Smart Manufacturing

CHAPTER 7Big Data for Small, Midsize, and Large Operations

Omar Abdon and Randy Shi

Introduction

In this chapter we describe the basics of data analytics and the migration to Big Data analytics when massive data volumes overwhelmed traditional methods of analysis, and detail the four process steps and the tools used in Big Data analytics. We demonstrate why Big Data is essential to manufacturing organizations of all sizes, and finally we demonstrate how Big Data analytics is helping small to midsize enterprises (SMEs), including best practices and affordable tools.

Data are facts and statistics collected together for reference or analysis. People have been analyzing data for thousands of years, but the Industrial Revolution (Industry 1.0) greatly increased available data for analysis, with the invention of the printing press making mass literacy possible.

The growth of data continued under Industry 2.0, with telegraph, telephone, and faster travel using trains and automobiles. The first uses of data analytics in business dates back to the turn to the early twentieth century, when Frederick Winslow Taylor initiated his time management exercises. Henry Ford's measuring the speed of his assembly lines is another early example.

Industry 3.0 accelerated the growth further with computers, software applications, the Internet, smart machines, barcoding, robots, and so on. Computers were key in the evolution of data analytics, as they were embraced as trusted decision-making support ...