AI Makes a Deep Impression on Industrial Manufacturing

May 11, 2021
A survey confirms AI adoption gained traction with industrial manufacturers over the course of the pandemic.

From design to process planning and production, manufacturers will be looking to advanced technologies such as artificial intelligence (AI) to step up resilience planning.

This finding was reinforced over the course of the past year, according to a KPMG survey, “Thriving in an AI World,” which solicited feedback from seven industries.

According to the survey, 49% of industrial manufacturing business leaders reported that AI is fully functional at scale within their organization, and 61% of industrial business leaders noted that an increase in productivity is its greatest potential benefit.

Business leaders also reported that AI is at least moderately functional in their organizations, including those in industrial manufacturing (93%), financial services (84%), technology (83%), retail (81%), life sciences (77 %), healthcare (67%) and government (61%).

The Big Takeaway

The survey’s historic finding, according to David Neely, managing director, Intelligent Information, KPMG, was that the reported adoption rate was highest of all industries in industrial manufacturing.

“It runs a little bit contrary to what we would have expected based on some of the interactions we’ve had with clients in this space,” Neely said. The industrial manufacturing (IM) sector was the most mature at using these technologies, but revealed the highest levels of angst when it came to employing AI technologies from a security, privacy and regulatory standpoint

He further explained that AI adoption in industrial manufacturing is observed along two realms. Firstly, IM adoption of technology automation has been concentrated in the back office for routine, value-added activities, where enabling efficiencies, productivity and cost reduction are key drivers.

Secondly, IM has for many years been a leader at incorporating AI technologies on the product side—for technology going into self-driving vehicles, aircraft, aerospace and defense products. IM has lagged when it comes to implementing AI technologies into business functions, Neely said.

For Roy Mathews, managing director, Data and Analytics, KPMG, IM can be further classified into three buckets: smart factories, smart products and smart office. “Smart office is the area in which industrial manufacturing is lagging,” he said. “But smart factories and smart products are the areas that are natural to IM, given the proliferation of Industry 4.0 initiatives. There is an incentive to automate. AI is a huge catalyst for Industry 4.0.”

There is a natural tendency to adapt AI for factory settings, said Mathews. The data associated with AI in IM environments is generally structured. Sensors capture structured data from equipment, which is easy to process and enable, he said. This is unlike financial services or healthcare, where there’s a lot of unstructured data.

Defining AI

For the study, AI was defined as any type of solution that generates insights from data or from patterns. The definition also extended the definition to automating tasks, which would include such concepts as machine learning, cognitive computing and robotic process automation. 

A further classification would be to think of AI as those technologies that emulate human decision making. “Contrast this to technologies that emulate human activities—which is more in the realm of process automation or robotic automation,” said Neely.

Experimental to Mainstream

While AI is native to technology companies such as Google and Apple, the rest of the industry is now adapting it and making it part of the business function, said Mathews.

“The use of AI over the last decade has generally been experimental, but in recent years we have started to see a move from experimental stage to a production at scale mode,” he explained. Prior to that, organizations would embark only on innovation-related initiatives until they had a proof of concept, noted Mathews. There has since been a huge shift to initiatives moving into mainstream and production mode.

Other Findings

1. 61% of industrial manufacturing business leaders say that an increase in productivity is the greatest potential benefit of AI adoption, and most (95%) agree that AI technology would make their company run more efficiently.

2. According to industrial manufacturing business leaders, in the next two years, AI technology will have varying impacts and will experience growth dependent on industry needs:

  • 21% for product design, development and engineering
  • 21% for maintenance operations
  • 15% for production/assembly

Mathews said that leveraging AI to gain efficiencies across maintenance, operations, product design and engineering, and supply chain will be integral to building enterprise resilience.

Neely agreed. “That’s going to be increasingly important in the future, given the learnings of the COVID-19 disruption,” he said. “Companies are looking closely at how they build resilient supply chains and how they’ll recover from disruptions and brand disruption on a global scale.

“Whereas those problems were historically tackled from a mathematical modeling and optimization standpoint, new AI technologies provide a more sophisticated means for analyzing data, looking at alternatives and optimizing in an even more powerful and sophisticated way,” he concluded.

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