Rajdeep Biswas Podcast Transcript
Rajdeep Biswas joins host Brian Thomas on The Digital Executive Podcast.
Welcome to Coruzant Technologies, home of The Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Rajdeep Biswas. Rajdeep Biswas is a visionary technology executive with two decades of experience in transforming global industrial operations through cutting edge AI, data analytics, and IoT solutions.
As the global vice president of industry solutions at Neudesic, An IBM company, Rajdeep has spearheaded digital transformation initiatives that drive efficiency, sustainability, and innovation across the core industrial sectors. His leadership in developing platforms such as SmartMaint IQ, a predictive maintenance solution integrating IIoT and AI, has empowered companies to monitor machine health in real time, reduce downtime, and optimize resource usage.
Rajdeep’s work has had a profound global impact, earning him recognition as a thought leader and influencer in the AI and industrial automation space. He has been a key driver of initiatives that shape the future of smarter factories and data-driven industrial ecosystems worldwide.
Well, good afternoon, Raj. Welcome to the show!
Rajdeep Biswas: Good afternoon, Brian. Thank you for the warm welcome. It’s an absolute pleasure to be here and I’m excited to discuss the evolving landscape of industrial operations and how AI analytics and IAOT are transforming the core industrial operations. Looking forward to a great conversation.
Brian Thomas: Absolutely. Appreciate you being on the show again. We’re going to jump right into your first question here, Raj. You’ve been at the forefront of transforming industrial operations through AI, data analytics and IOT. How has the integration of these technologies evolved over the years? And what do you see as the next big leap in industrial digitalization?
Rajdeep Biswas: Great question, Brian. The integration of AI data analytics and IOT in industrial operations has evolved significantly over the years. In the early stages, technology primarily focused on data collection and digitization. However, today we see a much more sophisticated use of AI, especially in predictive analytics.
In fact, predictive analytics is now the top AI use case across manufacturing, accounting for 29 percent of AI implementations. Targeting machinery and production asset maintenance. The economic impact is also substantial. For example, the manufacturing sector loses approximately 50 billion annually due to unplanned downtime.
With the integration of AI powered solutions like SmartMaint IQ and predictive maintenance, manufacturers can proactively manage machinery health, significantly reducing downtime and boosting overall efficiency. For every dollar invested in AI, companies are realizing an average return of 3. 5, and it typically takes only about 14 months for organizations to see a return on their AI investments.
Looking forward, the next big leap is towards autonomous systems and digital twins. Hundreds of millions of digital twins will soon represent physical assets, enabling real time data exchange and decision making. These autonomous systems will not only self-optimize, but also integrate with broader production ecosystems to manage everything from supply chains to energy consumption autonomously.
Brian Thomas: Thank you so much. I appreciate that and I love being just deep immersively invested in a lot of these technologies and as you know, being in technology, I’ve been a CIO for many years. Being an operations downtime is no fun at all. And so, I appreciate you working on that smart mate IQ platform that we’re going to talk about here just right now.
So, Raj SmartMaint IQ has been a game changer for predictive maintenance. Can you walk us through how it works and the key benefit it provides to industries in terms of reducing downtime and optimizing resource usage?
Rajdeep Biswas: Absolutely. SmartMaint IQ is designed to revolutionize predictive maintenance by integrating advanced AI, IAOT, and real time analytics into one single platform.
It monitors machine health using IoT sensors that gather data on temperature, pressure, vibration, and other critical metrics. The AI algorithm then analyzes these data points, identifying anomalies that may indicate potential equipment failure. The predictive approach significantly reduces unplanned downtime, which is a costly challenge for the industry.
For example, reducing unplanned downtime by 30 to 50 percent for a 1 million production line can save potentially 300,000 to 500,000 per year. Additionally, SmartMaint IQ extends equipment life by 20 to 40%, reducing replacement costs. $500,000 machine, for instance, could see cost savings of approximately 150,000 with extended life.
The platform also decreases maintenance costs by 10 to 25%, resulting in savings of 100 to 250, 000 annually for plants spending, let’s say, 1 million on maintenance. Moreover, by optimizing energy usage, it can reduce energy consumption by 10 to 15%. In translating into dollar savings, that can become 200 to 300, 000 for plants with 2 million energy expenditures.
SmartMaint IQ’s real time monitoring capability also drives overall equipment effectiveness, OWE, by 5 to 10%. For a plant producing 50 million worth of goods annually, this can result in an additional 2. 5 million to 5 million in output. It’s a comprehensive solution that transforms how industries manage assets, reducing costs, while enhancing efficiency and sustainability.
Brian Thomas: That’s awesome. You know, the part that attracts me, of course, is the unplanned downtime, how we can better proactively monitor those things and prevent those things, but the added benefit, as you mentioned, is cost savings and energy savings. So, I appreciate that. It really, really do. And that’s awesome.
Raj, digital transformation is a buzzword in many sectors. You and I both know that, right? But the manufacturing industry has a specific challenge. What are some of the common obstacles you’ve encountered in driving digital transformation for your clients and how do you overcome them?
Rajdeep Biswas: Great question.
Again, digital transformation in manufacturing comes with several unique challenges. Primarily allow legacy infrastructure and talent shortages. Many manufacturing facilities operate with equipment that has not been designed for connectivity, making it very difficult to integrate modern technologies.
Retrofitting machines to collect and transmit data through IOT sensors requires careful planning and investment. But it’s essential for enabling transformative IOT solutions. That which drives a significant ROI. Another significant challenge is the talent gap. Currently around 60 percent of skilled production positions remain unfilled.
To address this, we focus on developing intuitive AI tools and platforms. That are easy to adopt and use. For example, SmartMaint IQ’s interface is designed for ease of use, allowing operators to interact with systems without requiring extensive training in data analytics or AI. Cultural resistance is another challenge.
The shift from traditional to digital workflows requires a change in mindset. Which we address through upscaling initiatives and demonstrating the clear ROI of digital transformation. For instance, when companies see the direct impact of AI, like a 300, 000 reduction in downtime on a 1 million production line, they’re more likely to embrace these changes.
Lastly, cybersecurity concerns. I hope you will appreciate it as a former CIO are always top of mind by leveraging Microsoft Azure security features. We ensure that our platforms like SmartMaint IQ are compliant with industry standards, protecting sensitive operational data and giving clients the confidence to adopt these technologies.
Brian Thomas: Thank you. I appreciate that. And having a manufacturing perspective on this around digital transformation is so important. And we all know this, that there’s a lot of obstacles. And the big one typically is communication, right? The education and understanding the why and help people get that buy in.
So, you can move them from a No to a Yes. So, I appreciate you really breaking that down. Raj, last question of the evening, as a global recognized thought leader, you’ve advocated for AI’s role in enabling smarter manufacturing, what do you think is the most underutilized or misunderstood aspect of AI in industrial operations today?
Rajdeep Biswas: One of the most underutilized aspects of AI in manufacturing is its capability for dynamic and adaptive problem solving. Thank you. Particularly through generative AI, many organizations still see AI as a tool limited to predictive analytics or automation of basic tasks, but it’s far more transformative by leveraging generative AI.
Companies can create highly intelligent systems that operate in a more intuitive human-like manner, enhancing efficiency, agility and adaptability. Thank you. Traditional manufacturing applications often rely on hard coded rules and fixed datasets, making them costly and time consuming to change. However, generative AI shifts this paradigm, enabling applications that can interact through natural language and adopt based on real time data.
This transition transforms existing apps into intelligent platforms that provide personalized data driven experiences, continuously improving over time. For instance, rather than manually configuring a system when a new product line is introduced, Generative AI allows operators to interact with the system in natural language to quickly implement the change, saving both time and resources.
In the context of SmartMaint IQ, AI tools like Copilot can have a profound impact on various roles and functions. In this context, Copilot is an experience using generative AI to assist humans with complex cognitive tasks. For maintenance supervisors, for example, Copilot can quickly generate summaries of Open work orders identify areas of predictive risk and offer actionable recommendations for performance improvements.
This minimizes downtime and optimizes resource allocation. In R& D, for example, Copilot enhances productivity by accelerating document exploration and intelligent design guidance, enabling teams to quickly create IP documentation and refine product designs. For marketing and sales. It identifies market trends and generates sales content sweetly, while in supply chain management, it accelerates contract analysis and provides insights into supply risks.
This reduces downtime and ensures that organizations stay agile. In response to dynamic market conditions, the power of copilot extends into operational efficiencies as well. For example, it can assist quality assurance teams by summarizing top quality management issues and interacting with quality inspection images and videos in real time.
Enabling proactive root cause analysis and quality improvements by embedding AI tools like copilot into industrial systems, manufacturers can achieve seamless data driven operations that enhance both productivity and sustainability. With copilot’s capabilities tailored to specific business units, and that’s a key point, whether it’s enhancing customer interactions for sales teams, optimizing supply chain or improving product quality through intelligent operational insights.
The manufacturing industry can fully harness the potential of AI to build smarter, more responsive factories that drive innovation and competitiveness on a global scale.
Brian Thomas: Thank you very much, Raj. I appreciate that. I really do. And, you know, the power of gender of AI, you know, you, you highlighted a few examples in there, but the one that I really like is when there is a change within the production line and manufacturing, for example, when you have a partner, a machine partner.
You can actually make those communications human to machine and natural language, especially if there are some new instructions. And we’ve seen that. And I’ve talked to others on podcasts about manufacturing and the way they can actually train up employees with two changes or actually new employees coming onto the production line.
So, I really appreciate you highlighting some of the positive impacts to manufacturing with generative AI. And I think that’s just awesome. And Raj, it was such a pleasure having you on today. And I look forward to speaking with you real soon.
Rajdeep Biswas: Thank you for having me, Brian. It was a pleasure to share insights into the transformative power of AI and digital technologies in the manufacturing industry that is driving the future of smarter, more efficient, and most importantly, sustainable industrial operations. Thanks so much.
Brian Thomas: Bye for now.
Rajdeep Biswas Podcast Transcript. Listen to the audio on the guest’s podcast page.
