How artificial intelligence (AI) is revolutionizing the manufacturing industry

• Intro

• What is manufacturing and why should I care?

• Let’s talk about AI in manufacturing

• Concerns and challenges of AI in manufacturing

• Conclusion

If I say the word “manufacturing,” would it capture your attention?

What if I mention Apple and their “California Streaming” event from a couple days ago?

How about Tesla’s “AI Day” and their unveiling of their humanoid Tesla Bot a month ago?

Now I may have your attention along with a flooded news feed, millions of views on YouTube, and let’s not forget a customized like button on Twitter.

“Alright, alright, I’m listening! What’s your point?”

From some of the sexier brands like Apple and Tesla to some of the lesser in pharmaceuticals, engineering, and textiles (and everything in between), manufacturing plays a huge role in the global economy. Unfortunately, the year 2020 was a year like no other in recent history and had a significant impact on the industry. Forced shutdowns led to decline in production, causing a big dip in manufacturing employment levels. In these unprecedented times, there was a silver lining. In this article, we will explore modern day manufacturing challenges and how AI and advancement of technology is revolutionizing the world of manufacturing.

“By continuing to invest in digital initiatives across their production process and supply network, manufacturers can respond to the disruptions caused by the pandemic and build resilience that can enable them to thrive.” — 

According to the

, manufacturers in the US account for nearly 11% of the total output in the state, employing 8.58% of the workforce. In 2019, total output from manufacturing was $2.3T. In 2020, there was an average of 12M manufacturing employees. It’s fairly clear that manufacturing has a great impact on the economy and the labor force.

Before we dive into modern day manufacturing, we should better understand what manufacturing entails and some background on how it became one of the biggest industries globally. According to Investopedia, “Manufacturing is the processing of raw materials or parts into finished goods through the use of tools, human labor, machinery, and chemical processing. Large-scale manufacturing allows for the mass production of goods using assembly line processes and advanced technologies as core assets. Efficient manufacturing techniques enable manufacturers to take advantage of economies of scale, producing more units at a lower cost.” Historically, humans have looked for ways to transform raw materials (ore, wood, etc.) into finished products (e.g. metal goods, furniture, processed foods). The concept was to refine or process raw materials into something more useful, making it valuable in the eyes of consumers and businesses.

Manufacturers are pioneers of automation, coined in the 1940s at Ford Motor Co. Now, they’re leading the way in AI applications and using AI-powered analytics to improve efficiency, product quality, employee safety, and more. Let’s take a look at how AI is being used in manufacturing today:

I’m pretty sure robots are among the first things to come to mind when “AI” is mentioned. Well, in the manufacturing realm, robots are already a thing. According to a

, a record of 2.7M industrial robots operating in manufacturing plants around the world in 2019 with a recent report indicating an increasing need for automation in manufacturing processes. It’s

that robot sales could drive up from 465k in 2020 to 584k units in 2022, further surpassing the all-time high of 2018.

AI-powered robots mainly relieve staff from mundane and repetitive tasks. They can also provide new opportunities for scalable work such as:

• Assembly line work

• Handling raw materials

• Welding

• Machining

• Packaging

Overall impact of improving operational efficiency and quality, reducing downtown risk, with the ability to work 24/7 in harsh and hazardous environments.

Digital twins is a virtual representation of the as-designed, as-built, and as-maintained physical product which is augmented by real-time process data and analytics based on accurate configurations of the physical product, production systems, or equipment. This can pair up virtual and physical attributes for analyzing large data collected by sensors or cameras. The goal is to design and test equipment virtually.​​

Top manufacturing automotive companies such as Volkswagen, Toyota, and General Motors (GM) utilize AI in their factories. GANs are neural networks, a class of machine learning (ML) algorithms, capable of producing images based on given sets of photos (Check out our blog

to learn more about GANs). GM uses GANs in generative design, improved personalization, performance and customization. The software analyzes design permutations, which then recommends solutions which incorporate parameters such as material, budget, etc.

ML solutions can assist with inventory planning as they are useful in dealing with demand forecasting and supply planning. AI application in manufacturing allows for managing order records and delete/add new inventories. AI-powered demand forecasting tools provide more accurate results than most traditional methods (e.g.

, etc.) that engineers use at manufacturing facilities.

AI in supply chain management is rapidly growing. ML, NLP (

),

, robotics, and

all contribute to making supply chain management tasks smarter. AI can optimize warehouse management and logistic operations through demand predictions, order modification, even the capability of tracking and re-routing products in transit.

AI technology can assist with predictive maintenance by identifying potential downtime and accidents by analyzing the sensor data. The system then helps forecast when or if functional equipment will fail, so that maintenance and repair can be scheduled before failure occurs. Such analysis can help reduce malfunctioning risks and operational costs.

Assembly lines are data-driven and work based on a set of parameters and algorithms that provide guidelines to produce best possible end-products. AI can improve quality control by using computer vision technology and can detect defects of products on the production line, sometimes difficult even to expert human eyes. The system triggers alerts to users so they can review and make appropriate adjustments.

Or expert bunny eyes… (Source: Giphy)

Among nearly every industry (e.g.

,

,

,

,

,

,

,

,

, etc.), the question always arises “Will AI replace humans in the workforce?” Just like many of the other industries, there is no blaring indicator that AI will replace humans, but rather work to complement and support them. History has shown that technological advances are nothing more than a shift in focus and the way we do things. Certain roles will eventually become obsolete, while others will emerge. This will lead to other challenges such as shortage of specialized AI talent, which we’ll begin to see a trend of upskilling and reskilling to adapt in the near future.

By 2025,

predicts that the top 50 consumer goods manufacturers will have invested in a brand app using AI, embedded technology in the product, videos as a digital asset, and/or integrated innovation with IT and R&D teams. Such trends will drive business disruption and open up opportunities for manufacturers by providing more capabilities, reducing costs, increasing safety, and giving the ability for faster decision making.

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