Care and maintenance are vital to eliminate unplanned downtime. But you often find yourself confused between when to repair and when to replace the machine part. And if you have made a guess without much thought, it can result in massive inefficiencies. Because of this, many organizations are figuring out how AI and machine learning can provide deep insight in order to reduce failure and eliminate unplanned downtime.
We are heavily dependent on machinery to perform daily chores. We use buses, cars, and trains to travel. We have to depend on agricultural tools to harvest food and farm. In fact, you cannot imagine your everyday life without the presence of machinery. All is good until one day these machines fail us!
Downtime is Reducing Productivity
Remember, the biggest obstacle in the path of productivity is downtime. It takes the problem of only one machine to halt the entire supply chain. According to one survey, unplanned downtime costs industries roughly $50 billion annually.
Businesses are aware that a minor downtime is unavoidable since machines are sometimes taken off for inspections or repairs. Still, businesses try their best to keep the operations smooth, but this unplanned downtime causes disruption one way or the other.
Plan to Eliminate Unplanned Downtime
Unplanned downtime (reactive maintenance) is the perfect recipe for failure. Businesses cease when machines fail without warning. With planned maintenance, businesses have a greater chance of fixing the problem before time.
The best solution is proactive maintenance which employs analytics to check the capacities of the machines and optimize maintenance resources.
Predictive Maintenance
It all comes to predictive maintenance. Many industries are now investing in the predictive maintenance approach to eliminate unplanned downtime. The capabilities of artificial intelligence can transform preventive or proactive maintenance into predictive maintenance. In the predictive maintenance approach, repairs only occur when necessary.
These wonders are made possible by the qualities of AI like switches, connected sensors, and AI-enabled tools that can analyze the thousands of variables and put forward insights that otherwise would not have been detected.
Applications of Predictive Maintenance
Numerous industries have already embraced some form of predictive maintenance through continuous monitoring technologies. Since artificial intelligence gathers a massive amount of data with machine learning and advanced algorithms, businesses are able to use deep insights to make better decisions.
Let’s learn some of the applications of predictive maintenance used.
Automotive
It is recently learned that BMW aims to enhance its decision-making by using the huge amounts of data generated across, production, automotive design, and distribution.
Car manufacturers can bring efficiencies in their supply chains and highlight errors by vetting this data and filtering it through powerful analytics before the problem has occurred.
Trains
In Germany, Deutsche Bahn is trying to improve its preventative maintenance technology. They are aiming to make advanced data analytics recognize patterns in operating data by using algorithms and smart sensors.
Airlines
Scientists are trying to develop tiny robots that would enter the plane engine to carry out repairs or inspect its parts. Currently, such robots are being programmed to carry out inspections. But once this phase is perfected, the scientists are confident that the robots will be able to make repairs which will ultimately eliminate unplanned downtime
AI is the Future
Artificial intelligence can give suggestions to businesses where the investments should be made to eliminate unplanned downtime. Companies that will prepare themselves early will maintain a competitive edge over others in the future.
That is why you should also reach out to the AI experts at vteams to minimize downtimes in your organization.
