Lifecycle Engineering Blog 1
The Untapped Potential of Generative AI in Engineering and Manufacturing
In the age of rapid technological advancement, industries across the globe are racing to adopt cutting-edge solutions to stay competitive. One such game-changing technology is generative AI, a subset of artificial intelligence that has the potential to revolutionise the engineering and manufacturing sectors. Despite its immense promise, generative AI adoption in these industries remains surprisingly low. Generative AI implementation specialists BetterFutures estimate the uptake to be only around 30%. A recent study by McKinsey & Company puts this as low as 20%. This blog post explores the reasons behind this slow uptake and highlights the significant opportunities that await early adopters.
The Promise of Generative AI
Generative AI refers to algorithms that can create new content, designs, and solutions based on existing data. In engineering and manufacturing, this technology can streamline numerous processes, from design optimisation and material selection to quality control. Here are some key benefits generative AI offers:
Design Optimisation: Generative AI can analyse vast amounts of data to suggest optimal design modifications, leading to improved performance and reduced material usage.
Quality Control: AI-driven inspection systems can detect defects more accurately and consistently than human inspectors, ensuring higher product quality.
Process Automation: Generative AI can automate routine tasks, such as generating work instructions and managing knowledge databases, and creating reports freeing up engineers to focus on innovation.
The Current State of Adoption
Despite these clear advantages, generative AI adoption in engineering and manufacturing is still in its infancy. Many organisations have yet to harness the power of this technology, largely due to several key factors:
Lack of Awareness: Many companies are simply unaware of the potential benefits of generative AI. They may not fully understand how it can be integrated into their existing workflows or the competitive edge it can provide.
Complexity of Integration: Implementing generative AI requires a significant investment in time and resources. Companies need to overhaul their current systems, train employees, and ensure data quality – all of which can be daunting tasks.
Cultural Resistance: Change is often met with resistance, and the engineering and manufacturing sectors are no exception. There can be a reluctance to move away from traditional methods and adopt new technologies.
Concerns About Job Displacement: There is a fear that AI will replace human jobs. While AI can automate certain tasks, its primary role should be to augment human capabilities, allowing engineers to focus on more strategic and creative work.
Opportunities for Early Adopters
For those companies willing to take the plunge, the opportunities presented by generative AI are substantial. Early adopters can expect to see significant improvements in efficiency, cost savings, and innovation capabilities. Here’s how embracing generative AI can provide a competitive edge.
Enhanced Innovation: By automating routine tasks, generative AI frees up engineers to focus on innovation and problem-solving. This can lead to the development of new products and solutions that set the company apart from its competitors.
Cost Savings: Improved design and process optimisation can lead to significant cost savings through reduced material usage, lower defect rates, and minimised downtime.
Increased Agility: Companies that adopt generative AI can respond more quickly to market changes and customer demands, thanks to more efficient processes and better decision-making capabilities.
Better Decision Making: With AI providing insights from data that might have otherwise gone unnoticed, companies can make more informed decisions, reducing risks and improving outcomes.
Where do we go from Here?
The engineering and manufacturing industries stand on the brink of a technological revolution with generative AI. While the current adoption rate is low, the potential benefits for early adopters are immense. By overcoming the initial hurdles of awareness, integration, and cultural resistance, companies can unlock new levels of efficiency, innovation, and competitiveness. The use of tools such as the Engineering Virtual Assistant from BetterFutures will smooth the transition for companies and help overcome the inertia of AI adoption. Change is not easy for some organisations and people, but this change will be positive, as it will allow engineers to focus on value-added technical tasks through the reduction of mundane and repetitive tasks.
At Lifecycle Engineering, we are committed to helping companies navigate the complexities of AI adoption. Our consultancy services are designed to guide ambitious engineering and manufacturing firms through the process using tailored GPT technology, ensuring they reap the full benefits of generative AI. Don’t get left behind – embrace the future of engineering and manufacturing today!
Lifecycle Engineering Blog 2
Generative AI - An Opportunity for Operation and Maintenance?
The Need for Generative AI
Use cases for Generative AI are still in their infancy within traditional engineering and manufacturing industries. Traditional AI has found O&M applications in ‘data intensive’ areas, such predictive maintenance, but the Gen AI breakthrough has not yet made significant in-roads in the O&M field.
Gen AI has the capabilities to provide instant access to data and provide insights that have previously been hidden, or prohibitive effort-wise to compile. Engineers and technicians spend a great deal of time manually trawling through data to compile new service reports. This is particularly applicable to organisations managing many assets across a wide range of locations. Data and knowledge then tend to become dislocated and siloed. Gen AI technologies, such as Retrieval-Augmented Generation (RAG) can scan many data sources quickly and accurately, then summarise the data to a user based on specific queries. This can all be done without exposing user data to the wider internet, and without using data to train external AI models.
The Potential of Generative AI
Gen AI can be used to greatly enhance knowledge retrieval and sharing within organisations and be used to accurately automate processes and documentation. The operation and maintenance field therefore offers great potential for Gen AI applications. As the diagram highlights below, the Engineering Verified Assistants (EVA)tool is able to quickly and accurately pull together data from multiple sources and provide insights to the user. The data sources can also be used to automatically generate new reports, such as work instructions for maintenance activities. The responsible engineer or technician can then review and sign-off the generated report.
The team at Lifecycle Engineering and BetterFutures.ai have developed to following applications that can be readily employed within the O&M field:
Service Assistants
The EVA tool is able to employ a bespoke Large Language Model and advanced RAG model to automatically generate work instructions from previously created work instructions, maintenance manuals, local work instruction data, and risk assessment data. This can help users create work instructions quickly and accurately using data stored across an organisation. The Service Assistant can also be used to review newly generated reports against a specified standard prior to approval by the responsible engineer or technician. Both the creation and screening of new reports can significantly reduce the administrative burden of engineers and technicians.
Knowledge Sharing and Retrieval of Operational Data
The EVATM tool can instantly retrieve and summarise O&M data from various sources including SCADA, service reports, maintenance manuals, OEM service bulletins, and root cause investigation reports. The instant synthesis and presentation of this data from these types of sources, allows the user to be able to extract data trends that potentially provide a unique insight into the operation of plant or machinery. These insights can be used to drive-up O&M efficiency and ultimately drive higher returns for asset operators.
The Path Ahead for Operation and Maintenance
There is little doubt that Generative AI will change the way our industries operate. Agentic AI is currently becoming more and more prevalent in areas such as retail and hospitality. Technical functions within engineering and manufacturing, such as O&M need data accuracy and security. The EVA has been designed with accuracy and security in mind. As engineers we understand that 95% accuracy is not good enough for our industry. Our reinforcement learning approach, agentic workflows, and bespoke LLM helps to achieve close to 100% accuracy from the data referenced. A key feature of the tool is that it provides the user with instant access to the data source, to expedite the data verification process.
User data does not leave the users servers and AI models are not trained using data, which is any way proprietary to the user. Owners and operators of assets now have to possibility to automate a significant proportion of their O&M documentation activities using tools like EVA. This is only the beginning of a journey that will see massive strides in how Gen AI is employed in the O&M field.
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