The goal is to go beyond knowing what will happen in the future and influence it!!!

Predictive analytics used in a wide range of industries and operations, including retail, manufacturing, supply chains, financial services, healthcare & network management to predict the future probability for each customer, employee, product, vehicle, component, machine, to determine, inform, or influence organizational processes such as in marketing, credit risk assessment, fraud detection and government operations including law enforcement.

Predictive analytics is often discussed in the context of ever-growing big data, engineering data, data comes from sensors, instruments, and connected systems, a business system data, includes transaction data, sales results, customer complaints, and marketing information which allows users to project future outcomes based on historical data and machine learning techniques.


Data-driven predictive models can help companies solve long-standing problems in new ways for example:


Machine Utilization: A manufacturer is only as good as the machines that produce its products on time, Unfortunately machines break down, parts wear & tear, and the cost of equipment replacement can easily cost thousands of dollars. The use of Predictive analytics in manufacturing is enabling manufacturers to make better use of machine loss by analysis of data from sensors within equipment and automating the actual operation of these machines. Essentially, the manufacturer can determine when machines may need to be brought online or shut off to prevent a breakdown issue.


Preventive Maintenance: Preventive maintenance aims to reduce the issues found in devices by triggering alerts or calls for assistance from machines, based on the data captured inside the machines. This is a critical step in ensuring a manufacturer has all of the machines operating at maximum efficiency. The use of predictive analytics in manufacturing could be used to identify equipment manufacturer defects in machines by altering, the repairs of a broken, torn belt, reducing product demand and load on this particular machine.


Quality Improvement: Quality improvement is one of the most common predictive analytics. Databases can be aggregated faster, data is cleansed quicker, and data is stored in smaller spaces than ever before. As a result, the overall quality of the predictive analytics model is enhanced, providing a more robust plan of action for the manufacturer.


Follow us to stay updated about our next blog on the Importance of Predictive Analytics in Manufacturing 4.0.