At Suanfarma CDMO, we are committed to pushing the boundaries of maintenance management by utilizing advanced data and technology to foresee and prevent equipment failures before they occur. This forward-thinking approach significantly enhances our operational efficiency, ensuring that our machinery and equipment are always in optimal condition.
Moving Beyond Traditional Maintenance Methods
Traditionally, maintenance has been handled in one of two ways: preventive maintenance, where interventions are scheduled at regular intervals, and corrective maintenance, where repairs are made only after a failure occurs. While these methods have their merits, they also come with limitations—such as unnecessary downtime or unexpected equipment breakdowns.
We take a different approach with predictive maintenance, which is based on the continuous monitoring of equipment conditions. By using sensors and advanced analysis tools, we can predict the exact moment when maintenance should be performed, avoiding unnecessary interventions and reducing the risk of unexpected failures.
Key Benefits of Predictive Maintenance
- Continuous Monitoring: Our equipment is equipped with sensors that continuously monitor its condition, providing real-time data.
- Data Analysis: We leverage sophisticated analysis tools to interpret the data, predicting when maintenance is necessary.
- Scheduled Interventions: Maintenance is scheduled based on the actual needs of the equipment, rather than arbitrary timelines.
- Resource Optimization: By intervening only when necessary, we optimize the use of resources, including time, labor, and materials.
- Increased Reliability and Availability: Equipment is more reliable and available because it is maintained in peak condition.
- Reduced Downtime: Predictive maintenance minimizes unexpected equipment failures, thereby reducing downtime.
- Cost Savings: By preventing breakdowns and optimizing maintenance schedules, we achieve significant cost savings.
- Improved Safety: Well-maintained equipment is safer, reducing the risk of accidents.
How We Apply Predictive Maintenance
At Suanfarma CDMO, our predictive maintenance strategy is built on the following main pillars:
- Autonomous Maintenance: We transform traditional maintenance techniques into automated processes using cutting-edge technology. This not only simplifies operations but also accelerates them, allowing our team to focus on more complex tasks.
- Vibration Measurement: Our mechanics and engineers conduct real-time data measurement and monitor vibration trends. This enables us to predict the optimal time for overhauls, ensuring that we maximize the use of spare parts and extend the lifespan of our assets.
- IoT Connectivity: By integrating IoT inverters, we can remotely monitor equipment conditions and make accurate predictions through data analytics. This seamless connectivity allows us to take preemptive action, reducing the risk of unexpected failures.
- Oil Analysis: Even for routine processes like oil changes, we apply predictive maintenance. By analyzing oil conditions, we determine the best strategy for oil replacement or treatment, which is crucial in maintaining the efficiency of power transformers and air compressors.
At Suanfarma CDMO, our predictive maintenance approach not only ensures that our equipment operates at its best but also supports our broader goals of efficiency, cost-effectiveness, and safety. By continuously innovating and applying the latest technologies, we remain at the forefront of the industry, delivering reliable and high-quality services to our clients.
