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AI and the New Era of Manufacturing SoC1252 September 2021

Author: David Strachan-Olson (Send us feedback.)

Many companies across a wide range of industries are planning to incorporate machine learning and AI into their operations. Companies in the manufacturing industry are no exception. The covid‑19 pandemic is leading to manufacturers' investing heavily in digital technologies and AI. In addition, manufacturers are moving from simply experimenting with AI to incorporating AI across entire facilities. Companies are building the factories of the future with data and AI as central pillars.

The covid‑19 pandemic could be the turning point for the widespread adoption of digital and AI technologies in manufacturing.

Using AI in manufacturing is not a new concept. Indeed, proponents of AI technology and Industry 4.0 have been discussing the concept for many years. AI systems can provide a wide range of applications, including demand forecasting, computer-vision‑based verification and inspection, predictive maintenance for industrial equipment, and optimization of supply chains and various processes. What is new is the increased pace of adoption and an emphasis on taking a holistic approach to AI deployment. The pandemic is leading to a wave of investment in digital technologies and AI by manufacturers. In addition, manufacturers are moving out of pilot purgatory—a company's remaining stuck in a new technology's testing phase—and developing an overarching strategy for data and AI.

The pandemic caused (and is still causing) significant disruptions to global manufacturing networks and supply chains. As the pandemic hit, manufacturing companies faced a wide variety of problems, including plants' closing to stop the spread of the disease, delays of shipments from suppliers, factories' operating with reduced staff, and struggles to meet rapidly recovering demand. For instance, early in the pandemic, the food industry had to shift from wholesale production for restaurants to extra retail production for grocery stores. Supply-chain problems remain a major issue for some industries—particularly industries that are dependent on semiconductor chips. Because of these major disruptions, many manufacturers have found themselves increasingly reliant on digital and AI technologies.

A survey of global manufacturing companies that McKinsey & Company (New York, New York) conducted six months after the pandemic began provides insight into how the pandemic affected Industry 4.0 adoption and use. Overall, 94% of survey respondents said that Industry 4.0 had helped their organizations maintain operations during the pandemic. In addition, 65% of respondents said that the pandemic had increased the value of Industry 4.0 technology in their minds. Many companies that had scaled deployments of digital technologies before the pandemic were able to leverage their systems to respond to the pandemic. For example, the McKinsey report highlights a consumer-packaged‑goods company that used a digital twin of its supply chain to run multiple scenarios to assist in planning and a personal-protective-equipment manufacturer that used augmented-reality‑based remote assistance for commissioning a new manufacturing line. Meanwhile, some companies that had only experimented with digital-technology deployments struggled to adapt.

As the pandemic has dragged on, more and more manufacturers have adopted digital and AI technology. A November 2020 Google Cloud (Alphabet; Mountain View, California) survey of more than 1,000 senior manufacturing executives from seven countries revealed that 76% of respondents had turned to AI and other digital technologies because of the pandemic. In addition, 66% of respondents who were already using AI in their operations said that their reliance on AI was increasing. The survey also revealed that the main applications of AI technology were for quality control and supply-chain management and that the top reasons that companies employ AI technology in their daily operations were to aid in business continuity, to improve employee efficiency, and to be helpful to employees overall.

Because of a shortage of skilled workers, improving the efficiency and productivity of factories is increasingly important to manufacturers. A May 2021 report by Deloitte Touche Tohmatsu (London, England) and the Manufacturing Institute (Washington, DC) found that because of a skill gap in US manufacturing, more than 2 million US manufacturing jobs may go unfilled through 2030, costing the US economy approximately $1 trillion that year alone. By the end of 2020, the US manufacturing industry had recouped only 63% of the jobs that it lost during the pandemic. The report analyzes data from two online surveys that included more than 800 US manufacturing leaders and found that 77% of survey respondents said that they will struggle to attract and retain workers in 2021 and beyond. Ongoing labor issues might encourage manufacturers to accelerate adoption of automation technologies and AI tools to increase factory productivity.

As companies become increasingly familiar with deploying AI technology, they will look to expand the use of data analytics and AI from individual processes to entire facilities and global supply chains. Google Cloud and Siemens (Munich, Germany) recently announced a partnership to help companies incorporate AI into manufacturing facilities. Google Cloud will integrate its data and AI technologies with Siemens's industrial automation technologies and Industrial Edge platform. Combining the expertise of the two companies should create a product that enables manufacturers easily to use data and AI‑driven insights to automate mundane tasks, improve quality, and empower employees.

Germany is leveraging its advanced manufacturing base to lead many Industry 4.0 projects that incorporate AI through entire facilities. For example, the government of Baden‑Württemberg, Germany, is funding the SelFab (Self‑Learning Photovoltaic Factory; https://selfab.de) project, which is a collaboration among five research institutes. The research partners will create a digital framework for a photovoltaics-production line by digitizing all relevant production processes. Real‑time AI systems will analyze plant and factory data to allow process and product optimization. In addition, cooperation between the production automation and ongoing photovoltaics-research efforts should create synergies that aid the development of the self‑learning factory and the rapid transfer of advanced technologies to production. And BMW (Munich, Germany) is using Nvidia Corporation's (Santa Clara, California) Omniverse software platform to create a detailed simulation of a manufacturing facility for electric powertrains. The simulation includes photorealistic textures and accurate physics for the movement of robots and components. Omniverse enables BMW to simulate the entire production line and see how changes in the facility layout affect the production process. BMW is also using the simulation and machine-learning technology to optimize the movement of parts, robots, and people around the facility.

The covid‑19 pandemic has brought significant disruption to the manufacturing industry and could be the turning point for the widespread adoption of digital and AI technologies in manufacturing. Digital technologies and AI not only can aid in increasing productivity and reducing costs but also can improve flexibility, resilience, and transparency in operations and supply chains.