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All Journal International Journal of Industrial Engineering and Engineering Management
Diar Astanti, Ririn
Unknown Affiliation
Industrial & Manufacturing Engineering

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Business Intelligence for Decision Support System for Replenishment Policy in Mining Industry Seto, Franklin Chandra Pragnyono; Daryanto, Yosef; Diar Astanti, Ririn
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.7245

Abstract

The mining industry has unique characteristics in the sense that usually, the plant is located in a remote area while the headquarters are located in an urban area. These conditions pose challenges for the industry related to coordination within companies. This coordination is very important, especially in relation to the decision-making that must be carried out by the company. One of the important managerial decisions is related to the replenishment policy. To make replenishment decisions, companies need past data, such as biodiesel consumption rate, and current data, such as current stock and storage capacity, where the source of those data is in the plant. Often, decisions must be taken quickly because they have impacts on the continuousness of production operations at the plant. However, the remote location and shipping routes across rivers have created new challenges in the flow of goods and services supply because the shipment depends on the tides of the river. This research proposes a business intelligence system that collects, sorts, and visualizes data, then analyzes the replenishment decision to support decision-making in the mining industry. The system uses Microsoft Power BI software which is integrated with the company’s ERP system. To illustrate the applicability of the proposed system, it is applied to a coal mining company, especially in relation to the replenishment policy of biofuel. The result of this study indicates that the proposed system can work. In addition, it can reduce decision-making time by 220.65%.  
Business Intelligence for Decision Support System for Replenishment Policy in Mining Industry Seto, Franklin Chandra Pragnyono; Daryanto, Yosef; Diar Astanti, Ririn
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.7245

Abstract

The mining industry has unique characteristics in the sense that usually, the plant is located in a remote area while the headquarters are located in an urban area. These conditions pose challenges for the industry related to coordination within companies. This coordination is very important, especially in relation to the decision-making that must be carried out by the company. One of the important managerial decisions is related to the replenishment policy. To make replenishment decisions, companies need past data, such as biodiesel consumption rate, and current data, such as current stock and storage capacity, where the source of those data is in the plant. Often, decisions must be taken quickly because they have impacts on the continuousness of production operations at the plant. However, the remote location and shipping routes across rivers have created new challenges in the flow of goods and services supply because the shipment depends on the tides of the river. This research proposes a business intelligence system that collects, sorts, and visualizes data, then analyzes the replenishment decision to support decision-making in the mining industry. The system uses Microsoft Power BI software which is integrated with the company’s ERP system. To illustrate the applicability of the proposed system, it is applied to a coal mining company, especially in relation to the replenishment policy of biofuel. The result of this study indicates that the proposed system can work. In addition, it can reduce decision-making time by 220.65%.  
Proposed Framework Based on K-Means Clustering Technique to Provide Recommendations in Designing Job Rotation Supono, Arhens; Diar Astanti, Ririn
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 2 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i2.10911

Abstract

Designing work rotation (JR) is crucial for a company. It is necessary to design JR based on objective recommendations. With the current development of information technology, it is very possible for companies to store employee data digitally. Additionally, companies can process employee data using data mining techniques. Then the result can be used as a basis for designing JR. This research aims to provide a framework using the K-Means clustering technique to provide recommendations as a basis for designing JR. The proposed framework is implemented in a real case, specifically targeting 490 machine operators and technicians in a cigarette manufacturer in Indonesia. The clustering analysis results reveal a grouping of operators and technicians into five distinct categories. Furthermore, the characteristics of each group can be used as one criterion for providing recommendations for designing JR.
Co-Authors Seto, Franklin Chandra Pragnyono Supono, Arhens Yosef Daryanto, Yosef