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Industry 4.0 & Big Data Analytics

In our final article in Industry 4.0 series we explore the importance of one of the biggest driving forces behind this great paradigm shift in manufacturing, Big Data Analytics

(PresseBox) (Porto, ) With all the frenzy around Industry 4.0 it is important for manufacturers to understand that the new revolution is a revolution rooted in information. The ability to harness information from large amounts of data being generated from all parts of the operation, then turn it into value adding action items and organizational knowledge, leading to both improvement and innovation.

In our final article in Industry 4.0 series we will explore the importance of one of the biggest driving forces behind this great paradigm shift in manufacturing, Big Data Analytics.

Yes, Big Data has long been a buzzword in boardrooms of many global organizations, however, they key here is to explore its interplay with the other driving forces behind industry 4.0. Let’s look at the bigger picture for a moment before we zoom into Big Data.

Industry 4.0 is being brought about by the abundance of data, so how does this abundant data come into being? We have previously explored the sort of cyber-physical worlds evolving in most modern manufacturing operations, where phenomenon like augmented reality, Industrial Internet of Things, Cloud based software applications and Data storage is allowing more and more data to be generated.

These technological advances are allowing the previously inanimate objects like materials and equipment to have a voice, making them capable of not only interacting with human controllers but also allowing them to interact with themselves, internet provides a medium, cheaper electronics like sensors, RFID chips etc. provide the voice. Because of this profound change in manufacturing plants, the volume of data generated from the same plants have also increased drastically.

Now, when Big Data comes into the picture, all the data being generated and meticulously stored in the cloud will add no value to the operation, unless it could be examined and used to generate meaningful, actionable information. Most high tech MES applications which govern the manufacturing process in modern plants, are equipped with the capability to harness information from the large volume data being generated not only through the manufacturing process but also at the customer end and being reported or captured either through the internet or the ERP application of the organization.

Big Data Analytics in practice

What Big Data Analytics does is find trends, patterns and possible deductions from seemingly similar kind of data being generated. It impacts all aspects of operation:

  • plant productivity, say by examining the minute differences between data from two robots with exact specifications, but with one being slightly less efficient than the other,
  • production Quality, say by allowing for part-per-million defect data to be collated and summarized to point out a design flow in one of the new products being manufactured,
  • Time to market, by allowing errors or issues to be detected, even before the first negative feedback can be received from the customer or by allowing a product to become commercially viable by highlighting exactly what needs to improve right at the design stage.
This kind of ability to use massive amounts of data to trigger improvements and innovate is unprecedented and is only possible if all the factors which influence an industry 4.0 type environment are present especially Big Data Analytics.

Depending on how proficient the Big Data Analytics tool being offered by the MES or Software vendor, the analytics tool also provides other value adding benefits, such as being able to calculate and predict maintenance activity for machinery and allow better planning and orchestration of the process.

With the right Big-Data tool, it would be possible for containment plans to be implemented with minimal loss when an OOS incident occurs. Also, the data generated from the said OOS event will help prepare the future preventive actions and allow for other OOS events to be predicted beforehand, thereby saving the cost related with rework and waste.

Imagine a scenario where third party maintenance can be triggered from the MES software direct based on the analysis and machines specifications provided, it would save thousands of man-hours, spent in analyzing the machine states and help maintenance become a proactive and need based activity. And this is just one of the many wonders Big-Data Analytics can bring about when applied in the Industry 4.0 scenario.

Prepare for Data flood

It is critical that one understands that for Big-Data to be effective the cyber-physical world needs to be prepared and generating data, the process software needs to be capable of using the millions and millions of bytes of data being generated 24X7 from various production plants, R&D labs, Point of Sales/Customer, suppliers, dealers and so on. Only then does having the Big Data Analytics tool makes business sense.

To sum it up, Industry 4.0 may seem like a Pandora’s Box but would prove to be the exact opposite if approached with a clear understanding and by employing the right tools/vendors/technical experts, it will truly be a game changer. Stay tuned for more on this and Cyber-Physical systems which are becoming more and more prominent in the world of manufacturing even as you read this!!

Critical Manufacturing Deutschland GmbH

Critical Manufacturing empowers high performance operations for some of the most advanced manufacturers worldwide with innovative software technology and advanced services. Its new generation Manufacturing Execution System (MES) is an Industry 4.0 centerpiece, incorporating all necessary integration, mobile, connectivity and logical decentralization features. This deep, unified system increases performance, control and quality for complex manufacturing operations. The company is part of the Critical Group, a private group of companies founded in 1998 to provide solutions for mission and business critical information systems.

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