01 Jan 2027 01 Jan 2028 01 Jan 2029 01 Jan 2030 01 Jan 2031 01 Jan 2032 01 Jan 2033 01 Jan 2034 1.318.086,00 1.346.293,00 1.375.104,00 1.404.531,00 1.434.588,00 1.465.288,00 1.496.645,00 1.528.674,00 1.561.388,00 1.594.801,00 1.628.929,00 219.654,00 226.133,55 231.025,28 235.970,83 241.020,61 246.178,46 251.446,65 256.827,58 262.323,82 267.937,62 273.671,37 20.791,00 21.608,05 22.354,08 22.986,17 23.560,09 24.108,00 24.647,22 25.187,10 25.732,72 26.286,94 26.851,37 13.359,00 14.361,54 14.865,81 15.213,04 15.542,54 15.875,68 16.215,49 16.562,51 16.916,94 17.278,97 17.648,75 9.198,00 9.892,40 10.595,02 11.291,43 11.973,30 12.638,71 13.287,24 13.918,94 14.534,06 15.132,93 15.715,92 9.198,00 9.442,67 9.705,80 9.987,63 10.287,98 10.606,47 10.942,66 11.296,10 11.666,35 12.052,95 12.455,49 Non-commercial us e only! Population Total Waste Immigration Birth Emigration Death Immigration Rate Birth Rate Death Rate Emigration Rate Number of Individuals Waste Input Average Waste per Individual 3RWPS Waste Processed at 3RWPS Processed Waste at 3RWPS IWPS Waste Processed at IWPS Processed Waste at IWPS 3RWPS Level IWPS Level Waste Output Average Waste Generation Annual Decrease Fraction Waste per Year Average Waste Received at 3RWPS Average Waste Processed at 3RWPS Average Waste Received at IWPS Average Waste Processed at IWPS Average Waste Received at RDF Average Waste Processed at RDF Landfill Waste Received at Landfill 3RWPS Efficiency Efficiency IWPS RDF Average Waste Received at Landfill Waste Reduction Percentage 3RWPS Waste Management Performance Ratio Efficiency IWPS IWPS Waste Management Performance Ratio IWPS Rate Number of IWPS Facilities Waste Entering Landfill Unprocessed Waste Residual Waste from IWPS and 3RWPS Landfill Open Dumping Waste in Landfill Open Dumping Organic Processing Center Residual Waste Efficiency Number of 3RWPS Facilities 3RWPS Rate International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5944 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 compiling and formulating the model, the model was run to examine any discrepancies that could cause errors in the coding or model formula. Figure 12 Simulation results for Scenario 1 The simulation results show that between 2024 and 2034, the population of Sleman will increase from 1.3 million to 1.6 million, resulting in an increase in waste generation from 219,000 tons to 273,000 tons. In the Organic Processing Center (OPC) scenario, most organic waste is processed at the end after processing at the 3RWPS and IWPS, thus reducing the waste load at the landfill and OD, from 20,000 to 26,000 tons and 13,000 to 17,000 tons, respectively. b. Scenario 2 : Addition of Organic Waste Processing Center (OPC) & plastic shredding 3RWPS Scenario 2 was developed with efforts to develop plastic waste management. The 3RWPS in scenario 2 was designed to handle inorganic waste, specifically shredded plastic. Figure 13 Casual Loop Diagram (CLD) & Stock Flow Diagram (SFD) Model Scenario 2 Waste Management in Sleman Regency The presence of plastic shredding and the addition of inorganic 3RWPS units and OPC function as interventions that can reduce the residual load to IWPS and landfills, thereby slowing the rate of waste accumulation. The SFD model was developed from specific relationships between components in the CLD. The SFD model is built based on assumptions, limitations, and current data and conditions of waste management in Sleman Regency. The image above shows a waste processing system design that implements alternative plastic shredding and OPC strategies. After compiling and formulating the model, the model was run to examine whether there were any inconsistencies that caused errors in the coding or model formula. After ensuring there were no errors, the model was run. The following are the results of running the simulation model for scenario 2, which can be seen in the image below. 24 25 26 27 28 29 30 31 32 33 500.000 1.000.000 1.500.000 Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Non-commercial use only! Time Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Landfill Open Dumping (Ton) 01 Jan 2024 01 Jan 2025 01 Jan 2026 01 Jan 2027 01 Jan 2028 01 Jan 2029 01 Jan 2030 01 Jan 2031 01 Jan 2032 01 Jan 2033 01 Jan 2034 1.318.086,00 1.346.293,00 1.375.104,00 1.404.531,00 1.434.588,00 1.465.288,00 1.496.645,00 1.528.674,00 1.561.388,00 1.594.801,00 1.628.929,00 219.654,00 226.133,55 231.025,28 235.970,83 241.020,61 246.178,46 251.446,65 256.827,58 262.323,82 267.937,62 273.671,37 20.791,00 21.608,05 22.354,08 22.986,17 23.560,09 24.108,00 24.647,22 25.187,10 25.732,72 26.286,94 26.851,37 13.359,00 14.361,54 14.865,81 15.213,04 15.542,54 15.875,68 16.215,49 16.562,51 16.916,94 17.278,97 17.648,75 9.198,00 9.892,40 10.595,02 11.291,43 11.973,30 12.638,71 13.287,24 13.918,94 14.534,06 15.132,93 15.715,92 9.198,00 9.442,67 9.705,80 9.987,63 10.287,98 10.606,47 10.942,66 11.296,10 11.666,35 12.052,95 12.455,49 Non-commercial use only! Population Total Waste Immigration Birth Emigration Death Immigration Rate Birth Rate Death Rate Emigration Rate Number of Individuals Waste Input Average Waste per Individual 3RWPS Waste Processed at 3RWPS Processed Waste at 3RWPS IWPS Waste Processed at IWPS Processed Waste at IWPS 3RWPS Level IWPS Level Waste Output Average Waste Generation Annual Decrease Fraction Waste per Year Average Waste Received at 3RWPS Average Waste Processed at 3RWPS Average Waste Received at IWPS Average Waste Processed at IWPS Average Waste Received at RDF Average Waste Processed at RDF Landfill Waste Received at Landfill 3RWPS Efficiency Efficiency IWPS RDF Average Waste Received at Landfill Waste Reduction Percentage 3RWPS Waste Management Performance Ratio Efficiency IWPS IWPS Waste Management Performance Ratio IWPS Rate Number of IWPS Facilities Waste Entering Landfill Unprocessed Waste Residual Waste from IWPS and 3RWPS Landfill Open Dumping Waste in Landfill Open Dumping Organic Processing Center Residual Waste Efficiency Number of 3RWPS Facilities 3RWPS Rate 24 25 26 27 28 29 30 31 32 33 500.000 1.000.000 1.500.000 Popu la tion (In dividu) Tota l W a s te (T o n) 3RWPS (To n) I W PS (Ton) Land fill (Ton) Non-commercial use only! Time Population (Individu) Total Waste (Ton) 3RWPS (Ton) IWPS (Ton) Landfill (Ton) Landfill Open Dumping (Ton) 01 Jan 2024 01 Jan 2025 01 Jan 2026 01 Jan 2027 01 Jan 2028 01 Jan 2029 01 Jan 2030 01 Jan 2031 01 Jan 2032 01 Jan 2033 01 Jan 2034 1.318.086,00 1.346.293,00 1.375.104,00 1.404.531,00 1.434.588,00 1.465.288,00 1.496.645,00 1.528.674,00 1.561.388,00 1.594.801,00 1.628.929,00 219.654,00 226.133,55 231.025,28 235.970,83 241.020,61 246.178,46 251.446,65 256.827,58 262.323,82 267.937,62 273.671,37 20.791,00 24.314,32 26.582,89 28.086,91 29.186,60 30.077,01 30.862,36 31.598,36 32.314,84 33.027,85 33.746,10 13.359,00 14.361,54 14.865,81 15.213,04 15.542,54 15.875,68 16.215,49 16.562,51 16.916,94 17.278,97 17.648,75 9.198,00 9.402,20 9.610,92 9.824,29 10.042,39 10.265,33 10.493,22 10.726,17 10.964,29 11.207,69 11.456,51 9.198,00 9.170,64 9.143,08 9.115,19 9.086,97 9.058,38 9.029,44 9.000,11 8.970,39 8.940,27 8.909,73 Non-commercial use only! Population Total Waste Immigration Birth Emigration Death Immigration Rate Birth Rate Death Rate Emigration Rate Number of Individuals Waste Input Average Waste per Individual 3RWPS Waste Processed at 3RWPS Processed Waste at 3RWPS IWPS Waste Processed at IWPS Processed Waste at IWPS 3RWPS Level IWPS Level Waste Output Average Waste Generation Annual Decrease Fraction Waste per Year Average Waste Received at 3RWPS Average Waste Processed at 3RWPS Average Waste Received at IWPS Average Waste Processed at IWPS Average Waste Received at RDF Average Waste Processed at RDF Landfill Waste Received at Landfill 3RWPS Efficiency Efficiency IWPS RDF Average Waste Received at Landfill Waste Reduction Percentage 3RWPS Waste Management Performance Ratio Efficiency IWPS IWPS Waste Management Performance Ratio IWPS Rate Number of IWPS Facilities Waste Entering Landfill Open Dumping Unprocessed Waste Residual Waste from IWPS and 3RWPS Landfill Open Dumping Waste in Landfill Open Dumping Organic Processing Center Residual Waste Efficiency Number of 3RWPS Facilities 3RWPS Rate Inorganic Waste Efficiency Plastic Waste Plastic Shredding Number of Added Plastic Shredding 3RWPS Facilities Number of Plastic Shredding 3RWPS Facilities Inorganic Waste Management Performance Ratio International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5951 *Corresponding Author: Dwinaz Fadzil Muhammad Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 5936-5953 3. 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Tirkolaee, EB, Mahdavi, I., Esfahani, MMS, Weber, GW 2020. Strong green site allocation inventory problem to design urban waste management systems under uncertainty. Waste Handling, Vol. 102, pp. 340-350. Cite this Article: Muhammad, D.F., Kusrini, E. (2025). Strategy Design Dynamic System-Based Circular Supply Chain Management on Waste Management in Sleman Regency, Yogyakarta. International Journal of Current Science Research and Review, 8(12), pp. 5936-5953. DOI: https://doi.org/10.47191/ijcsrr/V8-i12-08