Published on February 9th, 2021 | by Ayushi Sharma

Understanding predictive analytics in manufacturing

With the advancement of technology and Industry 4.0 taking over the manufacturing industry, a tremendous amount of data is being generated. This data needs to be analyzed to generate actionable insights. But with the way the current market operated, the manufacturers need to think of a way ahead in the future. Predictive analytics in manufacturing is easing the operations, production, and much more for the manufacturers. It is transforming the way the manufacturing industry used to work.

Below are some of the key use cases of predictive analytics in manufacturing:

Predictive Maintenance

Machines at production units go through many rough conditions like high temperatures and pressures and more. This can cause a lot of unseen damage to the machines and their internal components. It can also cause breakdowns which can cost in terms of repairs and lost production time. Manufacturers understand the implications this can present and are now rapidly moving towards predictive analytics for resolution. By analyzing data coming from sensors and components and other sources, it can generate essential predictive insights. They can know when equipment is performing abnormally and needs replacement. They can know any potential machine failures and the cause for it. Based on historical data, they can also know which maintenance process is most efficient and cost-effective. Its power is not limited to this, but it can also send automated maintenance requests which help reduce operational costs over a period of time.

Enhancing complete manufacturing system

The manufacturing process is a long and complicated one, where various factors like raw material, machinery, supply and demand, and many other factors affect the final output and its cost. From seasonality to shipping location, the supply cost of raw materials keep fluctuating throughout the year. Moving on to the machines, they tend to breakdown affecting the production pace. A manufacturer used to deal with all of this in a reactive way. However, this has changed with predictive analytics implementation.

From identifying the optimum price and time for procuring raw material to recognizing all factors contributing to cost, identifying bottlenecks, and streamlining the complete process with appropriate forecasting, predictive analytics makes the complete manufacturing system smarter.

Forecasting Demand

Product demand fluctuates throughout the year based on multiple factors. Manufacturers need to understand the demand pattern to manufacture the goods in right quantity. With predictive analytics, they can analyze huge sets of historic data and other factors to understand this fluctuation better. This can empower them to make the right investments at the right time and prevent dead stocks or out of stock situations which can lead to huge losses. It can take into consideration consumer behavior, raw material availability, weather conditions, shipping location, and other factors to make accurate demand predictions. It can even identify hidden factors that influence the demand and provide game changing insights to manufacturers.

Enhanced Workforce Management

In a dynamic industry like manufacturing, human resource management is equally important and difficult. From finding the right employees and providing the right compensation to prevent attrition, HR managers have to cross multiple barriers on a daily basis. Managing employee productivity can be another challenge, which depends on demand and equipment conditions. HRs need to analyze data and forecast manpower need, training need, right scheduling patterns and more. By utilizing predictive analytics, they can strike the right workforce balance and ensure resource availability and productivity for better results.

Predictive analytics in manufacturing is certainly one of the most important things in the era of Industry 4.0. Manufacturers need to move from their traditional analytics tools to the AI- driven ones to survive and excel in this competitive market. With ever-changing consumer demands and industry trends, it only with predictive analytics that they can gauge the future and take the right steps to stay ahead.

Tags: manufacturing, predictive analytics