Predictive analytics is very important for equipment improvisation and product design. It is also extremely important for equipment maintenance. It helps us know and predict the circumstances under which our manufacturing equipment is likely to break down. With the help of IoT sensors, we can predict the lifetime of our equipment and the possible breakdown period. This not only helps in proactive maintenance but also helps in faster equipment restoration. Predictive analytics is also helpful in the prevention and mitigation of damage that is likely to be caused by faults in the equipment. It needs to be noted that predictive analytics is also pivotal for product design and manufacturing. This technology not only helps in testing a specific product but also helps in predicting its relevance to a specific industry.
Predictive analytics has also helped us in the effective maintenance of information technology systems. Ranging from management tools in a particular data center to the cooling units installed on a server, predictive analytics is essential for timely servicing. In both hardware and software maintenance problems, predictive analytics comes to our rescue. In addition to improving computations, it also analyses system maintenance problems and performance issues.
Weather forecasting is a phenomenon that requires automated systems and large sets of data. In addition to its reliance on data mining, weather forecasting is also heavily dependent on predictive analytics. After large sets of data have been collected by meteorological systems, they need to be processed to be fed into analytical systems. These analytical systems are automated machines that utilize several features of predictive analytics.
Predictive analytics is also a critical component of sales tools like Salesforce. Apart from serving new customers, predictive analytics also helps us to redress the grievances of the existing customers. Though predictive analytics helps us in dealing with the problems of old and new customers, the methodology adopted is different for each category. A feedback mechanism is necessary to analyze the experience of the old customers so that it can be used to serve new customers actively. The grievances of customers need to be solved in a time-bound manner and it is predictive analytics that helps in better scheduling of tasks and drafting an effective redressal mechanism.
It is difficult to imagine a platform of e-commerce functioning without predictive analytics. For the effective functioning of any E-Commerce platform, customer behavior and customer journeys need to be tracked. The listing of products needs to be appropriate and in accordance with the needs of customers. The portfolio of each customer is different and the needs of all such customers need to be serviced differently. This is not possible without the aid of predictive analytics.
After new products have been manufactured, the next stage is quality assurance and testing. With the help of artificial intelligence and cognitive solutions, the overall quality and operation of the systems are put to a rigorous test. After this, various data sets which relate to the performance of the product during testing are collected and analyzed. By virtue of predictive analytics, we can predict the lifetime of the product and its period of optimum functioning.
Predictive analytics is one of the chief components of decision sciences. With the help of modern tools like the Azure machine learning studio, we can make informed decisions for business forecasting. This type of predictive analytics has helped in business intelligence and management.