PREDICTIVE ANALYTICS IN MANUFACTURING

 

Predictive Analytics in manufacturing

 

Introduction

More data points can be gathered by real-time connected devices. This can aid in estimating the amount of time or the number of pieces that can be produced before a failure. Ordinary estimates, rather than actual needs, may be used in traditional maintenance plans to recommend when to replace parts or perform maintenance. Reducing unplanned downtime and removing unneeded and expensive maintenance services are both benefits of using data and factory analytics to predict problems.

 

What are Predictive Analytics?

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

Determining the likelihood of breakdowns is a typical manufacturing use of predictive analytics. Then, manufacturers can prepare ahead of time to turn off machines for preventive maintenance. Predictive analytics can also be used to restrict or eliminate any impact on the production pipeline.

Here are some use cases of Predictive Analytics in manufacturing:

 

1.     Predictive Maintenance: Predictive analytics can analyze historical and real-time data from sensors, equipment, and production systems to predict when machinery is likely to fail. By detecting early warning signs, manufacturers can schedule maintenance activities in advance, preventing costly unplanned downtime and reducing maintenance costs.

2.     Quality Control: Predictive analytics can analyze data from various sources, including production sensors, quality control checks, and historical data, to identify patterns and correlations related to product quality. This enables manufacturers to proactively detect defects, minimize scrap and rework, and optimize the production process for consistent quality output.

3.     Supply Chain Optimization: Predictive analytics can be used to analyze data from suppliers, inventory levels, demand patterns, and external factors such as weather or market trends. This helps manufacturers optimize their supply chain operations by forecasting demand, managing inventory levels, optimizing procurement, and improving overall supply chain efficiency.

4.     Energy Management: Predictive analytics can analyze energy consumption patterns, equipment efficiency, and historical data to optimize energy usage in manufacturing facilities. By identifying energy waste, manufacturers can implement energy-saving measures, reduce costs, and minimize their environmental footprint.

5.     Demand Forecasting: Predictive analytics can analyze historical sales data, market trends, customer behavior, and external factors to forecast future demand for products. This helps manufacturers optimize production planning, inventory management, and resource allocation, ensuring they meet customer demand while minimizing excess inventory or stockouts.

6.     Process Optimization: Predictive analytics can analyze real-time data from production lines, machines, and sensors to identify process inefficiencies, bottlenecks, or anomalies. By optimizing production processes, manufacturers can improve throughput, reduce cycle times, and increase overall productivity.

 

Conclusion

It determines the likelihood of failures and their causes for each step in the production process. Machine learning and automation are the icing on the cake. By alerting management to potential issues in advance, an automated predictive analytics project makes the entire process easy.

 

References

1.     https://www.google.com/search?q=predictive+analytics&oq=pred&aqs=chrome.3.69i59j69i57j35i39i650j0i20i131i263i433i512j0i131i433i512j0i512j0i433i512j46i433i512j0i433i512l2.4926j0j15&sourceid=chrome&ie=UTF-8

2.     https://www.machinemetrics.com/blog/predictive-analytics-in-manufacturing

 

 

Narsima Ahmed

@INTERNATIONAL SCHOOL OF MANAGEMENT EXCELLENCE

Intern @Hunnarvi Technologies under guidance of Nanobi data and analytics pvt ltd.

Views are personal.

#analytics #predictive analytics #nanobi #hunnarvi #ISME

 

 

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