In 2022, we will see artificial intelligence (AI) becoming the most transformative technology humanity has ever developed. According to Google CEO Sundar Pichai, “its impact will be even greater than that of fire or electricity on our development as a species”. 
AI and machine learning offer manufacturing a chance for new and stable growth.  When paired with traditional automation, it can improve efficiency and decrease costs. Here are five ways AI and ML will change the manufacturing sector in the coming year:

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1- Increased Customization

Traditional customization requires unprecedented time and labor, beginning with the design process.   Such optimization remains unattainable for smaller manufacturers due to the resulting costs. AI and ML tools bring optimization back into reach. AI tools like generative design feed the software with parameters for materials, costs, and manufacturing methods. The software then explores all possible solutions and design iterations within those parameters as virtual alternatives before the actual production process takes place. A thousand iterations can take less time and cost than one traditionally created mocked-up product.  Here, products or services are tested, and their data is reviewed for design flaws.

 

2- Smart Manufacturing

Added sensors connected to AI and ML algorithms work differently.  Smart systems can identify changes in temperature, material, or process and automatically adjust or stop manufacturing before ruining materials or producing a substandard product.   This quick response can decrease costs while increasing reliability.  In turn, quality improves while manufacturing time diminishes.

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3- Improved Sourcing

The heavy overseas shipping network trapped thousands of large containers far away from goods waiting for shipment.  Meanwhile, local railroads, ports, and trucking lines are dealing with labor shortages, causing bottlenecks and delays when goods finally arrive. To make matters worse, costs are skyrocketing everywhere. AI can search and track shipping data patterns and raw materials expenses.  This gives manufacturers a clearer picture of how and when raw materials and finished goods should move to and from their facility.  It can be vital for tracking trends and forecasting the best time for ordering and shipping to ensure the best outcomes and lowest prices.  This risk management optimizes the flow of goods, making the best use of manufacturing facilities, limiting delays, and reducing expensive waste.

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4- Better Cybersecurity

Cybercrime is rising, and manufacturing targets are valuable ([i]25% of cybercrime is within the manufacturing sector. cybercrime will cost companies across the world $6 trillion annually by 2021, increasing from $3 trillion in 2015.). This year the World Economic Forum identified cybercrime as potentially posing a more significant risk to society than terrorism[ii]. AI algorithms, with their ability to process and sort through massive amounts of data, can significantly reduce unknown threats from hackers.  It can prevent expensive infiltrations before they happen by detecting vulnerabilities and possible threats before something goes wrong.

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5- Hyper-automation
Hyper-automation refers to the use of advanced technologies, like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA). Hyper-automation transforms processes across industries like banking, healthcare, construction, and e-commerce. Through automation, humans are freed from repetitive and low-value tasks to focus on ones that are of a higher value to the organization. Together, automation and human involvement help organizations to provide superior customer experiences while reducing operational costs and boosting profitability.  With hyper-automation, digital workers operate alongside humans to deliver unmatched efficiency.