Hitachi Digital Services: Evangelising AI in Manufacturing
Ganesh Bukka brings nearly three decades of experience in the manufacturing and asset management sectors to his role as Vice President of Hitachi Digital Services (HDS).
Working with IT and OT systems across industries such as energy, utilities, transportation and discrete and process manufacturing, he applies this experience at HDS to integrating its deep legacy in OT systems with modern IT practices.
Ganesh guides digital transformation initiatives by leveraging his consulting expertise and Hitachi’s rich OT history, helping to enhance value through digital solutions.
Today his role frequently centers on the possibilities of one particular technology: AI.
Here, Ganesh shares how HDS is helping manufacturers to harness it to drive profound operational and organisational change.
How is HDS advancing manufacturing through AI?
We’re leading the charge in evangelising AI within the manufacturing landscape, identifying core use cases that drive improvements in Cost of Poor Quality (COPQ), Overall Equipment Efficiency (OEE), and Integrated Supply Chain Management.
One significant example is Hitachi Rail’s advanced digital factory being constructed in Hagerstown, Maryland in the US.
This facility, designed to build rail cars for the Washington Metro Authority, is designed around an AI-first approach that sees AI, robots and human workers collaborate to ensure high-quality production and sustainability.
The AI built into the operations eliminates human dependency for quality control, leveraging AI/ML and computer vision technologies to enhance product quality continuously.
The integration of AI across the manufacturing value chain has transformed the process, improving sustainability and efficiency.
What is the biggest challenge surrounding AI adoption in manufacturing?
It revolves around data. Specifically, data quality, availability, integration with legacy systems and the capability to process real-time data effectively.
These issues often lead to high capital costs, which can be a deterrent for many manufacturers.
To address these challenges, HDS has invested heavily in Industry Cloud frameworks like HARC OT and its proprietary Edge IoT platform, HGDI (Hitachi Global Data Integration).
These help to resolve data quality and integration issues, while partnerships with companies like NVIDIA and AWS enhance our ability to process data in real time.
Investments like these have significantly accelerated AI adoption, particularly with customers such as Hitachi Rail and a leading global automotive OEM.
How will agentic AI transform existing quality control and predictive maintenance models?
Agentic AI is a game-changer in manufacturing. Unlike traditional AI, agentic AI autonomously reasons data and works towards more accurate outcomes continuously.
We’ve seen firsthand the impact of deploying agentic AI in our factories, particularly through our Advanced Quality Inspection (AQI) solution.
Here, robotic spot dogs capture images and videos of the final product assembly. This computer vision data is processed in real-time at the edge, where agentic AI constantly refines the model, improving inconsistencies, defects and deviations to enhance product quality.
Additionally, agentic AI investigates the root causes of machinery stoppages, improving Overall Equipment Efficiency (OEE) and Operational Availability (OA).
We’ve already deployed this technology across over 400 machines in a leading automotive manufacturer, improving quality control and predictive maintenance.
How is AI challenging traditional engineering paradigms in manufacturing, particularly through integration with digital twins?
It’s revolutionising the way we think about digital twins in manufacturing.
Traditional digital twins are often based on physics-based models, which, while useful, can’t always scale effectively or learn from new data. AI-enabled digital twins meanwhile can scale across multiple fleets and diverse product families.
We’ve utilised AI to enable digital twins to learn continuously from new failure patterns, unlike traditional models.
By integrating AI with frameworks like HARC OT, these digital twins can constantly evolve and improve, offering manufacturers more adaptive and predictive insights to optimise performance.
Do you see AI altering the competitive dynamics of global manufacturing and supply chains?
Absolutely, AI is already reshaping the competitive landscape of global manufacturing and supply chains.
Manufacturers that embrace AI can see up to a 10% improvement in raw material and process efficiency.
Moreover, AI can extend product life by 15-20%, creating significant value through digital revenue streams and servitisation.
This shift not only enhances operational efficiency but also opens up new business models focused on product-as-a-service, allowing manufacturers to derive ongoing value from their products long after they leave the factory floor.
The result is a more agile, efficient and sustainable global manufacturing ecosystem.
