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All Journal Jurnal Indonesia Sosial Teknologi Journal Research of Social Science, Economics, and Management
Iwan Vanany
Institut Teknologi Sepuluh Nopember, Indonesia
Computer Science & IT Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Analysis of Supply Chain Risk Management in The Lubricant Industry Using the House of Risk (HOR) Method Muthia Putri Hidayati; Iwan Vanany
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 12 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i12.8821

Abstract

PT.X is a company engaged in lubricant manufacturing. Based on the results of field studies, several risks hinder the company's supply chain process, and PT.X has never conducted a risk assessment. Therefore, risk analysis and mitigation are needed to minimize the risks that arise in the company's supply chain. This research uses the Supply Chain Operation Reference (SCOR) model to map the company's supply chain activities and the House of Risk (HOR) method to identify risks in the supply chain, including risk events, risk agents, and their correlations. The results reveal a total of 32 risk events and 26 risk agents. Then, a Pareto diagram calculation is conducted, identifying 3 priority risk agents that need to be addressed: supplier inability to provide raw materials, lack of work supervision, and inadequate workforce competence. From these 3 priority risk agents, 8 risk mitigation measures have been determined: evaluating supplier performance, establishing contracts with suppliers, planning alternative supplier selection, conducting periodic supervision, establishing company standard operating procedures, providing skills training and work discipline, recruiting workers more selectively and rigorously, and placing workers according to their expertise.
Development of Inventory Policy Using Demand Driven Material Requirements Planning (Ddmrp) and Probabilistic Inventory Model: A Case Study on the Water Tank Industry in Indonesia Ghoffar Albab Maarif; Iwan Vanany
Journal Research of Social Science, Economics, and Management Vol. 5 No. 7 (2026): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i7.1343

Abstract

PT. X, a water tank manufacturer in Indonesia, has long faced inventory management challenges due to the lack of a structured policy. The current MRP system relies heavily on subjective employee judgment, leading to a reactive and imbalanced inventory system with frequent stockouts or overstock. This study aims to develop a more structured inventory policy that is examined using Monte Carlo simulations in order to achieve more effective and efficient control. Materials are first classified into two groups, namely general components and special components and mixtures. These two groups receive different treatment according to their characteristics. The general components utilize a probabilistic inventory model (R, s, S) to create structured inventory parameters, which are then evaluated against the existing system. Special and mixed components improve the existing MRP system to DDMRP (Demand Driven Material Requirements Planning) with buffer levels across different materials. This adjustment is necessary because the behavior of the MRP system causes system instability, as it is highly dependent on the estimated demand for the final product. Overall, the selected inventory policy resulted in total cost savings of IDR 494,464,820 per week, equivalent to IDR 25,712,170,632 per year. This study proves that pull-based inventory system adjustments, namely DDMRP for special and mixed components and probabilistic inventory models (R, s, S) for general components, are effective in minimizing overstock, stabilizing demand variability, and significantly improving service levels.
Co-Authors Ghoffar Albab Maarif Muthia Putri Hidayati