AI benefits for industrial manufacturing environments

Artificial intelligence insights

• As AI, particularly large language models (LLMs) like ChatGPT, advances, the director of digital industries at Stratus Technologies envisions transformative applications, streamlining maintenance processes, enabling remote issue assessments, and facilitating efficient decision-making with natural language-based capabilities.
• AI, particularly generative AI, contributes significantly to sustainability goals in manufacturing. Predictive maintenance not only extends equipment lifespan and efficiency but also reduces resource consumption. Additionally, AI plays a vital role in knowledge transfer, aiding the industry in retaining and transferring crucial engineering skills to the younger workforce.

For many maintenance and control engineers, artificial intelligence (AI) is not a new concept. Those more advanced in their digital transformation journeys have already become very familiar with, for example, machine learning (ML) technology that has been helping with predictive and preventive maintenance strategies by analyzing large datasets from numerous sources to help make better-informed decisions.

Similarly, the use of large, contextualized datasets for a range of applications is helping digitally advanced organizations address issues such as energy reduction, supply chain optimization, quality control and many other optimization efforts.

Management and interrogation of data for improved decision-making sits right at the heart of any digital transformation and it relies on server technology to deliver reliable data, often in real-time. Data needs to be not only available, but also complete. Data-downtime will fundamentally undermine any digitalization initiatives.

When it comes to the future of AI in industry (and society as a whole), Greg Hookings, director of digital industries, EMEA at Stratus Technologies, believes we are approaching an exciting period of exponential growth for its application with the recent advent of early large language model (LLM) AI, such as ChatGPT.

“This type of natural language-based capability to interrogate potentially limitless and varied public data sets will have huge implications across industry and society, most of which are yet to unfold, but are likely to enable humans in industry to access and handle data very differently,” he said. “Early industrial use-cases benefitting from LLM (or generative) AI to help maintenance engineers assess issues at remote sites is already close. So, an operator with access to the control-layer software for an asset at present can already see if, for example, a bearing is running hot, and can prepare the maintenance team to replace it before it poses a problem for production.”

However, with generative AI pulling extensive additional information from various public and private (secure, IP-based) resources, the engineer could effectively ‘talk’ to the system, asking for likely causes and likely implications, as well as requesting relevant pages of machinery manuals and historic data from the application in question so that the maintenance team could prepare for a visit not simply to replace a defective part, but to understand associated issues and have a full statement of works (SOW) laid out.

“If, for example, the asset was on an oil rig, it could help plan all of this with the required logistics of cargo-loads, weather, staffing and any other variables in mind,” Hookings said. “The whole process of issues identification, resolution planning, and SOW, which would normally take several people hours, days or even weeks to coordinate, could, in theory, be completed by a single person in a chat window in minutes, reducing the downtime of the asset in question dramatically, as well as the overall cost of maintenance.”  This example gives just a hint at what may be possible. It also points to the importance of the servers that are handling the critical information from within the system.

Using the data

While pointing out that a key role for AI in industrial applications currently is for analyzing data and turning it into usable information, Ed Goffin, VP product marketing at Pleora Technologies, explained  that more often than not Pleora is brought into a project to help solve a product quality issue, using tools like vision and AI. “The by-product of solving that quality issue is data and this is especially advantageous in manual manufacturing processes where there is less opportunity to gather insight,” he said. “By gaining this data a manufacturer can start to implement corrective actions, around a process or to identify a maintenance issue.” Offering an example Ed cited work undertaken with a manufacturer in the consumer goods market. “While they do AI-based quality inspection to solve quality concerns, the data also allows them to start to identify issues that may be occurring in their automated processes before they have a significant cost impact.”

In the near future, Goffin expects to see an increasing focus on the use of software tools to develop and customize AI-based workflows. “These software tools are becoming easier to use and offer an opportunity for control engineers to reduce external consulting costs as the software will give them the tools they need to design and deploy their own AI-based workflows.”

Sustainability

Saar Yoskovitz, CEO at Augury, argued AI will have an important role to play in helping engineers achieve their sustainability goals. “The technology can not only take into account vast amounts of data and analyze scores of manufacturing processes, but can also help manufacturers leverage new advances such as generative AI, which makes it easy to act on the insights the technology provides from the data.” Offering an example, he pointed out that AI plays a pivotal role in predictive maintenance, alerting manufacturers of upcoming breakdowns or faults before they become critical or cause downtime. This contributes to extending the equipment’s lifespan, enhancing its efficiency, and minimizing financial and resource losses caused by downtime.

An additional benefit of reduced downtime and improved machinery efficiency is a decrease in resource consumption, including a minimum 20% reduction in energy usage, diminished water consumption, and a reduction in waste generation. AI technology is an always-on system that can support real-time, around-the-clock monitoring to ensure the production process remains sustainable.

Finally, many industrial sectors are struggling to recruit new engineering talent as their mature engineers look towards retirement. It is important to retain engineering skills and transfer them to the younger generation as quickly as possible. AI can help here too because it collects manufacturing knowledge and uses it to create actionable insights, which enables new employees on the shop floor to get up to speed quickly and act on the suggestions provided by the technology.

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