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Building a greener, smarter future – why AI adoption in manufacturing goes beyond factory walls

By now, artificial intelligence is a cog in the well-oiled manufacturing machine. In fact, the AI in manufacturing market is predicted to reach a staggering $16.3 billion by 2027. How has it reached such heights? AI is a key element of the unfolding Fourth Industrial Revolution, being able to track core KPIs, produce accurate forecasting reports, anticipate tech disruptions, detect inefficiencies in real-time, and much, much more. That’s why the likes of BMW, Nissan, Canon, and Boeing have incorporated AI into their manufacturing processes.

But AI isn’t reserved for big players. Smaller companies are also leveraging AI to transform their operations, be more cost-effective, and optimize their sustainability efforts. Especially as some of the biggest concerns facing manufacturing at the moment include economic uncertainty, supply chain issues, and pending legislation around environmental practices, AI is a necessary solution for companies to stay resilient and agile.

Reports show that companies using AI have seen notable cost savings and revenue growth, but AI makes more than financial sense – it shapes companies that are more conscious of people and the planet. Here’s why AI adoption goes beyond four walls, and why every manufacturer should be integrating it.

More efficient product development across the board

AI functions across the entire lifecycle of a product. To begin, AI systems use machine learning to identify buying patterns and translate them into insights for manufacturers. For example, Danone Group uses AI for its demand forecasting and has seen a subsequent 30% reduction in lost sales, a 50% reduction in demand planners’ workload, and a 20% reduction in forecasting errors. Such data enables manufacturers to scale manufacturing accordingly and build products in more efficient ways.

AI software can also create multiple optimized designs for a product based on parameters like materials, size, manufacturing methods, and cost. With these designs, manufacturers can move forward with product development that is less time and resource-consuming. The effectiveness of AI-powered designs is why carmaker Nissan is currently developing its own AI to design cars without any human input.

Elsewhere, AI robotics and cobots (collaborative robots) facilitate product assembly. These machines work in close proximity to humans and can pick up, place, and sort through objects in the manufacturing process. AI improves their orientation and precision, meaning that more complex products can be built, and at a faster speed.

Perhaps one of the most impactful areas of AI in product development, however, is quality assurance. AI can ensure that items are fit for sale and meet companies’ high standards of delivery. Apple, Nintendo, Nokia, and Sony use Google Cloud Visual Inspection AI to find defects in their manufacturing, as the technology can highlight wrong, misplayed, missing, rotated, or deformed components at various stages of the assembly process. Even better, using the AI tool requires no previous technical expertise, so smaller, growing companies can embrace it too.

Other AI software called Robotic Process Automation can carry out repetitive, high-volume tasks like updating records, addressing queries, and performing calculations.

Smarter, safer (virtual) factories

Manufacturers have an inherent responsibility to protect their workers and warehouses – and they can do so using cutting-edge AI.

For one, AI programs can analyze data at a quicker and more accurate rate than humans, spotting dangerous situations in real time and alerting the right people to take action. Algorithms can comb through camera footage from manufacturing sites and flag high-risk situations. Likewise, AI can detect non-compliance like not wearing a helmet or harness in factories – which are some of the leading causes of accidents in manufacturing.

AI wearable sensors worn on clothing also contribute to safer work environments. These devices can monitor activities and stream data, if anomalies appear in the data, managers can be alerted to potential accidents; for instance, if large groups of workers are crowding around falling hazards.

The same heightened safety awareness applies to digital twin models, which are AI-driven, virtual representations of manufacturing floors. The models give manufacturers greater scope to experiment with scenarios in warehouses and make informed decisions about what technology and protocols to implement. Digital twins can essentially simulate operations and generate evidence to support safety cases – for example, by proving that robotics can weld materials and safely pass the completed component to a staff member for further assembly. It’s no surprise then, that already, one in five manufacturers is experimenting with actively developing a metaverse platform (a type of digital twin) for products and services.

Fueling green corporate responsibility

Corporate responsibility is a form of self-regulation where manufacturers undertake measures that benefit both people and the planet – and it’s becoming standard practice in the industry that produces one-fifth of all carbon emissions.

AI plays a big role in helping manufacturers be more sustainable. Predictions suggest that the tech could contribute to 79% of the UN’s Sustainable Development Goals, as well as accelerate greener behaviors across the board in manufacturing. For one, AI can help drive the reusing of heat in factory buildings and regulate lighting according to the number of people in the space. This management of facilities dramatically minimizes energy losses.

AI-based applications can also predict energy consumption, showing manufacturers where machines are inefficient and thus pushing them to invest in more eco-friendly hardware. AI solutions for one automotive company found that 40% of energy consumption for one machine occurred when it was not producing anything; the insight prompted the company to power down the machine more often and reap impressive energy and financial savings.

As stricter legislation is introduced to carry out sustainable practices in manufacturing, AI can guide businesses around their most energy-consuming areas and equipment, and prompt them to make positive eco changes before it becomes a mandatory (and expensive) switch.

Artificial intelligence has become synonymous with manufacturing – and not just in building physical products. It is facilitating efficiency, safety, and sustainability as a whole in the sector. And yet, we’re still in the early days of realizing what AI can do for the industry. Manufacturing companies that haven’t embraced the potential of AI need to do so now to reap the AI benefits today, as well as the AI innovation of tomorrow.