Data as a Connected Vehicle for Manufacturing

Big Data has become a big game-changer in most industries over the last few years. However, using big data in manufacturing increases transparency in each level of different manufacturing processes and operation.

IBM states that 53% of manufacturers apply Big Data and Data Analytics to create a competitive advantage for their organizations. In manufacturing, however, the biggest value of Big Data is that it can not only forecast problems using data but also actually solve them.

The Question always lies in understanding where to actually start, when it comes to taking benefits from Data that either exists or what’s needed?

Manufacturing has changed, especially in the COVID World, the push has now come more than ever to understand “What Data is Telling You”? In the time of the pandemic, Big Data has become even more critical. Experts from Gartner claim that the global financial impact of the pandemic on the economy will vary between $2T and $4.5T. No wonder that cost-reduction is manufacturers’ top priority these days.

There has always been a feeling among big, mid or small enterprises that Data is something which is available and we know it well.

In my experience of working with various customers, although the peripheral problem statements that is presented may look different, but deeper inside, I see that most data problems within enterprises are more or less similar.

The question lies where to really start? Some initial questions that always helps to uncover some potential outlines, would be:

• What Data is Available?
• Is the Data Standardized?
• How are the Data being collected?
• What kind of Data are being Collected?
• Reasons for not exploring other avenues of Data? Or Reasons for Sticking to a fixed pattern?
• Is there a Fixed Pattern?
• How does all of the data fit into the overall Manufacturing Scheme of things?

One of the patterns noticed, is that people are always fascinated about buzzwords and tend to run behind exploring different technologies first and then try to fit them backwards into making an attempt towards Custom Fit or even in most times a Forced Fit.

So when one thinks of IoT, Machine Learning, AI, Blockchain, ARVR, Quantum Computing, Cloud, 5G, etc. if we only look at these in terms of Siloed buckets, the outcomes may not yield what is originally anticipated.

Technologies are Converging and Data seems to be the fulcrum behind these emerging phenomena.

• IoT – Real Time Data Generation
• Machine Learning – Data Learnings from Both Historical and Real Time Data
• AI – Automated Data Outcome extended from ML
• Blockchain – Data Ecosystem imagined from a Distributed Viewpoint. Imagine machines functioning as independent nodes?
• DLTs augmented with IoT, ML & Decentral AI creates unique business models for Machine Economy
• AR/VR – Data Visualization making the experience Immersive
• Quantum Computing – Data Processing imagined with greater compute and speed
• 5G – Internet and Data driven Networking
• Cloud – Data Infrastructure

With what we see above, Data forms the pivot and to address real time machine and manufacturing business problems, thinking from a Data perspective would help in terms of creating a Data Topology that can then outline the usage of various tech stacks and thereby creating a Converged Ecosystem of a data driven manufacturing.

If we look at Industry 4.0, Digital Twins, Autonomous Machines, none of it can be possible following a specific technology bias. So why can’t we transform ourselves from being Tech Biased to Data driven approach?

If you find this useful, we will be happy to chat about how we can create a Data Topology for you.

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