Volume 35, Issue 3 (IJIEPR 2024)                   IJIEPR 2024, 35(3): 109-121 | Back to browse issues page


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Maflahah I, Wirvikananda W, Fansuri H, Asfan D F, Faridz R. Revenue Farmers Assessment using System Dynamics Approach: Seablite (Suaeda Maritima) Salt Production Case Study. IJIEPR 2024; 35 (3) :109-121
URL: http://ijiepr.iust.ac.ir/article-1-2070-en.html
1- Department of Agroindustrial Technology, Faculty of Agriculture, Universitas Trunojoyo Madura, Indonesia , iffanmaflahah@gmail.com
2- Department of Agroindustrial Technology, Faculty of Agriculture, Universitas Trunojoyo Madura, Indonesia
Abstract:   (752 Views)
Seablite salt (Suaeda maritima) was a unique product currently under development. Seablite is a low-sodium salt essential for modern society, particularly for those who prioritize their health. This investigation aims to employ a dynamic system approach to evaluate the revenue and profit generated by the salt production system. The dynamic systems approach steps: the construction of the causal loop diagram, the development of the stock-and-flow model, the parameterization of the model, the simulation to analyze the system's behavior under various conditions, the verification and validation, the development of policy recommendations, and the conclusion with a summary of the core findings. The model was developed using four submodels: (1) demand, (2) supply, (3) production cost, and (4) revenue. The moderate scenario demonstrates that the salt flow requirements can be satisfied by utilizing the dynamic system to protect the revenue and production costs. It was consistent with the escalating production expenses. According to the optimistic scenario, the salt demand can be satisfied until 2026. The company's revenue is insufficient to cover production costs due to the rise in raw material prices. Farmers begin to reap the rewards in this scenario. It's because the overall revenue exceeds the production costs.
Full-Text [PDF 486 kb]   (138 Downloads)    
Type of Study: Research | Subject: Simulation & Stochastic Models
Received: 2024/07/7 | Accepted: 2024/07/20 | Published: 2024/09/18

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