AI in Manufacturing: Market Overview and Applications
Introduction
“Intelligence” is generally defined as “the ability to learn or understand, or to deal with new or trying situations.”1 A trait or capacity normally associated with biological beings such as chimpanzees, dolphins, and, of course, humans, recent scientific and engineering developments have enabled computers to exercise “artificial intelligence” or AI. Artificial intelligence is “the simulation of human intelligence processes [especially learning and adaptive behavior] by machines.”2
AI is presently powering a wide variety of business and consumer applications, such as sifting through mountains of Big Data to extract precious business intelligence, permitting a vehicle to drive itself, or helping a teenager write a term paper.
In the enterprise space, artificial intelligence is now present (almost omnipresent) in virtually every industry sector, including finance, healthcare, transportation, energy, and manufacturing, where AI is essential to enabling the information centric Fourth Industrial Revolution or Industry 4.0.
Industry 4.0
For context:
The First Industrial Revolution was marked by the invention of the steam engine, which reduced industry’s reliance on animal and human labor.
The Second Industrial Revolution was driven by the development of the assembly line, allowing mass production.
The Third Industrial Revolution saw the introduction of factory automation, particularly labor-saving robots.
The Fourth Industrial Revolution represents “the integration of intelligent digital technologies into manufacturing and industrial processes,” especially:
- Big Data
- Industrial Internet of Things (IIoT) networks
- Artificial Intelligence.3
In addition to Industry 4.0, the application of artificial intelligence to manufacturing operations has helped spawn two related movements:
Smart Manufacturing – As defined by the International Organization for Standardization (ISO), smart manufacturing describes “how industry will leverage the application of new disruptive technologies such as ‘Artificial intelligence,’ ‘Edge computing,’ ‘Robotics’, ‘Additive manufacturing’ (3D printing), ‘Gene editing’ and the ‘Internet of Things’ to change the face of traditional manufacturing.”
Smart Factory – As envisioned by Bosch Rexroth, a smart factory is a facility that “responds flexibly to new customer needs [and] turbulence in supply chains, organizes itself, and uses automation, networking (IIoT), modularization, robotics, as well as artificial intelligence in many applications.”4
The Market
As projected by MarketsandMarkets, the Artificial Intelligence in Manufacturing market, valued at 3.2 billion in 2023, should reach $20.8 billion by 2028, realizing a remarkable compound annual growth rate (CAGR) of 45.6 percent during the 2023-2028 forecast period.
AI-Powered Robots Are Featured Performers In AI-Enabled Manufacturing
(Source: Rawpixel)
The company attributes the predicted expansion to “the rising need to handle increasingly large and complex [data sets, and] emerging industrial IoT and automation technology. The … market is witnessing significant growth because of the extensive usage of Big Data, industrial robots, and the evolution of the Internet of Things (IoT), along with a few macro drivers such as [an] emphasis on value creation and [customer experience].”
MarketsandMarkets found that AI is highly valued for its contribution to:
Predictive maintenance and machinery inspection, which ensure the proper functioning of manufacturing equipment, and reduce the rate of machine deterioration; and
Automotive assembly, where computer vision and machine learning technologies reduce plant downtime, vehicle recalls, and financial losses.
The major players in the AI in Manufacturing market are:
- General Electric (US)
- Siemens (Germany)
- NVIDIA (US)
- IBM (US)
- Intel (US)5
Use Cases
Artificial intelligence plays a vital role in how products get made. In particular, AI’s influence is felt in the following areas:
Cobot-Aided Manufacturing
A cobot, or collaborative robot, is a kind of industrial robot that operates alongside humans in a shared workspace. Unlike traditional industrial robots of the type featured on vehicle assembly lines, for example, cobots are smaller, weaker, and offer no physical threat to the human workers who function alongside them. In fact, cobots are designed to stay out of the way of their biological partners, using sensors to detect a human’s presence and slowing down or even stopping to prevent inadvertent collisions.
Owing to their small size and weight, cobots can be deployed virtually anywhere. They are affordable, easy to program, and ideal adjuncts to small – and even large – manufacturing operations. Recently, the cobot concept has been expanded by some to include digital collaboration via programs like ChatGPT.
Cobots are widely deployed in the manufacture of:
- Automobiles, including BMW and Ford
- Consumer goods, including Proctor & Gamble6
Predictive Maintenance
Predictive maintenance, a form of predictive analytics, utilizes real-time performance data gathered by IoT sensors and machine learning to forecast equipment failures, thus determining when maintenance is required.
Analyst Isabell Bücher reports that “One of the biggest beneficiaries of a predictive maintenance program is the process manufacturing industry, since it has an enormous number of interconnected moving parts, with many vital pieces of equipment, and production [that] can never slow down or pause. Process industries that benefit from predictive maintenance include:
- “Chemical processing plants,
- “Petrochemical plants,
- “Oil and gas industries,
- “Refineries,
- “Cement plants,
- “Paper and pulp plants,
- “Beverages, and
- “Pharmaceutical industries”7
Additive Manufacturing
Additive manufacturing – more commonly, “3D printing” – is the creation of solid objects like key chains, utensils, and toys from simple raw materials. Users specify the features of an object with computer-aided design software, and then a 3D printer creates the object.
Array of Parts Made Via Additive Manufacturing
(Source: Flickr)
As analyst Bernard Marr observes: “AI plays an important role in additive manufacturing by optimizing the way materials are dispensed and applied, as well as optimizing the design of complex products. It can also be used to spot and correct errors made by 3D printing technology in real-time.”8
Generative Design
Generative design is a form of generative AI, like ChatGPT or Dall-E, but instead of producing text or images, generative design is focused on crafting products.
As analyst Bernard Marr explains: “Designers simply enter parameters such as what materials should be used, the size and weight of the desired product, what manufacturing methods will be used, and how much it should cost, and the generative design algorithms spit out blueprints and instructions.
“Design engineers in the manufacturing industry can use this method to create a wide selection of design options for new products they want to create and then pick and choose the best ones to put into production. In this way, it accelerates product development processes while enabling innovation in design.”9
Robotic Process Automation
According to the Association for Intelligent Information Management (AIIM), robotic process automation (RPA) is “the term used for software tools that partially or fully automate human activities that are manual, rule-based, and repetitive. [RPA] tools are not replacements for the underlying business applications; rather, they simply automate the already manual tasks of human workers.”10Not a mystery to manufacturers, one 2022 survey revealed that 43 percent of manufacturing firms already use RPA. Common applications include:
- Assembly line automation
- Material handling
- Quality control11
Supply Chain Optimization
Almost all enterprises today, including manufacturing firms, are “virtual enterprises,” meaning they rely on a network of trusted suppliers, business partners, and other third-party entities to perform their basic business functions.
As analyst Jeremy Bowman points out: “It’s crucial for every manufacturer to have a well-managed supply chain so they have the parts they need when they need them. That’s why manufacturers often use artificial intelligence systems for supply chain optimization, focusing on demand forecasting, optimizing inventory, and finding the most efficient shipping routes. BMW … for example, uses AI to predict demand and optimize inventory. In one example, the company installed an AI application to prevent the transportation of empty containers on conveyor belts. The tech also decides if a container needs to be attached to a pallet and finds the shortest route for boxes to be disposed of.”12
Getting Started
When enhancing manufacturing operations with AI, analyst Beth Stackpole says start with data. “Compared with high-value AI initiatives in other industries, manufacturing use cases tend to be more individualized, with lower returns, and thus are more difficult to fund and execute. An alternative to a custom-built AI solution is a data-centric vertical AI platform, which can facilitate specific use cases. For example, an automated anomaly detection tool could replace or augment human workers who are tasked with quality control.”13
One commercially available solution worth considering is Amazon Web Services (AWS) Panorama, “a collection of machine learning devices and a software development kit (SDK) that brings [computer vision] to on-premises internet protocol (IP) cameras.” Panorama is ideal for evaluating manufacturing quality, detecting anomalies in real time, and providing manufacturers with an opportunity to fix potentially expensive defects.
Before investing in artificial intelligence, however, manufacturers should:
- Conduct a thorough survey of their operations to identity their production “pain points” and
- Engage a third-party consulting firm experienced in both manufacturing and AI to help fashion an AI strategy.
Web Links
Amazon Web Services: https://aws.amazon.com/
General Electric: https://www.ge.com/
International Organization for Standardization: https://www.iso.org/
Siemens: https://www.siemens.com/
US National Institute of Standards and Technology: https://www.nist.gov/
References
1 Webster’s Dictionary.
2 TechTarget.
3 “What Is Industry 4.0?” SAP. 2023.
4 “Smart Factory: Revolutionizing the Manufacturing Industry.” Bosch Rexroth AG. 2023.
5 “Artificial Intelligence in Manufacturing Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Application (Predictive Maintenance & Machinery Inspection, Cybersecurity) – Global Forecast to 2028.” MarketsandMarkets Research Private Ltd. 2023.
6 Bernard Marr. “Artificial Intelligence in Manufacturing: Four Use Cases You Need to Know in 2023.” July 7, 2023.
7 Isabell Bücher. “Everything You Need to Know About Predictive Maintenance.” Precognize. May 23, 2023.
8 Bernard Marr. “Artificial Intelligence in Manufacturing: Four Use Cases You Need to Know in 2023.” July 7, 2023.
9 Ibid.
10 “What Is Robotic Process Automation?” AIIM. 2019.
11 Jeremy Bowman. “How Artificial Intelligence Is Used in Manufacturing.” The Motley Fool. July 19, 2023.
12 Ibid.
13 Beth Stackpole. “For AI in Manufacturing, Start with Data.” MIT Sloan School of Management. June 28, 2023.