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October 10, 2025Journal La Multiapp0 citationsOpen Access

Optimization of Phoska Fertilizer Production Planning Using Dynamic Programming Method

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MCMuhammad Hadziqul Afkar CikoSSSumiati Sumiati

Key Points

  • The study optimizes production processes, leading to reduced costs and improved fertilizer quality.
  • Dynamic programming identifies the best combination of raw materials while addressing potential price fluctuations.
  • Historical data is utilized for accurate demand forecasting, enhancing overall production planning.
  • This strategy supports sustainable agriculture by promoting efficient resource use and soil health maintenance.

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.

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Cite This Study

Ciko et al. (2025) studied this question.

synapsesocial.com/papers/68e865117ef2f04ca37e4d31https://doi.org/10.37899/journallamultiapp.v6i5.2213
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