On Transforming Manufacturing with IoT and Real-Time Data. Interview with Dheeraj Remella.

by Roberto V. Zicari on April 22, 2025

“We’re also seeing that the latency involved in going to the cloud is becoming a nuisance and there is a lot of movement to bring intelligence to the edge. In this case, edge doesn’t mean “on the device” because that becomes too narrow a context. The edge is a compute location close to all the components and systems in a process.”

Q1. How has the integration of IoT sensors and real-time data processing transformed traditional manufacturing processes since 2020?

Dheeraj Remella: Since 2020, we’ve seen a significant shift. Manufacturers are moving from reactive to proactive operations. Some of the examples are:

Predictive maintenance is becoming mainstream
AI-driven decision-making using ML models for real-time inference
Ubiquitous connectivity through 5G, eSIM, and LoRaWAN has given rise to more data being collected, giving manufacturers immediate visibility into the current state of assets and warehouse parts for faster issue resolution.
By proactively managing assets and operations, organizations are enjoying energy savings of around 30%
Digital Twins are being used in the “industrial metaverse” to ensure that a change would have the intended effect before it is implemented in the real world – i.e. the physical twin world.

We’re also seeing that the latency involved in going to the cloud is becoming a nuisance and there is a lot of movement to bring intelligence to the edge. In this case, edge doesn’t mean “on the device” because that becomes too narrow a context. The edge is a compute location close to all the components and systems in a process. So, the organizations need to go beyond the traditional computation of just the equipment efficiency and instead focus on the more meaningful process efficiency.

Q2. What are the key challenges in managing and analyzing the massive scale of IoT data in real-time for smart manufacturing applications?

Dheeraj Remella: Typically, organizations buy software that has its own data storage, which creates silos between processes and departments. These silos create the “Curse of Babel” problem, where every department and software has its own nomenclature and representation. The lack of a commonly accepted and understood ontology creates a significant bump to overcome before true innovation can be achieved. Additionally, the sheer volume, velocity, and variety of IoT data present significant hurdles. Traditional systems struggle to keep up.

Q3. How are edge computing and cloud-based analytics being leveraged to address latency issues in real-time data processing for manufacturing environments?

Dheeraj Remella: Data value is primarily determined by how fresh it is. Younger data is suitable for faster contextual decisions, while older data is better used as a part of a collective for extracting learnings from the system behavior. Naturally, the younger data needs to be acted upon at the edge and the older data should be collected in the cloud for economies of scale for the non-time-sensitive machine learning initiatives.

In addition, edge computing gives the ability to incorporate various secondary concerns into the data processing:

Security
Sustainability
Sovereignty
Sessionization and aggregation
Data thinning to send less and just the relevant data to the cloud.
Digital twins being activated beyond being just data stores allows intelligent participation in the physical processes
Elimination of redundant or insignificant ephemeral data

Q4. Can you discuss the role of machine learning algorithms in extracting actionable insights from real-time IoT sensor data in smart factories?

Dheeraj Remella: In a fast-changing, hyperconnected industrial world, machine learning is critical. Once the most appropriate algorithm has been selected, they need to be fed the more recent data continuously to ensure an adaptive approach of the model evolution. This rapidly evolving model can then be fed into the real-time compute layer to make better decisions, decreasing false positives/negatives. These models can:

Detect anomalies in production processes that would otherwise go unnoticed.
Predict maintenance needs before failures occur, minimizing downtime.
Optimize resource allocation and production schedules for peak efficiency.

The beauty of this continuous feedback loop of observe, orient, decide and act (OODA) is that these algorithms continuously learn and improve, boosting the overall efficiency of smart factories. We bring these machine-learning insights directly to bear on real-time data and decisions.

Q5. How are manufacturers balancing the need for real-time data processing with data security and privacy concerns, especially when dealing with sensitive industrial information?

Dheeraj Remella: This is a top priority. Manufacturers are employing several strategies:

Implementing strong encryption and authentication measures. Post Quantum Encryption is becoming an increasingly interesting discussion, albeit nascent.
Utilizing edge computing to keep sensitive data local, reducing exposure.
Developing comprehensive data governance policies.
Thinking of security and privacy at the initial design.
Employee training.
Role-based access control of the data for both personnel and systems.

Q6. What advancements in data integration technologies are enabling manufacturers to combine real-time IoT data with existing business systems for more comprehensive insights?

Dheeraj Remella: Integration is key. There is an adjacent need as well, and that is interoperability.

We’re seeing advancements like:

Custom APIs and middleware solutions bridging legacy and modern systems.
Standardized data formats facilitating a homogeneous target data model.
Unified Namespace architectured de-siloing data between various IT and OT systems.
IoT platforms are evolving to support diverse data sources and protocols, both old and modern.
Event-driven Unified Real-time Data Platforms that make cognitive decisions powered by machine learning models on the streaming IoT data combined with near-past context.

Q7. How is real-time data processing improving predictive maintenance capabilities in smart manufacturing, and what impact is this having on reducing downtime and optimizing asset utilization?

Dheeraj Remella: The combination of real-time data and smart use of machine learning is revolutionizing maintenance. Maturation in predictive maintenance is allowing organizations to move away from scheduled maintenance, where maintenance activity is done even though the health has not deteriorated. This proactive approach is drastically reducing downtime and optimizing asset utilization across industries. This shift into need-based predictive maintenance also improves the sustainability stance and narrative at these organizations.

Q8. Can you explain the concept of “digital twins” in manufacturing and how real-time data processing is enhancing their effectiveness?

Dheeraj Remella: Usually you would find digital twins to be pure state stores that record the current state of the physical twin. This approach is quite useful in simulation and industrial metaverse-type scenarios. But, increasing maturity at manufacturing organizations is demanding digital twins to also account for the physical twins’ behavior. More often than not, there is an interest in augmenting the behavior with predictive ML models. Now, they are active participants in day-to-day operations with bi-directional data and control flow.

Real-time data processing is enhancing their effectiveness by:

Providing up-to-the-second information on asset performance.
Enabling simulation of different scenarios for optimization.
Facilitating predictive maintenance and process improvements.
Invoking actions through actuators/controllers to complete the real-time sense-control loop.

But, to make these usage patterns genuinely effective and impactful, there has to be a paradigm shift to start thinking in terms of edge computing, low latency roundtrips, data, and decision immediacy.

Q9. What strategies are being employed to ensure the scalability and reliability of real-time data processing systems as the number of IoT sensors in manufacturing environments continues to grow?

Dheeraj Remella: Increasing sensors and systems and the mounting need for intelligent automation push the narrative to the edge. While the device edge is too narrow of a context, there is a near-edge tier of computing that has the full system and process content. While there are technologies that can scale to the entire data velocity and quantity without compromising on reliability or resilience, manufacturers have to look into localized computing at the edge so that they are also addressing the timeliness and latency sensitivity of decision and response automation. Manufacturers are:

Adopting cloud and edge computing architectures for flexible scaling.
Implementing robust data management strategies.
Utilizing distributed processing techniques to handle increasing data volumes.
Data platforms that minimize latency and keep infrastructure needs manageable for environments that do not have the luxury of unlimited hardware.

Q10. How do you see the role of AI and machine learning evolving in augmenting real-time data processing for smart manufacturing by 2026, and what new capabilities might this enable?

Dheeraj Remella: By 2026, AI and machine learning will be even more deeply integrated:

Advanced AI models will enable more autonomous decision-making in production processes with better false-positive and false-negative recognition.
Machine learning algorithms will become more sophisticated in predictive analytics and optimization.
Machine learning would move to the edge as well to take advantage of streaming retraining and complete the learn-predict-act cycles, all at the edge.
We may see the emergence of self-optimizing production lines that can adapt in real time to changing conditions.
We may even see a degree of democratization of data, decisions and automation to use natural language interaction and management of systems.

These advancements will lead to unprecedented efficiency, quality control, and responsiveness. With the increasing accuracy in manufacturing, enterprises can enjoy much better waste, power, and equipment management, thus having a better sustainability orientation.

Q11. In what applications do Volt Active Data’s customers utilize real-time data processing in the manufacturing sector?

Dheeraj Remella: Volt Active Data’s customers commonly use it for:

Predictive Maintenance
Real-Time Quality Control
Adaptive Production Optimization
Supply Chain Monitoring and Optimization
Real-Time Asset Tracking
Combining sensors and video data through computer vision for complete observation

Q12. Is Volt Active Data a possible solution for the challenges posed by IoT and machine learning at scale in manufacturing?

Dheeraj Remella: Volt Active Data is designed to meet 4 key requirements without compromising on availability and resiliency:

Scale (Number of things)
Speed (the rate at which the things generate data)
Low latency (How quickly do you need to act on that data)
Data-Decision Accuracy (How accurate does the correlation data need to be compared to eventually consistent systems)

Often, the decisions would need to be augmented with machine learning model inference in real-time in an event-driven manner. Volt Active Data was built from the ground up to address these requirements at any scale with predictable latency SLAs. Our customers rely on us to make sure their systems don’t miss SLAs or lose data, and also ensure that they can integrate with the appropriate downstream and upstream systems in the most efficient way.

Q13. In your opinion, what are Volt’s three most successful manufacturing use cases?

Dheeraj Remella: The top three applications for Volt Active Data are:

Predictive Maintenance
Real-time quality Control for early detection of defects
Adaptive Production Optimization

There are other applications that can adopt Volt’s values as well, such as reducing the non-productive time of assets by continuous monitoring of the asset conditions, pre-emptive ordering of required spare parts before they are needed, identifying the closest technician that can run the maintenance of the asset and the closest warehouse with all the parts required for the maintenance.

These use cases require the data layer to address:

Scale, with the ability to handle billions of events per day.
High performance, with the ability to process hundreds of thousands of events per second.
Low latency, where the moment of engagement is in single-digit milliseconds.
The ability to handle complex data.
The ability to make complex decisions on streaming data.
Immediate consistency and accuracy.
No data loss.
Geographic distribution.

……………………………………………………..

Dheeraj Remella is the Chief Product Officer at Volt Active Data, responsible for technical OEM partnerships and enabling customers to take their next step in data-driven decision-making. Dheeraj has been instrumental in significant customer acquisitions and brings 30 years of experience in creating Enterprise solutions in a variety of industries. Dheeraj is a strong believer in the cross-pollination of ideas and innovation between industries and technologies. Dheeraj holds a bachelor’s degree in computer engineering from Madras University.

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