The Rise Of Big Data And Analytics In Manufacturing

In last week’s article, we discussed the rise of Industry 4.0 in manufacturing. Indeed, Industry 4.0 has the potential to revolutionise manufacturing practices.

In this week’s article, we will be discussing a specific facet of Industry 4.0: big data and data analytics, and their use in manufacturing. We will cover what exactly big data is, how big data is utilised by manufacturing companies, and how companies are preparing themselves for big data integration.

What is Big Data?

According to IBM, “[Big data] can be defined as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Characteristics of big data include high volume, high velocity and high variety… Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI), mobile devices, social media and the Internet of Things (IoT).”

Essentially, it is the collection and processing of vast amounts of data from various different sources aimed at optimising specific business processes and decision-making. According to Verdict, sources of data include but are not limited to: Social media, payments, sensor activity, machine-to-machine exchanges, etc. Advanced analytical tools, statistical models, and specifically-created software and even artificial intelligence models are utilised to aid the processing of data, writes McKinsey. 

Among the business processes companies aim to optimise through the use of big data and data analytics, according to IBM, include but are not limited to: Cost controls, resource management, the promotion of greater sustainability, the improvement of customer experiences, and the forecasting of product demand and production. Big data and data analytics have, as a result, become an integral aspect of managerial decision-making.

Big Data And Manufacturing

According to Forbes, “Better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%) are the top three areas big data can improve manufacturing performances.” Additionally, “Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real-time, making trade-offs on which suppliers receive the most time-sensitive orders.” 

With regard to “understanding plant performance,” companies can leverage big data and data analytics to optimise production and machine efficiency. Sensors on machinery provide invaluable data that offer insight into machine performance which can then be manipulated by operations managers to perform to a specific standard and/or to improve efficiency on the factory floor. They may even help quantify how daily factory performance may impact financial performance down to the very last machine.

Moreover, according to McKinsey, analytics allows companies to carry out predictive maintenance of machinery using historical performance data of machines which can limit the time machine is out of service. “Predictive maintenance typically reduces machine downtime by 30 to 50 percent and increases machine life by 20 to 40 percent.” At the same time, the use of advanced analytics can allow manufactures greater flexibility when it comes to build-to-order configurations of products while reducing costs associated with complex manufacturing.

Preparing For Big Data Integration

The World Economic Forum writes “Most companies recognize data and analytics are rapidly changing the way they manufacture goods. Among survey participants, 81% say they have implemented at least one data and analytics use case, and 72% say that the importance has increased over the past three years.” 

In order to properly maximise big data and data analytics utilities, companies must, first, focus on a specific value when applying big data and data analytics. Second, the proper information technology and operations technology infrastructure must be in place. Third, companies must ensure organisational readiness as well as employee capabilities are sufficient to be able to properly exploit big data and data analytics. 

Harvard Business Review adds data collection and data fusion capabilities are another important aspect to develop and consider. Vast troves of data are necessary to optimise big data and data analytics. Moreover, data protection and data privacy are important aspects necessary to prevent the rampant misuse of and collection of data.

Conclusion

Big data and data analytics, alongside other Industry 4.0 technological innovations, have the potential to revolutionise business operations and manufacturing, more specifically, in the near future.

From improved supply chain visibility, greater production efficiency, to the introduction of predictive maintenance practices. However, companies must be prepared to adapt to these new changes before they are able to maximise benefits it may gain.