Optimization of Phoska Fertilizer Production Planning Using Dynamic Programming Method
Abstract
Indonesia's agricultural sector plays a crucial role in ensuring food security and bolstering the national economy. A major challenge, however, is the declining quality of land due to the unregulated use of chemical fertilizers. PT Gresik Nusantara Fertilizer addresses this issue by producing soil-enhancing fertilizers. Their main offerings include GNF Mutiara, GNF SP-36, and GNF Phoska, which aim to enhance soil health and increase agricultural yields. The company operates with a continuous production system, where raw materials represent the most significant expense due to their variety and volume. Labor costs arise from worker wages involved in tasks like mixing, granulating, and packaging. Energy costs, especially electricity, are critical for operating production machinery, particularly during the drying phase using a rotary dryer. Packaging costs entail the use of sacks for distributing the fertilizer to markets. To effectively manage these components and minimize resource waste, a strategy is needed to optimize production and maintain cost-efficiency. This study employs Dynamic Programming (DP) to enhance the use of raw materials in producing Phoska fertilizer. This method helps determine the optimal mix of raw materials by considering potential price variations, thereby promoting more efficient production. Additionally, forecasting techniques are utilized in the study to predict fertilizer demand based on historical data.
References
Adam. (2022). New student registration application using forecasting method. Journal of Informatics Engineering, 2(1).
Afandi, R. R. (2024). Analisis Peramalan Penjualan Semen Menggunakan Metode Single Moving Average Dan Single Eksponential Smoothing. JUSTI (Jurnal Sistem dan Teknik Industri), 5(2), 188-196. https://doi.org/10.30587/justicb.v5i2.9314
Alotaibi, A., & Nadeem, F. (2021). A review of applications of linear programming to optimize agricultural solutions. International Journal of Information Engineering and Electronic Business, 10(2), 11. https://doi.org/10.5815/ijieeb.2021.02.02
Azevedo, B. F., Rocha, A. M. A., & Pereira, A. I. (2024). Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning, 113(7), 4055-4097. https://doi.org/10.1007/s10994-023-06467-x
Bao, S., Sun, P., Zhu, J., Ji, Q., & Liu, J. (2022). Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain. Energy, 256, 124682. https://doi.org/10.1016/j.energy.2022.124682
Bao, S., Sun, P., Zhu, J., Ji, Q., & Liu, J. (2022). Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain. Energy, 256, 124682. https://doi.org/10.1016/j.energy.2022.124682
Beheshti, H. M., & Beheshti, C. M. (2010). Improving productivity and firm performance with enterprise resource planning. Enterprise Information Systems, 4(4), 445-472. https://doi.org/10.1080/17517575.2010.511276
Biva, A. T. (2024). A Comprehensive Study of Machine Learning Approaches for Financial Time Series Forecasting (Doctoral dissertation, University of Dhaka).
Campbell, J. L., Rustad, L. E., Porter, J. H., Taylor, J. R., Dereszynski, E. W., Shanley, J. B., ... & Boose, E. R. (2013). Quantity is nothing without quality: Automated QA/QC for streaming environmental sensor data. BioScience, 63(7), 574-585. https://doi.org/10.1525/bio.2013.63.7.10
Chaturvedi, R., Darokar, H., Patil, P. P., Kumar, M., Sangeeta, K., Aravinda, K., & Kadhim, A. A. (2023). Maximizing towards the sustainability: Integrating materials, energy, and resource efficiency in revolutionizing manufacturing industry. In E3S Web of Conferences (Vol. 453, p. 01036). EDP Sciences. https://doi.org/10.1051/e3sconf/202345301036
Costa, L. (2024). In-depth study and development of a planning and forecasting application to generate forecasting scenarios in support to annual and monthly planning activities (Doctoral dissertation, Politecnico di Torino).
Darwati, I., & Hayuningtyas, R. Y. (2023). Simple moving average and weighted moving average methods in predicting rice production. Journal of Science and Management, 11(2). https://doi.org/10.31294/evolusi.v11i2.17267
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American statistical association, 106(496), 1513-1527. https://doi.org/10.1198/jasa.2011.tm09771
Dorfman, R. (2022). Application of linear programming to the theory of the firm: including an analysis of monopolistic firms by non-linear programming. Univ of California Press.
Dwimahendrawan, A. (2021). Sosialisasi Perencanaan Dan Penjadwalan Produksi Pada Industri Rumah Tangga Keripik Kedelai Di Desa Kertonegoro, Kecamatan Jenggawah, Kabupaten Jember. Majalah Ilmiah Pelita Ilmu, 4(2), 58-75. https://doi.org/10.37849/mipi.v4i2.255
Gharibi, A., Doniavi, E., & Hasanzadeh, R. (2024). A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification. Energy Conversion and Management, 308, 118392. https://doi.org/10.1016/j.enconman.2024.118392
Hamirsa, M. H. (2022). Usulan Perencanaan Peramalan (Forecasting) dan Safety Stock Persediaan Spare Part Busi Champion Type RA7YC-2 (EV01/EW-01/2) Menggunakan Metode Time Series pada PT Triangle Motorindo Semarang. Industrial Engineering Online Journal, 11(1).
Hawkins, C. (2012). Manufacturing. The Cambridge companion to the Roman economy, 175-94.
Hayati, T., & Firdaus, H. (2024). Peningkatan Kualitas Sistem Produksi Di Perusahaan Sandal Sandria Tasikmalaya Dengan Menggunakan Assembly To Order. Jurnal Media Teknologi, 11(01).
Herlina, E., Prabowo, F. H. E., & Nuraida, D. (2021). Analysis of quality control in improving the production process. Journal of Business Management Focus, 11(2), 173. https://doi.org/10.12928/fokus.v11i2.4263
Hitomi, K. (2017). Manufacturing systems engineering: a unified approach to manufacturing technology, production management and industrial economics. Routledge.
Lotysh, V., Gumeniuk, L., & Humeniuk, P. (2023). Comparison of the effectiveness of time series analysis methods: SMA, WMA, EMA, EWMA, and Kalman filter for data analysis. Informatyka, automatyka, pomiary w gospodarce i ochronie środowiska, 13(3), 71-74. https://doi.org/10.35784/iapgos.3652
Lubis, M. H., Tanjung, A. A., & Martina, D. (2022). Forecasting for batik production with single moving average. Journal of Computer Technology and Information Systems, 2(2).
Mabude, K., Malela‐Majika, J. C., Castagliola, P., & Shongwe, S. C. (2021). Generally weighted moving average monitoring schemes: Overview and perspectives. Quality and Reliability Engineering International, 37(2), 409-432.
Marselina, E., & Rokamah, R. (2022). Production management of Galih Kurnia chips home industry, Bubakan Village, Tulakan District, Pacitan Regency. Niqosiya: Journal of Economics and Business Research, 2(1), 105–120. https://doi.org/10.21154/niqosiya.v2i1.706
Mico, A. D., Arifianto, D., & Zakiyyah, A. M. (2022). Peramalan Penjualan Batu Gamping Pada UD eko jaya menggunakan single exponential smoothing dan double exponential smoothing. Jurnal cafetaria, 3(2), 151-160.
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
Moser, H. (2013). manufacturing. Economic Development Journal, 12(1), 5.
Nakachew, K., Yigermal, H., Assefa, F., Gelaye, Y., & Ali, S. (2024). Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management. Open Agriculture, 9(1), 20220356. https://doi.org/10.1515/opag-2022-0356
Nuraeni, N., & Santoso, B. (2024). The role of raw material inventory management in production scheduling of PT XYZ. Journal of Business Management, 2(2), 379–394.
Oktavianty, V. N., & Sukmono, T. (2020). Optimization of minimum cost determination in raw material inventory control using dynamic programming method (Case study at PT. XYZ). Spektrum Industri, 18(1), 15. https://doi.org/10.12928/si.v18i1.10972
Peeters, K., & van Ooijen, H. (2020). Hybrid make-to-stock and make-to-order systems: a taxonomic review. International Journal of Production Research, 58(15), 4659-4688. https://doi.org/10.1080/00207543.2020.1778204
Perry, M. B. (2010). The weighted moving average technique. Wiley Encyclopedia of Operations Research and Management Science. https://doi.org/10.1002/9780470400531.eorms0964
Riki, & Stefanus. (2020). Inventory control with forecasting method: Moving average and exponential smoothing. Algor Journal, 2(1). https://jurnal.buddhidharma.ac.id/index.php/algor/index
Sholehah, R., Marsudi, M., & Budianto, A. G. (2021). Analysis of soybean raw material inventory using EOQ, ROP and safety stock of tofu production based on forecasting method at PT. Langgeng. Jieom Journal.
Soeltanong, M. B., & Sasongko, C. (2021). Production planning and inventory control in manufacturing companies. Journal of Accounting and Taxation Research, 8(1), 14–27.
Staiger, R. D., Schwandt, H., Puhan, M. A., & Clavien, P. A. (2019). Improving surgical outcomes through benchmarking. Journal of British Surgery, 106(1), 59-64. https://doi.org/10.1002/bjs.10976
Sunandar, H. (2020). Optimization of greedy algorithm implementation in Rupiah currency exchange function. [Nama Jurnal tidak tercantum].
Wahyono, A. T., Novianto, D. J., Nugroho, T. A., Agusti, F., Industry, J. T., Science, F., Technology, D., Duta, U., & Surakarta, B. (2024). Optimization of travel routes to Duta Bangsa University Surakarta Campus using dynamic programming method. Journal of Science Innovation and Technology (SINTECH), 4, 14–19. https://doi.org/10.47701
Xing, Y., & Wang, X. (2024). Precise application of water and fertilizer to crops: challenges and opportunities. Frontiers in Plant Science, 15, 1444560. https://doi.org/10.3389/fpls.2024.1444560
Copyright (c) 2025 Journal La Multiapp
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
