Big Data and Its Importance in Manufacturing

Deepak Mathivathanan and Sivakumar K.

Introduction

Big Data has become an unavoidable term both with academicians and practitioners in the present information age. Since the beginning of the 21st century, technology has been playing a major role in our lives with remarkable innovations in terms of digital devices which can be used to learn about human behaviour. In 2010, Eric Schmidt, the former C.E.O. of Google, reported that There were five Exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every two days, and the pace is increasing’ (Schmidt & Cohen, 2013). Most companies are storing and utilising enormous amounts of data as information for analysing their business’s progress. IBM reported that every day 2.5 quintillion (2.5 x 1018) bytes of data is generated from various sources like in messages, digital pictures, invoices, videos, sensors, social media posts and from numerous other digital sources (IBM, 2014). By 2020, approximately 100 billion connected devices will be producing data and thus the application of Big Data analytics has become a necessity (Walport, 2014). This enormous amount of data is referred to as ‘Big Data’, and it can be systematically processed and analysed to understand the current trends towards developing competitive business models. Modern manufacturing facilities include technologies such as the Internet of Things (I.o.T.s) and cyber-physical systems (C.P.S.) which are capable of recording and transmitting raw low-level granular data. These captured data can be subjected to analytics and any modelling applications to derive insights to improve the existing operations. The analysis of the captured Big Data is called Big Data Analytics. Beyond the rhetoric, this chapter is dedicated to details about what are the difficulties encountered by the manufacturing industries and how Big Data can impact manufacturing in facing the challenges.

 
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