Predictive Analytics application in steel manufacturing

Predictive Analytics application in steel manufacturing

Data is a valuable asset for any manufacturing industries in the current time, only if they know how to extract hidden value from it. Data mining and Predictive analytics are the great tools that help to optimize the process operations, minimize the operational cost, reduce rejection or rework, and increase profits. Data mining applies machine learning and other complex algorithms to discover hidden patterns from the data. Predictive analytics is the process of using business knowledge to extract hidden value from those newly discovered patterns and make the prediction about future outcomes [1].

Data mining + business knowledge = Predictive analytics [2].

Traditionally in steel manufacturing environment quality of the products are determined after their final production. If the quality metrics of the finished products do not meet customer requirement, it is either reworked or rejected. This access inventory and rework create a bottleneck in the production cycle. As the quality check is done after final production, there is a necessity to forecast quality metrics to ensure it meets customer needs.

Predictive Analytics is applied to the industrial data collected over the years for developing various data learning model to estimate the quality of the products. Moreover, if the quality does not meet the desired range, it will help us to adjust the process parameters before production to achieve customer-defined quality. This way predictive analytics plays a major role in optimizing and control the process variables to manufacture products with minimum rejection, good quality, low production time, and better efficiency.

In a nutshell, Predictive Analytics and Data mining plays a critical role in the transformation of traditional manufacturing to smart manufacturing in current times [3]

References –

[1] D. T. Larose and C. D. Larose, Data mining and predictive analytics, 2nd ed. 2015.

[2] D. A. B. Dr. Tommy Jung, Dr. Mohamed Chaouchi, Predictive Analytics for Dummies. For Dummies, 2016.

[3] “worldsteel | Our stories: Steel rises to the challenges of Industry 4.0.” [Online]. Available: https://stories.worldsteel.org/innovation/steel-rises-challenges-industry-4-0/. [Accessed: 07-Jun-2018].

 

Jaspal Singh

Graduate Student – University of Regina

 

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