As part of an ongoing series of Industry 4.0 articles to help manufacturers embrace the fourth Industrial Revolution, we will be going through some concepts, theories, and practical examples. Some of the articles include those about Industry 4.0 success stories, Equipment-as-a-Service, Industrial Internet of Things, and more. To keep up with the series, sign up for our newsletter.
It has been more than 10 years since the term ‘big data’ was introduced to the general public in a 2007 Wired article. 11 years later, the term has become one of the most frequently used buzzwords, with companies across a wide spectrum of industries all investing into data analytics, data science, and other related technologies.
This has also affected manufacturing businesses, as big data is one of the key tenets of Industry 4.0.
What is Big Data?
From our Industry 4.0 glossary, the definition of ‘big data’ is:
“Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.”
Instead of small, silo-ed files that exist purely for administrative purposes, big data deals with the immense amount of data that companies can then use in order to benefit their business.
One of the most popular statistics used in big data is the statement made by Eric Schmidt, the former executive chairman of Google, back in 2010.
He spoke at a conference about the fact that we were currently producing the same amount of data every two days, as had been created from the beginning of human civilisation till the year 2003.
Big Data and Industry 4.0
It stands to reason, then, that big data has also come into play in the realm of manufacturing. Businesses across the different manufacturing verticals have been dealing with the advent of Industry 4.0, which includes as a key tenet, big data itself.
Big data is closely related to advanced analytics, which helps businesses apply the results gleaned through analysing the data to produce actionable steps. One way in which big data has helped manufacturing immensely is in the form of supply chain performance.
A McKinsey report from 2014 describes how operations managers “can use advanced analytics to take a deep dive into historical process data, identify patterns and relationships among discrete process steps and inputs, and then optimize the factors that prove to have the greatest effect on yield”. That helps with making decisions on the shop floor, predictive maintenance, and other asset performance nodes.
A Forbes article speaks about the efficiency and accuracy that big data analytics can provide manufacturers such as biopharmaceutical companies, which have multiple variables that need to be monitored and tailored simultaneously to produce higher yields.
Big Data in 2019 and Beyond
Currently, big data is one of the most frequently used terms in businesses looking to stay profitable in the age of Industry 4.0. Back in 2016, a Honeywell survey showed that 68% of manufacturers were already investing in data analytics.
The combination of big data with other Industry 4.0 staples, like the Industry Internet of Things (IIoT), has become a must-do for manufacturers intent on maintaining their competitiveness.
As we look towards 2019, 2020, and years ahead, many introductory (or test) programmes are now showing enough results to push big data into the wider manufacturing front; it is now understood that big data is necessary for manufacturers. At its base, big data (and advanced analytics) can allow manufacturers to gain visibility into previously unknown factors and variables, and work to have those variables become predictable and beneficial for the business. Output forecasting, supply planning, and support for the mass-customisation of manufacturing are just some use cases that companies can implement.
However, the new challenge focuses on the implementation of big data across the entire manufacturing process, from the front-end CRM and sales process, to the back-end ERP and shop floor. How can manufacturers ensure that the data they collect is analysed accurately and then redeployed for better use across the business? That is where the rest of Industry 4.0 comes into play; big data analytics needs to be used in tandem with other Industry 4.0 technologies, such as automation, IIoT, and more, to work best.