AI in Manufacturing Use Cases
While robotics is far from new in manufacturing, AI can take it to the next level. It'll automate complex and repetitive tasks previously too difficult for traditional automation systems. AI can improve efficiency and precision in assembly, welding, painting, and material handling functions.
AI can also power robots that work alongside humans. Collaborative robots (cobots) can do repetitive or dangerous tasks with more precision. They adapt to human movements, enhancing productivity and reducing the chance of workplace injuries.
Ford Motor Company uses cobots for welding, gluing, and quality control. Six of its cobots can sand the entire body surface of a car in just 35 seconds.
Since Henry Ford began leveraging assembly lines more than a century ago in manufacturing, they've reduced costs, improved quality, and dramatically boosted productivity. The challenge is when consumers want a customized product that's not the same as all the others coming off the line.
AI can enable mass customization, allowing manufacturers to offer personalized products at scale by dynamically adjusting production processes based on individual customer preferences. A great example is Nike, which offers "Nike by You." Through a website, customers can choose a base shoe, customize the colors, and even put their own lettering on their custom sneakers.
AI can also cut costs and wastage by optimizing designs to meet specific functional requirements while minimizing material usage.
A challenge for manufacturers has always been trying to see into the future when deciding how much to make of something. Too much, and product is wasted or has to be sold off at cut-rate prices. Too little, and there's a missed opportunity to sell more.
This is one area where AI makes a huge difference with its ability to crunch vast amounts of data for actionable analytics. AI algorithms can look through historical sales data, market trends, and external factors like weather or economic conditions to accurately forecast future demand. With better forecasting, companies can maintain optimal inventory levels, reducing both stockouts and overstock situations. eCommerce giants like Amazon routinely use AI to ensure that products are available in just the right quantities exactly when customers want them.
Amazon with their inventory management
Maersk Line with their shipping efficiencies
Walmart with their customer service
The key to minimizing costs associated with quality control problems is spotting issues as soon as possible—or better yet—preventing them in the first place. AI does both far better than human inspectors.
AI-powered computer vision systems can examine products for defects or inconsistencies, ensuring higher quality and reducing waste. Not only that, AI can monitor production processes in real time, adjusting parameters to maintain consistent product quality.
A study by McKinsey found that AI can help reduce quality control costs by 10 to 20 percent.
Something else that AI does better than humans is keeping manufacturing equipment running. It can analyze data from machinery sensors to predict when equipment will likely fail, allowing for maintenance before a breakdown occurs. Predictive maintenance helps in reducing unplanned downtime and extends the lifespan of machinery.
BMW says that using AI for predictive maintenance has helped it avoid around 500 minutes of disruption a year at one plant alone.
AI can optimize energy consumption in manufacturing plants by analyzing usage patterns and adjusting processes to be more energy efficient. It'll also help track and reduce carbon footprints, helping companies meet sustainability goals.
One California water utility uses an AI-enabled control system to save thousands of kilowatts of electricity annually.
