Data Analytics, IoT and its Successful Experimentations


According to Dipankar Ghosh is Founder-CEO, Digi2O, Internet of things (IoT) emerged from two key ingredients, imagination and extensive experimentation. In this article, Dipankar explains how IoT and data analytics can benefit industries and business in real time while minimizing risks and maximizing benefits in terms of its unexplored potentials.

IOT has been the talk of the town in this decade. It is a term that almost everyone has heard about although everyone still keeps on guessing when asked about. To really get machines connected is a challenge but decision to adopt this is something beyond it. Manufacturers across the globe shifting are still in the process to adopt intelligence so that their business scale up.

So as to speak, today there still exist equipment owners who may not have put an effort to get their machines ‘connected’. Today it is possible says Dipankar, for the buyers and users of these machines to retrofit solutions that help them achieve this. For equipment owners, this will create a major impact on their scalability, both technically and in operations.

Dipankar Ghosh, Founder-CEO, Digi2O talk about data analytics
Dipankar Ghosh, Founder-CEO, Digi2O

The Data Analytics part of the story

Data analytics as per its definition is the process of examining datasets and analyzing the information they have to make decisions or get to a conclusion. In machine world, sensors are the source of this data. The sense elements such as temperature variations, changes in inputs and output in a machine etc. to mention a few. This way machines generate a lot of data and due to working with a large commercial manufacture floor, problems are expected too.
Industrial Internet of Things (IoT). Internet of things was primarily devised to solve certain problems that arose; trying to find fault which sounded good in terms of less human intervention and hazard. Traditionally, this job was given to data  analytics engine and software to analyze data using existing instrumentation models. They helped engineers decide the best improvement programs by analyzing existing models in plans based on the gravity of the problem back in those days.

Today the system has been made aware to be conscious about anomalies and predict even before they occur. Enter predictive analysis in the picture and engineers were able to go through instances of situations based on the analytics and data received from these machines. For example, in some cases, certain benchmarks such as maximum, temperature, greasing etc. in a machine determines its efficiency and life. So, now how much of all of these can these machines take has to be predicted using empirical and statistical point of view? The inferences must match the most economical way while minimizing the number of experiments.

Perhaps the point to note here is evolving to a better design, value engineering, perhaps finding/ replacing with long-lasting material which could match the manufacturers and customers benchmarks.

More valuable than oil and diamonds

Data is the new oil, the more your mine the more insights in improving the existing processes we get. “ For example, if an industrialist wants to increase uptime and production what he can do is to improve machines maintenance, set operations efficiency parameters, and provide skill training for operators. As a result, manufacturing cycle time improves and production along with product improves. This part is known to everyone but IoT adoption is still a challenge and we are yet to assess its full-scale potential ”, says Dipankar.

Are you motivated to adopt IoT?

There are several reasons as to why business must adopt the connected aspect. The first question is a gamble to understand if it is good enough to standalone machines with specific tasks on a factory floor going forward.

With a positive response on this, IoT based connected platforms have shown us that they can drastically reduce costs at both capital and operations. Cost reduction indeed is an important factor in improving the bottom line of a business.

Generic challenges

From a Technical point of view Industrial Internet of Things (IoT). is at the breaking the shell and coming out of its initial state of R&D. This starts a new phase of experimentation once again.
“We have sensors in place and we have analytic engines giving us a suggestion and making decisions. The new phase comes into play as every machine is different. For example, the motor has vibration, temperature, and lubrication on bearing which are understood. How to predict the shaft life might be a challenge for which Artificial Intelligence (AI) could be incorporated”, Dipankar Ghosh.

Wait, there is more!

This is an opportunity area. Companies today have data on all the end to end activates including shipment details. After service and future traceability aspects like the warranty on the shaft, equipment owner overload (which is subjective) based on usage could be tracked in real-time with the Internet of Things (IoT). If required the service person can access this data, track and analyze the information for quick predictive root cause analysis. This would be of much value to the customer and gives everyone a better understanding of prevention of failure in the future. Additionally, material problems and inspection faults could be traced back to analyze and improve. Interestingly, it is all up to the human imagination and using it to improve. Imagination is the key to getting more from the Internet of Things (IoT).

For more such interesting data analytics stories, read more.


Article by Shanosh Kumar, Technology Journalist at EFY


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