Prevention is not enough in the new normal: Predictive Maintenance
Most people have heard the saying, “Prevention is better than a cure”. Reactive maintenance is considered as poor maintenance since management would replace the unit once the equipment unexpectedly breaks down. Unplanned downtime costs industrial manufacturers a whopping estimate of $50 billion annually and 42% of this unplanned downtime is due to equipment failure. Therefore preventive maintenance is conducted by regularly inspecting the equipment. Most manufacturers do monthly inspections halting operations and then inspecting the equipment if there are minor issues within the system.
Prevention Maintenance in Global Pandemic
If the inspections did not detect any faults, then the labor and material costs would be considered as excessive. Planned downtime is most likely to be shorter than unplanned but any kind of downtime would result in millions of dollars wasted. Furthermore, the parts on the machine are being replaced based on the theoretical rate of failure given by the original equipment manufacturer (OEM). The theoretical rate of failure does not factor in the different conditions in different manufacturing plants. Parts can wear down before reaching failure rate or can still be in good condition despite exceeding the failure rate. This proactive approach not only requires excessive downtime and resources but also it also requires more extensive inventory management for replacement components.
These issues were further exacerbated by the global pandemic of 2020. During the COVID-19 pandemic, demand skyrocketed when people started to buy essential items like face masks, alcohol, and toilet paper. Supply chains were also cut abruptly so manufacturers had to work with the equipment they currently had. In fact, some of the workers were forced to stay at home. The manufacturers needed to reduce the downtime to satisfy demand, utilize the equipment that they currently have, and monitor the equipment from their homes.
With the way the world is currently adapting manufacturers must install innovative systems to provide products and services to their customers. As we progress through Industry 4.0, several innovations have been created within the factories to increase operational efficiency. One of these improvements is called predictive maintenance. Predictive maintenance utilizes smart sensors and gathers data from the equipment in the manufacturing plant. Based on the data gathered, artificial intelligence can determine what are the faults within the system and the remaining useful life of the equipment.
Monitoring the equipment’s health using real-time data would have accumulated millions of dollars of savings. If the predictive maintenance system is installed properly, planned downtime for diagnostics would decrease. This will increase operational efficiency in such a way the manufacturing plant can stay afloat in times where crises happen intermittently. The only downtime the manufacturing plant will occur is when the system predicts that certain equipment is on the verge of failure and needs to be replaced. Since the remaining useful life of the equipment has been maximized, the maintenance costs are very minimal compared to preventive maintenance. When countless issues have come to light, predictive maintenance can be used to adapt to challenging times.
Challenges of Predictive Maintenance
The features of predictive maintenance may seem complicated since the user must adapt to technological advancements such as data management and digital threads. The flow of data from the sensors must be distributed towards a hub. The hub will then be analyzed by artificial intelligence (AI) and provide insights into the data. The data is then presented to the user through the use of charts and graphs. The user must also ensure that the data that is sent is not compromised and that the AI can gather useful data to accurately predict failures. The systems to integrate predictive maintenance may be too complex, especially for engineers who are used to the current preventive maintenance. However numerous services have been created for companies to smoothly transition to having a smart and data-centric manufacturing process.
As a summary:
Reactive maintenance is ineffective since the machine is only replaced when it reaches the point of failure
Preventive maintenance is implemented by most plants by inspecting the equipment on a routine basis and replacing parts based on the life cycle indicated by the manufacturer
COVID-19 forced manufacturing plants to down at least as possible and maximize the equipment’s health.
Predictive maintenance overcomes the hindrances of COVID-19 by utilizing smart sensors, secured digital thread, and artificial intelligence
Even if changing from preventive to predictive may seem overwhelming, new companies provide these solutions and services to shift into predictive maintenance with ease.