By Chetan Gupta, General Manager, Advanced AI Center, and Head of Global AI Center of Excellence, Hitachi, Ltd.
The manufacturing industry is on the cusp of a major transformation—driven largely by the rise of artificial intelligence (AI). This shift is poised to revolutionize traditional manufacturing processes, fundamentally reshaping the industry's operations.
By 2035, as AI becomes increasingly adept at detecting faulty or idle machinery, profitability in the sector is expected to improve by up to 38%1, a change as significant as the introduction of the assembly line in the early 20th century. Just as that innovation redefined production, AI will similarly leave an indelible mark, driving efficiency, innovation, and growth to unprecedented levels.
While this transformation is still in its early stages, manufacturers across various segments are already embedding AI into their production and planning processes. AI's applications span the entire manufacturing value chain, from supply chain management and production planning to safety and execution. In many companies, AI has become an integral part of their daily operations, particularly in quality control, where it can improve parts inspection and reduce defects.
Additionally, AI plays a crucial role in predictive maintenance, helping to prevent costly equipment failures that could otherwise halt entire production lines. AI-driven predictive maintenance allows Hitachi’s customers to anticipate equipment failures before they happen, minimizing downtime and boosting efficiency. Real-time quality control systems detect product defects instantly, ensuring consistently high product quality. Additionally, AI optimizes manufacturing workflows by pinpointing inefficiencies, streamlining processes and improving outcomes.
The advent of Generative AI (GenAI) pushes the envelope even further. GenAI is a type of artificial intelligence used to create new content based on models trained on text, visual, and audio data in response to prompts. For many use cases, Large Language Models (LLMs) eliminate the need for manufacturers to develop custom AI/ML (Artificial Intelligence/Machine Learning) models, allowing them to accelerate innovation and deployment with ready-to-use, high-performance tools.
AI’s impact—especially that of GenAI—can be thought of as akin to the move from artisanal production to automation, as it opens a myriad of new possibilities for manufacturing. For example, an AI agent built with GenAI could potentially ingest a company’s maintenance and operations manuals, serving the information through a mixed-reality interface so that an operator can quickly access the right information. To take an example from the healthcare industry, Mayo Clinic is exploring GenAI to assist in clinical documentation, improving accuracy and freeing up more time for patient care2.
Another promising area is in the early stages of the manufacturing value chain. If a manufacturer needs to answer an RFP (Request For Proposal), for example, GenAI can be used to help create a response by examining previous responses from the company, thus aiding the subsequent design process. Similarly, AI can be used to augment existing product design processes.
What’s more, GenAI has implications for the future of digital transformation projects3 as it can help manufacturers get greater benefits from their current initiatives. While digital transformation gave companies the ability to access data, these same organizations often did not have the vision or wherewithal to leverage that greater access to information to achieve real business value. The result is that until now, digital transformation’s performance hasn’t lived up to its promise. Indeed, Bain & Company estimates the success rate in industrial organizations to be about 8%4.
Enter AI and GenAI, which enable companies to finally leverage all that data. Indeed, GenAI adoption is expected to accelerate the forces now underway5 in the manufacturing landscape, sometimes referred to as Industry 4.0, resulting in more streamlined product development as well as faster response times to changes in the market.
These are just a few examples, among many, where GenAI is expected to touch every facet of the manufacturing value chain.
On paper, this all sounds wonderful. But when it comes to AI-driven solutions, no single software company offers a comprehensive solution, so manufacturers often find themselves working on individual projects with different vendors. This requires in-house expertise as these solutions need to be integrated and managed. The result: any solution is likely to become more expensive and time-consuming to implement.
So, how should you proceed?
Start by recognizing that there are prerequisites from the business side as well as the AI side.
Begin by identifying your business’ key pain points. This will require the participation of domain experts, AI experts, and the IT team. Domain experts are essential to assess and qualify the business problem. Otherwise, people might look to solve a problem with AI but fail to satisfy necessary business requirements. The IT team also needs to be available to ensure that solutions are effectively deployed on the shop floor. You don’t want to risk building solutions that can’t be deployed widely.
Then, once you’ve determined that your company has enough data to leverage AI and address your business problem, you can adopt a stage-gate development process.
Test your solution through a small Proof of Concept (PoC). This approach will help you manage your budget as well as your time. If the PoC is successful, you can go directly into production or try to improve the performance of the model further before deploying it in the field.
It’s essential that you engage a team that has expertise in AI, which is what one of the largest trucking companies in North America did when they needed help with a critical project.
Penske Truck Leasing maintains about 440,000 vehicles in its fleet. When Penske’s trucks came in for repair checks, newer, less well-trained technicians often struggled to diagnose problems efficiently. At the same time, they also faced a time crunch to finish their jobs. So, they would essentially clear a repair and send the vehicle back into the field. All too often, the same vehicle would return to the shop under the classification of a repeat repair. This inefficiency was both costly and resource-intensive.
Hitachi addressed this issue by building a Machine Learning model that helped Penske technicians correctly predict or recommend the proper repair action for their trucks. This innovation not only reduced the time vehicles spent in the shop but also drastically reduced repeat repairs, positively impacting the company’s bottom line.
This is just one example of how Hitachi is developing innovative AI solutions that enhance predictive maintenance and optimize production processes. Our commitment to leveraging AI and IoT (Internet of Things) technologies is driving significant advancements in manufacturing, ensuring a competitive edge and promoting sustainable practices for companies.
As a leader in digital transformation in the manufacturing industry, Hitachi has decades of experience addressing challenges in maintenance, repair operations, optimization, quality assurance, and supply chains.
These are all challenges that AI can help resolve. With Hitachi as your partner, you have end-to-end support — from strategic advisory and solution development experience to data management and deep technology expertise.
Together, we can help you unlock the expansive potential of AI to drive innovation, boost your competitiveness. Whether you’re beginning your AI journey or ready to scale, contact us today.
Vice President, Industrial AI Lab, R&D Division, Hitachi America, Ltd., General Manager, Advanced AI Center, Hitachi, Ltd., Head of Global AI CoEGM, Advanced AI Center, Hitachi, Ltd.
Chetan Gupta is a distinguished professional in Industrial AI, recognized for his contributions both within Hitachi and in the broader industry. His journey has seen him transition from a dedicated researcher to an influential leader. As the GM of Hitachi's Advanced AI Research Center in Japan, leader of the Industrial AI Lab in North America, and head of the Global AI CoE, he steers groundbreaking work in AI technologies and solutions that are crucial for industrial, enterprise, and societal applications. Chetan has close to 200 papers and patents and holds a Ph.D. in Mathematics and M.S. in Mathematical Computer Science and Chemical Engineering. He has won numerous awards internally and externally and his leadership is also evident in his contributions to key projects and educational initiatives within the field.
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