Improving plant operator performance and maximizing efficiency has become increasingly important in industrial settings amidst challenges such as workforce shortages and skills gaps. As a result, businesses are looking to adopt emerging technologies such as artificial intelligence (AI) to aid in these challenges. Hrishikesh Upasani, LSS Offering Manager at Honeywell Process Solutions discusses the current state of AI in manufacturing, tools available to help operators in this age of digital transformation and what is on the horizon for AI and machine learning (ML) in the coming years.
Q: What is the current state of AI and ML in the manufacturing space?
A: AI and ML have become critical to manufacturing operations across industries. According to a 2024 study from McKinsey & Company, 72% of organizations surveyed have adopted AI in at least one business function, a jump from 55% in 2023.
In this age of digital transformation and the rise of generative AI, natural language processing (NLP) on large language models (LLM) using sentiment analysis are examples of how generative AI is being integrated into the manufacturing industry. From generating reports and serving as intelligent search tools to autonomously driving workflows, generative AI is a necessary tool manufacturers are implementing, or must quickly begin to implement, to increase efficiency and improve overall performance.
Q: What technologies does Honeywell have to offer to support plant operators in improving overall performance?
A: One example of this type of technology is Honeywell’s Experion Highly Augmented Lookahead Operations (HALO) Operator Advisor, which leverages advanced AI/ML to provide users with actionable insights and recommended strategies to address performance-related gaps. The software extracts valuable insights from distributed controls systems, translating them into user-friendly dashboards, offering operators a holistic view of performance and opportunities for improvement. By monitoring KPIs and highlighting deviations from optimal production levels, HALO Operator Advisor enables users to make informed decisions to optimize efficiency and throughput.
Q: What advantages does HALO Operator Advisor offer industrial operators and how can it help companies address the skills gap with its current workforce?
A: According to a recent study by Deloitte and The Manufacturing Institute, the manufacturing skills gap in the U.S. could result in 2.1 million unfilled jobs by 2030. A large majority of manufacturers say they have problems attracting and retaining employees, which is a heightened issue for specialized positions. At a time like this, it more crucial than ever to incorporate the tools that will provide employees with the learnings on how each staff member is critical to day-to-day operations.
One of the key features of HALO Operator Advisor is the ability to leverage AI to provide insights into operator workload during shift operations, enabling proactive measures to enhance productivity. Additionally, the software offers detailed assessments of operator competency and can facilitate the development of training plans to expedite skill building. Both features offer the opportunity to improve overall operational performance and give employees real time feedback and training opportunities.
Potential benefits include reduction in incidents and human errors, annual reduction in operational costs by optimizing worker productivity and training, and advancing toward full autonomous plant operation; and also, savings in annual maintenance costs through improved equipment reliability.
Q: What are some of the challenges of implementing AI and ML into operations?
A: Data, cybersecurity and scalability are three major challenges operators encounter when implementing AI and ML into operations. In terms of data, large AI/ML models need a huge amount of data in quantity and variety. Clean and accessible data is key to the success of these models, and manufactures can struggle with having enough high-quality data for input. Additionally, speed and security of data access is critical. In an industrial context, it is crucial to have the right controls and protection when deploying digital transformation initiatives.
Finally, the inherent complexity of adopting these types of technologies can prove to be challenging for companies. Through our decades of experience, it is clear the scalability of a digital transformation is not purely technical – it is about change management and executive engagement. Success depends on setting the key success criteria and adoption milestones, driving a change program, and ensuring regular measurement and reporting of progress.
Q: How do you see these types of technologies evolving in the next five years?
A: In the coming years, manufacturers need to remain flexible and versatile when it comes to implementing learnings and takeaways from working with new technologies. One main trend we expect to evolve further in the next five years is virtualization and cloud computing. As more analytics and AI algorithms are deployed in digital transformation programs, there will be a tradeoff between physical, virtual, and cloud computing risks and opportunities. While some manufacturers are still not comfortable with cloud computing for certain applications, as we get beyond the initial hesitancy and implementation challenges, we expect to see more virtual and cloud adoption in years to come.
