Pengelolaan Risiko Rantai Pasok Menggunakan Metode House Of Risk (HOR) Dengan Prediksi Occurrence Berbasis Machine Learning

Azizah, Badriatul Nur (2026) Pengelolaan Risiko Rantai Pasok Menggunakan Metode House Of Risk (HOR) Dengan Prediksi Occurrence Berbasis Machine Learning. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pengelolaan risiko rantai pasok menjadi aspek krusial dalam menjaga kelancaran operasional perusahaan manufaktur, khususnya pada industri karung plastik yang bergantung pada bahan baku impor. PT Rajawali Tanjungsari Enjiniring menghadapi berbagai risiko pada proses perencanaan, pengadaan, produksi dan distribusi, baik dari faktor eksternal seperti fluktuasi nilai tukar dan harga impor, maupun faktor internal seperti keterbatasan modal kerja, keterlambatan bahan baku, dan gangguan operasional. Penelitian ini bertujuan untuk mengidentifikasi risiko dan penyebabnya serta menentukan prioritas mitigasi yang efektif dengan menggunakan metode House of risk (HoR). Integrasi machine learning digunakan dalam penentuan nilai occurrence untuk menghasilkan estimasi probabilitas risiko yang lebih objektif, berbasis data historis, dan adaptif terhadap perubahan kondisi. Hasil penelitian diharapkan mampu menghasilkan risk profile yang komprehensif, mengidentifikasi risk agent prioritas, serta memberikan rekomendasi tindakan mitigasi optimal sehingga perusahaan dapat meningkatkan efisiensi biaya dan ketahanan rantai pasok.
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Supply chain risk management has become a crucial aspect of maintaining smooth operational performance in manufacturing companies, particularly in the plastic sack industry, which heavily depends on imported raw materials. PT Rajawali Tanjungsari Enjiniring faces various risks throughout its planning, procurement, production, and distribution processes, arising from both external factors, such as exchange rate fluctuations and import price volatility, and internal factors, such as limited working capital, raw material delivery delays, and operational disruptions. This study aims to identify supply chain risks and their underlying causes, as well as to determine effective mitigation priorities using the House of risk (HoR) method. The integration of machine learning is employed to estimate occurrence values, enabling more objective, data-driven risk probability assessments based on historical data and adaptive to changing conditions.The expected outcomes of this research include the development of a comprehensive risk profile, the identification of priority risk agents, and the formulation of optimal mitigation strategies. These findings are intended to help the company improve cost efficiency and enhance the resilience of its supply chain.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Supply Chain Risk Management, House of risk, Machine learning, Occurrence, Mitigasi Risiko
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management T Technology > T Technology (General) > T174.5 Technology--Risk assessment. T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T58.62 Decision support systems
Divisions:	Faculty of Industrial Technology > Industrial Engineering > 26201-(S1) Undergraduate Thesis
Depositing User:	Badriatul Nur Azizah
Date Deposited:	30 Jul 2026 03:13
Last Modified:	30 Jul 2026 03:13
URI:	http://repository.its.ac.id/id/eprint/140205

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