Pemodelan Sistem Persediaan Menggunakan Pendekatan Sistem Dinamik
Keywords:
Peramalan, Persediaan, Sistem Dinamik, Moving Average
Abstract
Kondisi bisnis yang tidak menentu dapat berdampak pada permasalahan terkait dengan permintaan dan persediaan. Tingkat persediaan yang kurang baik berdampak pada permasalahan kekurangan maupun persediaan. Penelitian ini bertujuan mensimulasikan tingkat persediaan suatu distributor berdasarkan estimasi tingkat permintaan yang terjadi. Penelitian ini menggunakan model simulasi program dinamik dengan bantuan tools Powersim 10. Hasil pemodelan dinyatakan valid apabila titik validasi AME kurang dari 30% dan dinyatakan menyerupai bila titik validasi AME kurang dari 10% Hasil simulasi menunjukkan pemodelan pada stok gudang mendapatkan nilai validasi 3,37 dan tingkat permintaan sebesar 2,02 yang yang menyatakan model data dikatakan valid. Hasil pengujian menunjukkan hasil simulasi memberikan sistem prediksi lebih baik dibandingkan dengan menggunakan metode moving average.
Downloads
Download data is not yet available.
References
[1] Y. Tliche, A. Taghipour, and B. Canel-Depitre, ‘An improved forecasting approach to reduce inventory levels in decentralized supply chains’, Eur. J. Oper. Res., vol. 287, no. 2, pp. 511–527, Dec. 2020, doi: https://doi.org/10.1016/j.ejor.2020.04.044.
[2] R. Qiu, Y. Sun, and M. Sun, ‘A distributionally robust optimization approach for multi-product inventory decisions with budget constraint and demand and yield uncertainties’, Comput. Oper. Res., vol. 126, p. 105081, Feb. 2021, doi: https://doi.org/10.1016/j.cor.2020.105081.
[3] C. Shi, W. Chen, and I. Duenyas, ‘Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand’, Oper. Res., vol. 64, no. 2, pp. 362–370, Feb. 2016, doi: https://doi.org/10.1287/opre.2015.1474.
[4] N. R. Sanders and K. B. Manrodt, ‘Forecasting Software in Practice: Use, Satisfaction, and Performance’, Interfaces (Providence)., vol. 33, no. 5, pp. 90–93, Oct. 2003, doi: https://doi.org/10.1287/inte.33.5.90.19251.
[5] M. M. Ali and J. E. Boylan, ‘On the effect of non-optimal forecasting methods on supply chain downstream demand’, IMA J. Manag. Math., vol. 23, no. 1, pp. 81–98, Jan. 2012, doi: https://doi.org/10.1093/imaman/dpr005.
[6] F. R. Johnston, J. E. Boyland, M. Meadows, and E. Shale, ‘Some properties of a simple moving average when applied to forecasting a time series’, J. Oper. Res. Soc., vol. 50, no. 12, pp. 1267–1271, Dec. 1999, doi: https://doi.org/10.1057/palgrave.jors.2600823.
[7] E. León-Castro, E. Avilés-Ochoa, J. M. Merigó, and A. M. Gil-Lafuente, ‘Heavy Moving Averages and Their Application in Econometric Forecasting’, Cybern. Syst., 2018, doi: https://doi.org/10.1080/01969722.2017.1412883.
[8] S. H. Sakdiyah, N. Eltivia, N. I. Riwajanti, and K. Ekasari, ‘Forecasting Analysis on the Impact of Pandemic Towards Cigarette Sales’, 2021, doi: https://doi.org/10.2991/aebmr.k.210717.053.
[9] R. Hanif, S. Mustafa, S. Iqbal, and S. Piracha, ‘A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method’, J. Comput. Cogn. Eng., vol. 2, no. 2, pp. 143–149, Mar. 2022, doi: https://doi.org/10.47852/bonviewJCCE2202159.
[10] G. Zhang, X. Zhang, and H. Feng, ‘Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion’, Expert Syst., vol. 33, no. 5, pp. 501–516, Oct. 2016, doi: https://doi.org/10.1111/exsy.12164.
[11] I. J. Martinez-Moyano and G. P. Richardson, ‘Best practices in system dynamics modeling’, Syst. Dyn. Rev., vol. 29, no. 2, pp. 102–123, Apr. 2013, doi: https://doi.org/10.1002/sdr.1495.
[12] S. M. Fortsch, J. H. Choi, and E. A. Khapalova, ‘Competition Can Help Predict Sales’, J. Forecast., 2021, doi: https://doi.org/10.1002/for.2818.
[13] P. D. Sentia, Andriansyah, R. D. Lufika, and M. J. Satria, ‘The planning of iron sand inventory with System Dynamic Approach’, IOP Conf. Ser. Mater. Sci. Eng., vol. 931, no. 1, p. 012015, Sep. 2020, doi: https://doi.org/10.1088/1757-899X/931/1/012015.
[14] I. Belhajali and W. Hachicha, ‘System dynamics simulation to determine safety stock for a single-stage inventory system’, in 2013 International Conference on Advanced Logistics and Transport, 2013, pp. 488–493, doi: https://doi.org/10.1109/ICAdLT.2013.6568507.
[15] M. K. Simon and J. Goes, ‘Ex post facto research’, Retrieved Sept., vol. 25, pp. 1–2, 2013, [Online]. Available: http://profshankarbhusari.com/wp-content/uploads/2016/05/Ex-Post-Facto-research.pdf.
[16] M. Giuffre, ‘Designing research: Ex post facto designs’, J. PeriAnesthesia Nurs., vol. 12, no. 3, pp. 191–195, Jun. 1997, doi: https://doi.org/10.1016/S1089-9472(97)80038-X.
[17] F. Samyn, ‘The use of artificial neural networks as a sales forecasting method: A quantitative ex post facto study’, University of Phoenix, 2013, [Online]. Available: https://www.proquest.com/openview/661b56a0147c956f3badaa37c1c9c3d9/1?pq-origsite=gscholar&cbl=18750.
[18] A. Inam, J. Adamowski, J. Halbe, and S. Prasher, ‘Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan’, J. Environ. Manage., vol. 152, pp. 251–267, Apr. 2015, doi: https://doi.org/10.1016/j.jenvman.2015.01.052.
[19] R. Iannone, G. Martino, S. Miranda, and S. Riemma, ‘Modeling Fashion Retail Supply Chain through Causal Loop Diagram’, IFAC-PapersOnLine, vol. 48, no. 3, pp. 1290–1295, 2015, doi: https://doi.org/10.1016/j.ifacol.2015.06.263.
[20] H. Dianat, S. Wilkinson, P. Williams, and H. Khatibi, ‘Planning the resilient city: Investigations into using “causal loop diagram” in combination with “UNISDR scorecard” for making cities more resilient’, Int. J. Disaster Risk Reduct., vol. 65, p. 102561, 2021, doi: https://doi.org/10.1016/j.ijdrr.2021.102561.
[21] W. Rusiawan, P. Tjiptoherijanto, E. Suganda, and L. Darmajanti, ‘System Dynamics Modeling for Urban Economic Growth and CO2 Emission: A Case Study of Jakarta, Indonesia’, Procedia Environ. Sci., vol. 28, pp. 330–340, 2015, doi: https://doi.org/10.1016/j.proenv.2015.07.042.
[22] C. Musi, S. Anggoro, and S. Sunarsih, ‘System dynamic modelling and simulation for cultivation of forest land: case study Perum Perhutani, Central Java, Indonesia’, J. Ecol. Eng., vol. 18, no. 4, pp. 25–34, Jul. 2017, doi: https://doi.org/10.12911/22998993/74307.
[23] T. Asy’arie, ‘Strategi pengelolaan sampah kota melalui penerapan alternatif kebijakan lingkungan’, Universitas Indonesia, 2016, [Online]. Available: http://digilib.ui.ac.id/detail?id=20422819&lokasi=lokal.
[24] M. K. Saraji and A. M. Sharifabadi, ‘Application of System Dynamics in Forecasting: A Systematic Review.’, Int. J. Manag. Account. Econ., vol. 4, no. 12, pp. 1192–2005, 2017, [Online]. Available: https://www.ijmae.com/article_115223.html.
[25] E. Suryani, S.-Y. Chou, and C.-H. Chen, ‘Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework’, Expert Syst. Appl., vol. 37, no. 3, pp. 2324–2339, 2010, https://doi.org/doi: 10.1016/j.eswa.2009.07.041.
[26] Y. Wada, K. Hamada, and N. Hirata, ‘Shipbuilding capacity optimization using shipbuilding demand forecasting model’, J. Mar. Sci. Technol., vol. 27, no. 1, pp. 522–540, Mar. 2022, doi: https://doi.org/10.1007/s00773-021-00852-8.
[27] H. Zhang, X. Zhang, and B. Zhang, ‘System dynamics approach to urban water demand forecasting’, Trans. Tianjin Univ., vol. 15, no. 1, pp. 70–74, Feb. 2009, doi: https://doi.org/10.1007/s12209-009-0014-5.
[28] G. Li, K. K. F. Wong, H. Song, and S. F. Witt, ‘Tourism Demand Forecasting: A Time Varying Parameter Error Correction Model’, J. Travel Res., vol. 45, no. 2, pp. 175–185, Nov. 2006, doi: https://doi.org/10.1177/0047287506291596.
[29] Z. Hu, J. Ma, L. Yang, X. Li, and M. Pang, ‘Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand’, Sustainability, vol. 11, no. 5, p. 1272, Feb. 2019, doi: https://doi.org/10.3390/su11051272.
[30] A. Narayanan, F. Sahin, and E. P. Robinson, ‘Demand and order‐fulfillment planning: The impact of point‐of‐sale data, retailer orders and distribution center orders on forecast accuracy’, J. Oper. Manag., vol. 65, no. 5, pp. 468–486, Jul. 2019, doi: https://doi.org/10.1002/joom.1026.
[31] J. Wen, Z. Xian, K. Chen, and W. Luo, ‘A novel forward operator-based Bayesian recurrent neural network-based short-term net load demand forecasting considering demand-side renewable energy’, Front. Energy Res., vol. 10, Sep. 2022, doi: https://doi.org/10.3389/fenrg.2022.963657.
[2] R. Qiu, Y. Sun, and M. Sun, ‘A distributionally robust optimization approach for multi-product inventory decisions with budget constraint and demand and yield uncertainties’, Comput. Oper. Res., vol. 126, p. 105081, Feb. 2021, doi: https://doi.org/10.1016/j.cor.2020.105081.
[3] C. Shi, W. Chen, and I. Duenyas, ‘Technical Note—Nonparametric Data-Driven Algorithms for Multiproduct Inventory Systems with Censored Demand’, Oper. Res., vol. 64, no. 2, pp. 362–370, Feb. 2016, doi: https://doi.org/10.1287/opre.2015.1474.
[4] N. R. Sanders and K. B. Manrodt, ‘Forecasting Software in Practice: Use, Satisfaction, and Performance’, Interfaces (Providence)., vol. 33, no. 5, pp. 90–93, Oct. 2003, doi: https://doi.org/10.1287/inte.33.5.90.19251.
[5] M. M. Ali and J. E. Boylan, ‘On the effect of non-optimal forecasting methods on supply chain downstream demand’, IMA J. Manag. Math., vol. 23, no. 1, pp. 81–98, Jan. 2012, doi: https://doi.org/10.1093/imaman/dpr005.
[6] F. R. Johnston, J. E. Boyland, M. Meadows, and E. Shale, ‘Some properties of a simple moving average when applied to forecasting a time series’, J. Oper. Res. Soc., vol. 50, no. 12, pp. 1267–1271, Dec. 1999, doi: https://doi.org/10.1057/palgrave.jors.2600823.
[7] E. León-Castro, E. Avilés-Ochoa, J. M. Merigó, and A. M. Gil-Lafuente, ‘Heavy Moving Averages and Their Application in Econometric Forecasting’, Cybern. Syst., 2018, doi: https://doi.org/10.1080/01969722.2017.1412883.
[8] S. H. Sakdiyah, N. Eltivia, N. I. Riwajanti, and K. Ekasari, ‘Forecasting Analysis on the Impact of Pandemic Towards Cigarette Sales’, 2021, doi: https://doi.org/10.2991/aebmr.k.210717.053.
[9] R. Hanif, S. Mustafa, S. Iqbal, and S. Piracha, ‘A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method’, J. Comput. Cogn. Eng., vol. 2, no. 2, pp. 143–149, Mar. 2022, doi: https://doi.org/10.47852/bonviewJCCE2202159.
[10] G. Zhang, X. Zhang, and H. Feng, ‘Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion’, Expert Syst., vol. 33, no. 5, pp. 501–516, Oct. 2016, doi: https://doi.org/10.1111/exsy.12164.
[11] I. J. Martinez-Moyano and G. P. Richardson, ‘Best practices in system dynamics modeling’, Syst. Dyn. Rev., vol. 29, no. 2, pp. 102–123, Apr. 2013, doi: https://doi.org/10.1002/sdr.1495.
[12] S. M. Fortsch, J. H. Choi, and E. A. Khapalova, ‘Competition Can Help Predict Sales’, J. Forecast., 2021, doi: https://doi.org/10.1002/for.2818.
[13] P. D. Sentia, Andriansyah, R. D. Lufika, and M. J. Satria, ‘The planning of iron sand inventory with System Dynamic Approach’, IOP Conf. Ser. Mater. Sci. Eng., vol. 931, no. 1, p. 012015, Sep. 2020, doi: https://doi.org/10.1088/1757-899X/931/1/012015.
[14] I. Belhajali and W. Hachicha, ‘System dynamics simulation to determine safety stock for a single-stage inventory system’, in 2013 International Conference on Advanced Logistics and Transport, 2013, pp. 488–493, doi: https://doi.org/10.1109/ICAdLT.2013.6568507.
[15] M. K. Simon and J. Goes, ‘Ex post facto research’, Retrieved Sept., vol. 25, pp. 1–2, 2013, [Online]. Available: http://profshankarbhusari.com/wp-content/uploads/2016/05/Ex-Post-Facto-research.pdf.
[16] M. Giuffre, ‘Designing research: Ex post facto designs’, J. PeriAnesthesia Nurs., vol. 12, no. 3, pp. 191–195, Jun. 1997, doi: https://doi.org/10.1016/S1089-9472(97)80038-X.
[17] F. Samyn, ‘The use of artificial neural networks as a sales forecasting method: A quantitative ex post facto study’, University of Phoenix, 2013, [Online]. Available: https://www.proquest.com/openview/661b56a0147c956f3badaa37c1c9c3d9/1?pq-origsite=gscholar&cbl=18750.
[18] A. Inam, J. Adamowski, J. Halbe, and S. Prasher, ‘Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan’, J. Environ. Manage., vol. 152, pp. 251–267, Apr. 2015, doi: https://doi.org/10.1016/j.jenvman.2015.01.052.
[19] R. Iannone, G. Martino, S. Miranda, and S. Riemma, ‘Modeling Fashion Retail Supply Chain through Causal Loop Diagram’, IFAC-PapersOnLine, vol. 48, no. 3, pp. 1290–1295, 2015, doi: https://doi.org/10.1016/j.ifacol.2015.06.263.
[20] H. Dianat, S. Wilkinson, P. Williams, and H. Khatibi, ‘Planning the resilient city: Investigations into using “causal loop diagram” in combination with “UNISDR scorecard” for making cities more resilient’, Int. J. Disaster Risk Reduct., vol. 65, p. 102561, 2021, doi: https://doi.org/10.1016/j.ijdrr.2021.102561.
[21] W. Rusiawan, P. Tjiptoherijanto, E. Suganda, and L. Darmajanti, ‘System Dynamics Modeling for Urban Economic Growth and CO2 Emission: A Case Study of Jakarta, Indonesia’, Procedia Environ. Sci., vol. 28, pp. 330–340, 2015, doi: https://doi.org/10.1016/j.proenv.2015.07.042.
[22] C. Musi, S. Anggoro, and S. Sunarsih, ‘System dynamic modelling and simulation for cultivation of forest land: case study Perum Perhutani, Central Java, Indonesia’, J. Ecol. Eng., vol. 18, no. 4, pp. 25–34, Jul. 2017, doi: https://doi.org/10.12911/22998993/74307.
[23] T. Asy’arie, ‘Strategi pengelolaan sampah kota melalui penerapan alternatif kebijakan lingkungan’, Universitas Indonesia, 2016, [Online]. Available: http://digilib.ui.ac.id/detail?id=20422819&lokasi=lokal.
[24] M. K. Saraji and A. M. Sharifabadi, ‘Application of System Dynamics in Forecasting: A Systematic Review.’, Int. J. Manag. Account. Econ., vol. 4, no. 12, pp. 1192–2005, 2017, [Online]. Available: https://www.ijmae.com/article_115223.html.
[25] E. Suryani, S.-Y. Chou, and C.-H. Chen, ‘Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework’, Expert Syst. Appl., vol. 37, no. 3, pp. 2324–2339, 2010, https://doi.org/doi: 10.1016/j.eswa.2009.07.041.
[26] Y. Wada, K. Hamada, and N. Hirata, ‘Shipbuilding capacity optimization using shipbuilding demand forecasting model’, J. Mar. Sci. Technol., vol. 27, no. 1, pp. 522–540, Mar. 2022, doi: https://doi.org/10.1007/s00773-021-00852-8.
[27] H. Zhang, X. Zhang, and B. Zhang, ‘System dynamics approach to urban water demand forecasting’, Trans. Tianjin Univ., vol. 15, no. 1, pp. 70–74, Feb. 2009, doi: https://doi.org/10.1007/s12209-009-0014-5.
[28] G. Li, K. K. F. Wong, H. Song, and S. F. Witt, ‘Tourism Demand Forecasting: A Time Varying Parameter Error Correction Model’, J. Travel Res., vol. 45, no. 2, pp. 175–185, Nov. 2006, doi: https://doi.org/10.1177/0047287506291596.
[29] Z. Hu, J. Ma, L. Yang, X. Li, and M. Pang, ‘Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand’, Sustainability, vol. 11, no. 5, p. 1272, Feb. 2019, doi: https://doi.org/10.3390/su11051272.
[30] A. Narayanan, F. Sahin, and E. P. Robinson, ‘Demand and order‐fulfillment planning: The impact of point‐of‐sale data, retailer orders and distribution center orders on forecast accuracy’, J. Oper. Manag., vol. 65, no. 5, pp. 468–486, Jul. 2019, doi: https://doi.org/10.1002/joom.1026.
[31] J. Wen, Z. Xian, K. Chen, and W. Luo, ‘A novel forward operator-based Bayesian recurrent neural network-based short-term net load demand forecasting considering demand-side renewable energy’, Front. Energy Res., vol. 10, Sep. 2022, doi: https://doi.org/10.3389/fenrg.2022.963657.
Published
2023-09-16
How to Cite
Zulfikar, T. E., Supriyadi, S., Rosihin, R., & Nalhadi, A. (2023, September 16). Pemodelan Sistem Persediaan Menggunakan Pendekatan Sistem Dinamik. JiTEKH, 11(2), 62-69. https://doi.org/https://doi.org/10.35447/jitekh.v11i2.783
Section
JITEKH
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
.png)
