International Journal of Industrial Engineering and Engineering Management

Achieving sustainability in transportation and supply chains is critical in today's competitive, environmentally conscious world. This study focuses on prioritizing factors for a sustainable Galvalume steel supply chain in Indonesia, emphasizing material availability, efficient logistics, and eco-friendly practices. A new method utilizing the Analytic Hierarchy Process (AHP) is proposed to address the balance between economic efficiency and environmental responsibility. Six criteria were identified: material availability (X1), transport choice (X2), cost-effective packaging (X3), customer service (X4), profit optimization (X5), and environmental safety (X6). Results indicate that material availability (X1) is the top priority, followed by cost-effective packaging (X3) and environmental safety (X6). The study highlights the importance of consistent decision-making in sustainable supply chain management and identifies opportunities for incorporating electric vehicles and greener transport methods in future research. Overall, this research provides actionable insights for optimizing Galvalume steel supply chains with minimal environmental impact.

PT XYZ-Banten is the largest power generation company in Southeast Asia with a total output power of 3400 MW. The production process at PLTU XYZ is a continuous process. Of the 6 pumping machines that operate, if one of the machines or equipment is damaged or fails, the entire process will be stopped. Boiler Feed Pump (BFP) is the pump machine with the highest damage rate, with a percentage of 60% of all pumps located at PT XYZ-Banten. This research aims to optimize maintenance to increase reliability and minimize downtime on the BFP pump. The stages of the research with maintenance analysis using the Reliability Centered Maintenance (RCM)-FMEA method to identify the causes of failure, the impact caused, the category of damage, and the actions to be taken. The results with the Reliability Centered Maintenance (RCM)-FMEA method show that the most critical component is ARV with its task selection Condition Derect (CD) and the maintenance interval period for each component, including ARV 73 hours, Pipe 334 Hours, Mechanical Seal 960 hours, and Scooptube 1080 hours. Finally, the proposed improvements with 5W + 1H for more reliable and efficient maintenance optimization. Management implications are expected to help companies prevent machine failures and determine other factors that cause these failures.

This study examines the operational challenges faced by an offshore company that has specialized in rigs and floaters, repairs and upgrades, offshore platforms, and specialized shipbuilding over the past seven years. Despite steady growth, the company has encountered significant issues related to contingency fund management during the construction phase. To mitigate unpredictable risk exposure, the company applies a 20% contingency to the total cost estimate of every offshore construction material. However, this approach has led to a consistent 10% surplus, resulting in excessive costs and inventory. The research aims to evaluate the effectiveness of the current contingency allocation strategy and propose solutions to reduce surplus costs and excess inventory. By analyzing the company's data, the study identifies key inefficiencies and suggests optimized approaches to contingency fund management. The findings aim to provide actionable insights for enhancing financial and inventory management practices, ultimately improving the company's overall operational efficiency and profitability.

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.

This paper aims to identify the gaps between the bootcamp program and industrial needs using text mining techniques. The case studies focus on bootcamp programs for full-stack laravel vue.js developers, and uiux designers. The data utilized includes curriculum or syllabi from the boot camps and industrial requirements gathered from online job portals, particularly job descriptions from Indeed.com. The analysis revealed a gap of 30% between the bootcamp curriculum and industrial needs for full-stack Laravel vue.js developer, and 20% between the bootcamp curriculum and industrial needs for uiux designer. Based on this identification, alternative solutions were proposed to enhance the curriculum or syllabus. The results of the usability test are OK level. It is indicated that satisfactory outcomes, suggesting that the proposed improvements could help make bootcamp programs more responsive to the evolving needs of the related industry.