Problems with the production process come first, followed by issues with the product's quality. Poor product quality, for instance, may cause a higher rate of client returns. These issues may not only be related to the supplier's products' quality, but may also be caused by events that take place inside or outside the business, such as mistakes made by people or machines that affect how the products are produced. If there is a disruption in the supply chain, things like lead times will lengthen, production planning will be illogical, and inventory levels will rise. Additionally, it will reduce manufacturing efficiency and result in idle personnel or machines. At Last, the majority of businesses follow the time-tested preventive maintenance tenet. However, the high-speed equipment of today demands a more dynamic, data-driven strategy. Downtime occurrences eventually happen when equipment isn't maintained on a regular basis. However, if equipment is "over-maintained," producers will lose money on unnecessary machine parts, supplies, labor, and downtime. Manufacturers can better predict when machines are likely to fail by using machine performance and health data, allowing them to do the proper amount of maintenance at the proper time. The data's dependability is not sufficient to foresee the full data, according to the company's financial prediction for its products. Frequently, that data merely fails to provide information about the direction of the company's real operation. Predictions have no value without valid data.