OPTIMISASI JARINGAN RANTAI PASOK HILIR LIQUEFIED PETROLEUM GAS (LPG) NASIONAL PT X MENGGUNAKAN PEMROGRAMAN LINIER

Liquefied petroleum gas (LPG) is a widely used gas fuel product in Indonesia. According to data from Badan Pusat Statistik (BPS), 87.12% of households in Indonesia used LPG for cooking purposes in 2022. In the same year, LPG consumption in Indonesia reached 8.2 MT. A state-owned company, hereinaf...

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Bibliographic Details
Main Author: Khansa Nailah, Kania
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/84094
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Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:Liquefied petroleum gas (LPG) is a widely used gas fuel product in Indonesia. According to data from Badan Pusat Statistik (BPS), 87.12% of households in Indonesia used LPG for cooking purposes in 2022. In the same year, LPG consumption in Indonesia reached 8.2 MT. A state-owned company, hereinafter referred to as PT X, dominates the downstream natural gas business activities in Indonesia with a market share of 97%. The government, through the Ministry of Energy and Mineral Resources (ESDM), regulates the benchmark price of 3 kg LPG through Keputusan Menteri ESDM No. 253.K/12/MEM/2020. The document specifies the standard price for freight costs at US$50.11/MT. Currently, the transportation cost of LPG managed by PT X significantly exceeds the standard, amounting to US$105.76/MT in 2022. The cost difference resulted in a loss for PT X because it exceeded the amount of subsidy that could be paid by the government. This discrepancy is due to suboptimal existing supply chain networks, which involve LPG supply using tanker ships and distribution using tanker trucks. This study aims to minimize LPG supply chain costs by optimizing the supply and distribution network configuration of PT X. Linear programming is utilized to determine the optimal volume of supply and distribution in PT X's national LPG supply chain network. The focus of decision-making in this research lies at the tactical level. Based on the optimal model solution, a 2.45% cost saving in the supply chain is achieved compared to the existing supply chain costs.