How AI is set to transform the manufacturing industry

  (photo credit: INGIMAGE)
(photo credit: INGIMAGE)

Artificial intelligence continues to make headlines – good and bad. While some leading technologists are warning of potential threats from AI, industry commentators continue to focus on the positive benefits for manufacturing:

  • According to an IDC study, 50 percent of respondents planned to use AI across business functions in the next 12 months with AI-powered automation at the center of process transformations. 
  • A report by Capgemini Research found that 51 percent of top global manufacturers in Europe were implementing at least one use case of AI. 
  • Looking to the future, analysis by PwC forecast that global economic output measured by gross domestic product could be 14 percent higher in 2030 than baseline predictions of $114 trillion as a result of the predicted adoption of AI. 

In Israel, there are further indications of its value, according to Ziv Katzir, Head of the National Program for AI Infrastructure at the Israel Innovation Authority (IIA), “In 2023, we will see more and more industry verticals adopting AI, and we will see companies being built from the ground up with AI at their center. This has transformative potential.”

These insights are strong indicators of the AI-related transformation already taking place in manufacturing, so what’s driving the change, where is AI being used and how important are the potential benefits of adoption?

Why AI is driving change 

At one level, AI is seen as a new way of solving traditional manufacturing problems – high production costs, process inefficiencies, equipment failure and supply chain issues. However, leading manufacturers are looking beyond those challenges to make AI the driver for more transformative strategies.

The IDC study, for example, found that AI initiatives were driving improvements in four key business outcomes:

  • 35 percent increase in innovation
  • 33 percent improvement in sustainability
  • 32 percent improvement in customer retention
  • 32 percent increase in employee retention

These outcomes were ahead of traditional drivers such as improved operational efficiency (31 percent) and improved employee productivity (30 percent).

Looking at regional drivers, the study found significant variations in manufacturers’ expectations and objectives:

  • North America’s respondents put improved operational efficiency, improved employee productivity and improved customer experience as their top three goals.
  • In Europe, Middle East and Africa, improved operational efficiency, improved customer experience and increased innovation topped the lists.
  • In the Asia-Pacific region, increased revenue from new markets or new products, improved customer experience and improved operational efficiency were the priorities. 

AI’s impact on manufacturing processes

There are many potential use cases for AI in the manufacturing processes, but the most common areas for adoption are currently predictive maintenance and quality control.

The US National Institute for Standards and Technology believes that there are five areas where AI creates a significant financial impact:

  • Predictive maintenance. By taking historic data from maintenance logs, production staff can predict how a machine will behave under a future payload, whether they will need to fix it, when, why and how – based on what fixed that problem in the past. This can reduce downtime significantly.
  • Predictive quality. Predicting and reducing quality issues can yield significant cost savings.
  • Scrap reduction. Using metrics to predict behavior across product specifications can minimize scrap and maximize product quality.
  • Increasing yield. Predicting if and when a machine or process will no longer meet given specifications enables production staff to proactively do what’s needed to bring it back into specification, reducing quality passes.
  • Demand and inventory forecasting. With a thorough understanding of plant operations and the data behind production, like MRP software, AI can forecast the demand and movement of critical parts, resulting in significant inventory savings.

Challenges to adoption

Despite the clear benefits of AI in manufacturing, a number of perceived barriers to adoption remain. Analysis by PwC identified three main challenges:

  • Technology not yet mature
  • Workforce lacks the skills to implement AI
  • Uncertainty over the Return on Investment

The firm also found that the businesses who were moving quickly on AI adoption were those that had already made strong progress on digitizing their core business processes. PwC classifies them as digital champions, compared to digital novices — 69 percent of digital champions had implemented, piloted or planned AI initiatives, compared to just 10 percent of novices. 

Data is the foundation

Data and software are essential components of successful AI implementation. The PwC report points out that accurate data acquisition, management and governance are key to applying AI algorithms to manufacturing processes. They explain that sensor data from connected factory equipment is a key data source in the manufacturing sector, so the production line and factories play a critical role in the data-acquisition process.

PwC recommends that manufacturers should start by mapping their main data objects, such as production facilities, machinery and products and the associated data sources to understand the data volumes, velocities and varieties they will be dealing with.

Adoption continues to grow

According to the IDC study, AI adoption has increased three times since 2019, with 25 percent of AI initiatives now reported to be in production. As adoption and spending continue to rise, AI is set to disrupt and transform virtually every process in the industry.

This article was written by Ian Linton