In this podcast, Vivek Gupta – Joint Vice President, DCM Shriram Limited and Sunil – Founder & CEO, Nanoprecise Sci Corp, talk about Scaling AI+IoT Projects in Process Industries. Mr. Gupta shares his thoughts and experiences about going from Pilot to Scale with the AI-based Predictive Maintenance System from Nanoprecise along with some key learnings from the Pilot implementation. He talks about navigating common challenges such as knowing where to begin and moving from one application to multiple applications within a short time span.
The podcast begins with the host – Steve Dobie introducing Vivek Gupta, and Mr. Gupta talking about his journey towards digitizing the manufacturing operations at DCM Shriram Limited. Mr. Gupta is a joint VP of Instrumentation & Digital Journey for a 57-year-old manufacturing plant of DCM Shriram Limited, which is a 125-year-old company in India. He discusses why technology adoption is an important driver for improving the competitiveness of manufacturing processes. He notes that with the ongoing pandemic as well as the push to increase the operational capabilities of the manufacturing equipment, maintenance teams are under a lot of pressure to do more with less. This makes it essential to move away from a Run to Failure Maintenance practice to a Reliability Centered Maintenance approach where Predictive Maintenance can be a significant piece of the picture.
In the podcast, Mr. Gupta talks about the main reasons for choosing Nanoprecise solutions for pilot and subsequently for scaling across their plants. As the lead for the digitalization journey, he talks about challenges faced in the initial stages such as increased resistance from stakeholders to the adoption of new technology and details the outcome of successful rounds of discussions that transpired into buy-in from stakeholders for the successful implementation of the solution.
Mr. Gupta emphasizes the importance of monitoring machine health by comparing it with the health of an individual. Just like how frequent medical checkups and monitoring for people individuals can detect issues early, the health of a machine needs to be monitored continuously so that changes can be observed and actions can be planned to achieve improvements in performance and assure longer life.
Mr. Gupta also talks about the difference between Wired & Wireless Sensors by pointing out that wireless sensors have superior advantages over the wired sensors in a number of key aspects, and they offer improved capabilities for full-time machine health monitoring. However, it was pointed out by Mr. Vedula that an organization needs to determine the correct connectivity protocol for the application when it comes to wireless sensors. This is because having fulltime connectivity is not the goal. It is not about the sensors sending too much or too little data, but capturing and analyzing the data that can provide meaningful insights on the performance of the machines. Advanced solutions have the capability to predict failures long before they happen.
Mr. Gupta hints at a new area of application with MachineDoctorTM sensors, and is working with the Nanoprecise team to determine the feasibility of the AI-based Predictive Maintenance Solution in the transportation sector.
The podcast was concluded by agreeing that Digital transformation is a journey in itself that is driven by the right people, process and technology. While wireless solutions provide a higher factor of reliability to overall operations, it was noted that the maintenance teams need to be onboarded from the beginning as they are the critical elements responsible for the optimal functioning of machines, as they are the key players working to save costs, prevent downtime and ultimately improve the bottom-line of the business. The technology when implemented appropriately, has the potential to pave the way for co-invention opportunity for the organization as well as the solution provider.
Tune in to know more about the advice and learnings from the Digital Transformation Head of a multi-billion-dollar company, on Scaling up an AI-based Predictive Maintenance System.