Hitachi Digital Services: Scaling the IIoT Data Backbone

While talk of smart factories is everywhere, few have yet found success in scaling these technologies.

To achieve this growth, manufacturers need to do more than just connect machines. 

Ganesh Bukka is Vice President & Global Head Industry 4.0 at Hitachi Digital Services and has cracked the code on moving from experiments to rollout.

He shares his expertise with Manufacturing Digital. 

See the full story in the April 2026 edition of Manufacturing Digital.

How do you define a smart factory today?

A smart factory today digitalises the core of a traditional manufacturing environment by unifying the edge-to-cloud journey.

Unlike conventional setups, it leverages real-time data from interconnected machines, systems and processes to optimise every stage of production—from supply chain operations through manufacturing and delivery.

By creating a dynamic and responsive environment, smart factories enable manufacturers to adapt quickly to changing market demands, improve efficiency, reduce costs and consistently ensure product quality.

At its core, a smart factory is an intelligent ecosystem where digital technologies seamlessly connect machines, processes and people to drive continuous improvement and business value.

A strong example of this evolution is our Hagerstown Advanced Rail Digital Factory, where these principles have been put into practice to create a truly cutting-edge smart factory model.

Another example is Hitachi’s Omika Works factory, which was recognised by the World Economic Forum as an Advanced 4th Industrial Revolution Lighthouse.

Where does IIoT fit within this ecosystem?

IIoT sits at the foundation of the smart factory ecosystem. It acts as the connective layer that links machines, sensors, systems and people across the shop floor and enterprise.

Without IIoT, there is no reliable flow of real-time operational data to power analytics, AI models, digital twins or sustainability platforms.

In practical terms, IIoT enables data acquisition from critical assets, supports edge processing for low-latency decision-making and integrates operational data with enterprise systems such as MES, ERP and cloud platforms. It provides the visibility and contextualised data needed for predictive maintenance, quality optimisation, energy management and connected worker solutions.

Ultimately, IIoT serves as the backbone of the smart factory, transforming standalone equipment into intelligent, connected assets and establishing the data foundation for AI, industrial edge computing and Industry 5.0 capabilities.

Initiatives such as the Hagerstown Advanced Rail Digital Factory and Hitachi Omika Works, developed by Hitachi, illustrate how this connectivity translates into real-world operational transformation.

What role do sensors play in the edge-to-cloud journey?

Sensors are the starting point of the edge-to-cloud journey. They serve as the primary data generators within a smart factory, capturing real-time information on machine performance, environmental conditions, product quality, energy consumption and asset health.

Without accurate and reliable sensor data, the rest of the digital ecosystem cannot function effectively.

At the edge, sensor data is processed locally to enable low-latency, mission-critical decisions—such as anomaly detection, quality inspection or safety alerts. This ensures rapid response times and minimises downtime.

As the data moves to the cloud, it is aggregated, contextualised and analysed at scale to power advanced analytics, AI models, digital twins and enterprise-level optimisation.

In essence, sensors are the foundation of the edge-to-cloud architecture—they transform physical operations into digital insights, enabling real-time control at the edge and strategic intelligence in the cloud.

What key success factors have you seen enable smart factory initiatives to scale?

We’ve all seen pilot projects that demonstrated real potential but never scaled beyond individual sites. These experiences have reinforced an important lesson: efficiency and connectivity alone are not enough.

What truly enables smart factory initiatives to scale is a holistic approach—one that brings IT and OT together, prepares and upskills the workforce, strengthens cybersecurity and builds maturity around data, governance and operating models.

We’ve seen this approach deliver real results with global manufacturers, including a large automotive client where we successfully scaled smart factory capabilities across multiple sites, demonstrating what true smart factory impact looks like in action.

See the full story in the April 2026 edition of Manufacturing Digital.

How do you approach retrofitting older assets with IIoT connectivity?

Retrofitting older assets with IIoT connectivity starts with a pragmatic, value-driven mindset.

Because most brownfield environments were never designed for connectivity, the first priority is identifying high-impact use cases—such as predictive maintenance, energy monitoring or quality improvement—where measurable outcomes can be achieved.

From a technical standpoint, this typically involves deploying non-intrusive sensors, edge gateways and protocol converters to capture machine data without interrupting operations.

Edge devices normalise legacy protocols, enable local processing for low-latency decision-making and securely transmit contextualised data to enterprise or cloud platforms for scalable analytics.

Cybersecurity must be embedded from the outset, especially when integrating legacy equipment that lacks modern safeguards.

Equal emphasis should be placed on data contextualisation—mapping assets, structuring tags and integrating with manufacturing and enterprise systems so that captured data becomes actionable rather than merely available.

Ultimately, successful retrofitting is less about connecting everything immediately and more about prioritising value, minimising operational disruption and establishing a scalable pathway into the broader smart factory ecosystem.

How are advanced analytics and AI changing modern factories?

Advanced analytics and AI are transforming modern factories by enabling a more human-centric, resilient and sustainable operating model, while moving intelligence closer to the industrial edge.

Instead of relying solely on centralised systems, manufacturers are increasingly adopting Industrial Edge AI to make real-time, mission-critical decisions at the point of operation—reducing latency, improving accuracy and minimising downtime.

At Hitachi, we have been investing significantly across these areas and our solutions bring this transformation into real-world operations. For example, our Automated Quality Inspection system at the Hagerstown Advanced Rail Digital Factory combines human–AI collaboration with industrial edge technologies to improve quality outcomes and operational efficiency.

We also partnered with a global automotive leader on a Manufacturing Digital Transformation (MDT) programme, implementing a connected factory platform by identifying and integrating critical equipment and sensors to enable predictive and adaptive analytics. In parallel, Rita ONE, our sustainability suite, helps manufacturers embed ESG objectives directly into day-to-day factory operations.

How critical is the underlying IIoT architecture to data streams?

The underlying IIoT architecture is central to the reliability, scalability and business value of smart factory data streams.

While sensors generate data, architecture determines whether that data is secure, contextualised, real-time and actionable. A well-designed foundation enables seamless ingestion from heterogeneous assets, normalises legacy and modern protocols, supports edge processing for low-latency decisions and integrates smoothly with cloud platforms and enterprise systems such as MES and ERP. It also embeds cybersecurity, governance and scalability by design, ensuring consistency as deployments expand across plants.

This was evident in an MDT programme delivered for a global automotive leader, where a robust IIoT architecture enabled standardised data models, scalable analytics and measurable operational improvements across multiple facilities.

Could you share a few success stories from smart factory implementations?

We’ve delivered several impactful smart factory implementations across industries.

For a global FMCG client, we deployed an AR- and ML-based solution that significantly improved frontline worker productivity and execution efficiency.

At our Hagerstown Advanced Rail Digital Factory, we have driven IT–OT–AI innovation using GenAI-powered quality inspection, computer vision, the Spot robot and industrial metaverse capabilities, creating a highly advanced digital manufacturing environment.

We have also helped clients advance their sustainability goals through Rita ONE, our ESG and sustainability reporting solution. In another engagement with a multinational tyre manufacturer, we leveraged advanced AI/ML models to implement advanced process control, enabling the prediction of critical quality metrics and improved process stability.

Additionally, we have delivered Digital Twin–based factory simulations using the NVIDIA Omniverse platform, enabling manufacturers to model, optimise and de-risk factory operations before deploying changes in the physical environment.

See the full story in the April 2026 edition of Manufacturing Digital.