The turret machine is one type of lathe that has The main characteristic is that the tool for sequential operations can be set in readiness for use in the appropriate sequence. This turret machine is intended for cutting, drilling and smoothing work pieces. The process of making this collar aims to find out what material is used and the function of the collar. In the process, the iron is clamped with a chuck then the drill which is located on the loose head is brought close to the workpiece then the chuck rotates so that the workpiece has a hole with an inner diameter of 11.2 mm, after that the workpiece is sprayed with bromus liquid to ensure that the workpiece does not rust easily and does not experience excessive heat which can damage the workpiece. Entering the next stage, the workpiece is cut to a length of 9 mm, and the next process is that the workpiece is placed in a jumper. The cumper process functions to smooth the workpiece so that it is not rough before entering quality control. After that, the object enters quality control. In quality control, the work object is measured using a jig measuring tool to determine whether the work object is OK or NG. The aim of this process is to make quality spare parts that are safe to use. The disadvantage of the turret machine is that it is a manual process that requires hand speed and expertise in operating the turret machine..
 
 

Background: Traditional naval maintenance strategies rely on centralized supply chains and pre-manufactured spare parts, leading to long repair downtimes and logistical inefficiencies, particularly for vessels operating in remote maritime regions. Additive manufacturing (3D printing) offers a disruptive alternative by enabling on-demand production of spare parts, reducing dependence on external suppliers, and enhancing fleet self-sufficiency. However, material durability, operational feasibility, and cost-effectiveness remain underexplored for naval applications. Original Value: This research advances the study of AM in naval engineering, assessing its practical viability beyond theoretical potential. Unlike previous studies focusing on commercial maritime applications, this study evaluates 3D printing’s impact on naval fleet readiness, supply chain resilience, and sustainability. Objectives: The study investigates how 3D printing can optimize naval maintenance efficiency, specifically analyzing its feasibility, material performance, cost implications, and logistical advantages. Methodology: A qualitative-empirical approach was used, combining material performance testing, expert interviews, and operational case studies to evaluate mechanical durability, economic feasibility, and AM integration challenges. Results: Findings indicate that AM reduces repair downtime by 40%, lowers part procurement costs by 30–50%, and enhances supply chain resilience. However, material limitations and infrastructure readiness remain key adoption challenges. Conclusions: Hybrid AM adoption—where 3D printing supplements rather than replaces traditional manufacturing—offers the most practical near-term approach for naval fleets. Strategic investment in material research, onboard AM training, and fabrication infrastructure will enhance fleet efficiency, reduce environmental impact, and future-proof maritime maintenance strategies.

In an era of increasingly fierce competition in the energy sector, companies are required to not only meet customer needs, but also to improve service quality and customer satisfaction in a sustainable manner. This study aims to analyze and improve the performance of the Marketing and Customer Service Division at PT XYZ by applying the 5W1H method and the Fishbone Diagram.  The study identified a number of factors that contributed to the division's low performance, including a lack of adequate training and decreased work motivation. Through in-depth analysis, this study provides strategic recommendations to improve the quality of training, the development of an integrated information system, and the establishment of clear service standards. The implementation of these recommendations is expected to improve productivity, service quality, and customer satisfaction, as well as strengthen the company's position in a competitive market.

PT XYZ, which aims to optimize the production process of Airbus A350 aircraft wing components. The background of this research focuses on the importance of efficiency and effectiveness in the aviation industry, where each component must be produced to high standards to ensure aircraft safety and performance. In order to achieve this goal, the author applies several methods for completing the task, including system modeling and Interpretive Structural Modeling (ISM). This method allows for in-depth analysis of the structure and relationships between elements in the production system. Data processing is carried out through four main stages: model concept design, data collection, analysis, and evaluation. The raw data used includes the company layout and wing component production flow. The results of this data processing provide significant insights into potential improvements in the existing production system, as well as recommendations for improving operational efficiency. Thus, this report not only contributes to the development of knowledge in the field of systems engineering, but also provides practical advice for PT XYZ to improve their production performance.

Demand for additional electric power increases as electricity demand increases, but the process is often hampered by obstacles in the operational workflow. The main objective of this research is to analyze operational risks in kWh addition services using the Root Cause Analysis (RCA) method using the 5 Why technique and fishbone diagram. The RCA method equipped with a fishbone diagram will produce a mapping of the causes of operational risk obstacles in the PT XYZ kWh addition service in a structured manner. In the process, the author identifies and analyzes the various risks faced, and proposes improvements aimed at improving service quality. Based on the results of the research analysis, there are two operational risks related to kWh addition, namely: the migration process of postpaid kWh meters to prepaid meters and the use of electricity by customers who exceed the contracted power. The conclusion shows that most of the operational risks come from customer behavior. Proposed improvements include increasing the frequency of inspections, proactive planning for power additions, and transparency of information to customers. The results of this report are expected to make a positive contribution to operational risk management at PT XYZ and improve customer satisfaction.

PT. XYZ is an international company operating in the energy sector, especially oil and gas, with a focus on fabrication and construction activities for large-scale projects. One of the main production processes that is very influential is welding. To ensure welding quality, PT. XYZ uses the Non-Destructive Testing – Ultrasonic Testing (NDT-UT) method. However, during the inspection process, problems are often found in the form of rejects due to defects that do not meet welding quality standards. Some types of defects detected include LOF (Lack of Fusion), LOP (Lack of Penetration), and ESI (Excessive Spatter Inclusion). This research aims to determine the percentage of the most dominant welding defects, identify factors that cause defects, and provide recommendations for improvements to improve welding quality. The method used is Statistical Quality Control (SQC) with the help of tools such as check sheets, histograms, Pareto diagrams, scatter diagrams, control charts and fishbone diagrams. Based on the data collected, a LOF defect of 5,434 mm was found, an ESI defect of 1,990 mm, and an LOP defect of 1,030 mm. SQC analysis shows that the dominant defect is LOF with a percentage of 64%. The main causal factors come from methods, humans, machines and materials. Therefore, companies need to make improvements and developments to reduce product defects so that they can improve welding quality.

This research develops a predictive maintenance framework for hydraulic systems by utilizing Internet of Things (IoT) technology and machine learning. Hydraulic systems often experience unexpected failures, causing expensive downtime and disrupting industrial operations. By installing IoT sensors, data about system performance and condition can be collected in real-time. This data is analyzed using machine learning algorithms to detect patterns and signs of possible failure. The proposed framework enables early detection of problems and provides timely maintenance recommendations, improving operational efficiency and reducing maintenance costs. Test results show that this approach can improve the reliability of the hydraulic system and extend the service life of the equipment. This research makes a significant contribution to the development of innovative, data-driven maintenance solutions for industry.

This paper explores energy harvesting techniques applicable to Internet of Things (IoT) devices, focusing on renewable energy sources such as solar, thermal, and kinetic energy. The study reviews current technologies and their efficiencies, presenting a comparative analysis of different energy harvesting methods. Experimental results demonstrate the feasibility of integrating these techniques into low-power IoT applications, potentially extending the operational life of devices without the need for frequent battery replacements. The research underscores the importance of sustainable energy solutions in the IoT landscape.