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The Utilization of GPS and GSM technology in operational vehicle monitoring offers convenience and efficiency in management. This research designs a GPS-based vehicle monitoring system that is integrated with vehicle maintenance management to assist companies in making decisions related to fuel efficiency, predictive maintenance scheduling, and safety aspects. By monitoring vehicle position and condition in real-time, companies can optimize travel routes, reduce travel time and reduce operational costs. The system also includes tire pressure and fuel percentage monitoring, which serve as important indicators in an accurate and sustainable vehicle maintenance strategy. This research uses quantitative methods to analyze various aspects in designing a GPS-based monitoring system that is integrated with operational vehicle maintenance management. With the resulting data, managers are able to identify trends that support predictive maintenance as well as preventive steps to reduce the risk of sudden damage. The research results show that the integration of GPS and GSM provides significant benefits in improving performance and reliability for vehicles, especially in the distribution and logistics sectors. The implementation of this system is expected to support the company in maintaining optimal vehicle performance and contribute to operational sustainability.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

The continued advancements in Internet of Things (IoT) and Machine Learning (ML) technologies have led to their adoption in various domains including in industries for predictive maintenance among other applications. Given the resource constraints of IoT devices, they cannot process the resource-intensive ML algorithms hence data collected by the devices are first sent to the cloud where the algorithms are hosted for processing and inference with the results being sent back to the devices for action and/or notifications. The need to transmit data to the cloud for processing leads to increased costs, energy consumption, and high latencies affecting the implementation of the solution. Interestingly with Tiny Machine Learning (TinyML), it is possible to develop algorithms enabling edge inference on resource-constrained devices. From existing review papers, the researchers were not able to find, a comprehensive review with a focus on this area showing the need for a targeted review that can shed light on how TinyML can be tailored for predictive maintenance tasks in industries. This study therefore presents a systematic literature review of the application of TinyML in predictive maintenance in industrial settings. TinyML overview and its benefits are presented, a TinyML process flow is proposed and various use cases and their classifications have been presented. Through this exploration, the study shows the critical need for TinyML-driven solutions in predictive maintenance, identifies the existing challenges, and proposes a roadmap for future research.

This research investigates ship maintenance management within the context of vocational education in maritime engineering, focusing on the experiences of senior students during internships in port and shipping management industries. Utilising qualitative methods, including semi-structured interviews and focus group discussions, the study explores the practical application of theoretical knowledge in preventive, corrective, and proactive maintenance strategies. Seven participants from a shipping vocational school in Jakarta provided insights into methodologies, challenges, and educational outcomes in ship maintenance. Key findings highlight the effectiveness of preventive maintenance in enhancing operational reliability and reducing downtime, despite challenges such as resource constraints and operational disruptions. Recommendations include curriculum enhancements to integrate advanced training in predictive maintenance technologies and promote sustainable practices. The research underscores the importance of vocational education in preparing future maritime engineers for industry demands and advancing sustainable practices in maritime transportation.  

Energy efficiency has become a cornerstone in industrial optimization, reducing operational costs and contributing to sustainability. This paper reviews key innovative approaches in mechanical systems used to enhance energy efficiency within industrial applications. It covers advances in system design, smart technologies, automation, and predictive maintenance. By understanding these techniques, industries can make strides toward greener production processes, lower energy costs, and reduced environmental impact.

Artificial Intelligence (AI) is transforming mechanical engineering and industrial processes by introducing unprecedented levels of efficiency, precision, and innovation. From predictive maintenance and autonomous robotics to material optimization and digital twins, AI-enabled systems are reshaping the industry landscape. This article examines key applications of AI in mechanical engineering, exploring how they contribute to sustainable industrial innovation, improve productivity, and pave the way for future advancements.

This study explores an artificial intelligence (AI)-based predictive maintenance system for industrial machinery in Indonesian manufacturing. By utilizing machine learning algorithms, the system can analyze real-time machine data to predict equipment failures and recommend timely maintenance actions. The implementation of predictive maintenance has shown to reduce machine downtime by 20% and improve operational efficiency in manufacturing plants in Jakarta and Surabaya. This paper discusses the technical design of the predictive maintenance system, its economic impact on production costs, and implications for Indonesia's industrial sector.

PT The lathe is one of the essential machines in production activities in this company. The amount of downtime caused by machine failure will reduce product quality and quantity. Therefore, this research aims to identify the factors that most influence the risk of lathe failure so that repair priorities and maintenance strategies can be determined. This can be achieved by using the Failure Mode and Effect Analysis (FMEA) method. FMEA is a method that can be used to identify the causes and impacts of each possible failure mode on machine components by systematically explaining the levels of failure so that appropriate prevention or repair can be carried out. The FMEA method shows the Risk Priority Number (RPN) value as a reference in determining the choice of maintenance strategy, namely, predictive maintenance, preventive maintenance or corrective maintenance. The research results show that the highest RPN value is found in the motor shaft pulley component at 320 and Nut Screw at 210. Maintenance priorities are determined based on the Pareto diagram principle. The appropriate maintenance strategy carried out by PT.   Keywords: FMEA, Lathe, Maintenance, RPN