Using Big Data for Machine Learning Analytics in Manufacturing

Ajit has over 10 years of experience in manufacturing operations and consulting. His functional expertise comprises Industrial Operations, Supply Chain, and Quality and Manufacturing Analytics. He has successfully executed various cost saving, process improvement and business intelligence (BI) projects for leading manufacturing companies. Saurabh has over three years of industry experience in the manufacturing and telecom domains. He has worked in the areas of research and analytics across Supply Chain, Sales and Marketing, Warranty, and New Product Introduction. His current focus is on BI and Big Data Analytics. Saurabh is an engineering graduate with a Diploma in Management. Aditya has over seven years of work experience in the IT industry, with over three years in the manufacturing domain as a functional consultant. He has worked in ERP development and implementation across small and medium businesses in India. He holds a Diploma in Management from IIM Lucknow. Machine Learning as a concept has been in existence for many decades now. However, most manufacturing operations — such as repairing an aircraft engine, planning the product mix in cement production, or ensuring energy control in a large facility — are still largely dependent on experience-based human decisions. The advent of Big Data technology, coupled with efficient data storage mechanisms and parallel processing frameworks, has found new use for the petabytes of data generated by manufacturing operations. Applying Machine Learning techniques to the shop floor has enabled increased accuracy in decision-making and improvement in performance. This paper explores how Machine Learning algorithms, in conjunction with Big Data technologies, can help manufacturers bring about operational and business transformation.