The IoT is revolutionizing Jishu Hozen by enabling real-time monitoring and predictive maintenance. By equipping machines with sensors that gather and transmit data, organizations can monitor equipment status in real-time, predict failures before they occur, and schedule maintenance more effectively. This proactive approach to maintenance aligns perfectly with the principles of Jishu Hozen, where the goal is to prevent breakdowns and maintain optimal equipment performance. According to a report by McKinsey & Company, IoT applications in manufacturing could generate up to $3.7 trillion in value by 2025, much of which will come from enhanced predictive maintenance capabilities.

Real-world examples of IoT in Jishu Hozen include smart factories where sensors detect abnormal vibrations, temperatures, or sounds from equipment, signaling the need for maintenance before a breakdown occurs. For instance, Siemens has implemented IoT sensors in its gas turbine factories to predict maintenance needs, significantly reducing unplanned downtime and improving efficiency. This technology empowers operators to take immediate action, fostering a sense of ownership and responsibility towards their equipment.

Moreover, IoT facilitates a deeper understanding of equipment behavior over time, allowing for continuous improvement in maintenance practices. By analyzing historical data collected from sensors, organizations can identify patterns and root causes of equipment issues, leading to more effective Jishu Hozen strategies.

AI is another powerful tool in advancing Jishu Hozen practices. Through machine learning algorithms, AI can analyze vast amounts of data from equipment sensors to predict failures with high accuracy. This capability enables organizations to transition from traditional, calendar-based maintenance to condition-based and predictive maintenance models. A study by Accenture revealed that AI could increase profitability rates by an average of 38% by 2035, with the manufacturing sector being one of the primary beneficiaries due to improved maintenance strategies.

For example, AI-driven analytics platforms can alert operators about potential equipment failures weeks or even months in advance, allowing for timely intervention without disrupting production schedules. General Electric’s Predix platform is a notable example, offering AI-powered insights that help operators predict and prevent equipment failures, thereby enhancing the effectiveness of Jishu Hozen initiatives.

Furthermore, AI enhances decision-making in maintenance by providing recommendations based on predictive analytics. This not only improves maintenance efficiency but also supports operators in developing a deeper understanding of equipment health, contributing to a culture of continuous improvement and operational excellence.

AR is transforming Jishu Hozen by providing operators with real-time, hands-free access to information and guidance during maintenance procedures. Through AR headsets or mobile devices, operators can view digital overlays of maintenance instructions, diagrams, and machine data directly on their field of vision, reducing errors and improving efficiency. Gartner predicts that by 2025, over 50% of field service management deployments will include mobile AR collaboration and knowledge sharing applications, up from less than 1% in 2019.

An example of AR in action is Porsche’s use of AR glasses in service centers, which allows technicians to receive remote support from experts, overlaying instructions and diagrams onto the physical equipment. This not only speeds up maintenance tasks but also enhances the learning and autonomy of operators, key principles of Jishu Hozen.

AR also facilitates better training and skill development for operators, enabling them to perform maintenance tasks with greater precision and confidence. By simulating equipment problems and maintenance procedures, AR provides a safe and effective training environment, further strengthening the capabilities of the workforce in line with Jishu Hozen objectives.