Exploring the Possibility of Big Data and Machine Learning in Steel Melting Shop Logistics Planning

K. Abhishek¹ , A. Ganvir² , A. Rouf³ , A. Das⁴ , K. Rama Krishna⁵

Section:Review Paper, Product Type: Isroset-Journal
Vol.5 , Issue.5 , pp.1-5, May-2019

Online published on May 31, 2019

Copyright © K. Abhishek, A. Ganvir, A. Rouf, A. Das, K. Rama Krishna . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The efficacy of a manufacturing process is pinned on proper logistics management that lies at the core of a smooth, uninterrupted production process. The same is true for steel melting shops (SMS), which is a quintessential example of the importance of proper logistics management in a steel plant. As SMS further comprise of sub-units, viz., the converter shop, secondary refining unit (SRU) and caster shop, the onus of the synchronization between these units lies on logistics planning. In SMS, steel ladles filled with liquid steel move from one unit to another by overhead cranes and transfer cars for treatment. Therefore, the steady movement of ladles in the shop is the essence of SMS operation. The aim of the paper is to explore the possibility of Machine Learning in automating the logistics planning, monitoring and controlling processes in SMS, which, at present, is left entirely at the disposal of shop floor personals. This work deals with Machine Learning on the surface. Though the limited scope of the paper may not dwell deep into the topic, which is required to come up with a complete model, it provides the initial groundwork to develop smart SMS.

Key-Words / Index Term :
steel melting shop, machine learning, big data, artificial intelligence

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