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My Robot Brings All the Boys to the Yard, Its AI is Better than Yours

In this special guest feature, Aviran Yaacov, CEO, and Co-founder of EcoPlant, believes that both AI and ML technologies are making impactful strides in manufacturing, and there is no time like the present for manufacturers to get on board and explore ways to transform their processes to benefit across all fronts. Aviran has over ten years of experience and expertise in operations, finance, sales, and people management in the IT industry. Before his current role, Aviran was a Senior Sales Executive for a SAP Business One integration firm. He is part of the management team in Ecoplant since it was established in 2016. From the bootstrapping stage, he oversaw business development in the company. He generated partnerships with Ecoplant’s solution with large corporations including Ecolab, Dannon, Nestle, Unilever, and Hill-Rom.

Robots and machines are already everywhere, especially in manufacturing. However, many experts predicted they would have advanced faster than they have. The truth is bringing automation and dynamic controlling into the physical world turned out to be much more challenging than was previously assumed. But with state-of-the-art AI and machine learning (ML) available today, the leaps are getting larger by the day. The technology might be new, but its implementation will have various effects on manufacturing. 

Better than ever before

Thanks to AI and ML technologies, machines can now learn to handle a wide range of objects and tasks on their own. These enhancements are a far cry from the robots of yesteryear, which simply performed monotonous tasks. Machines are now capable of being endowed with greater levels of intelligence to acquire new skills autonomously, and to generalize unseen situations. It’s a true game-changer for the manufacturing industry as a whole in the following ways:

  • ML can teach robots and machines how to do high-level planning in ways they couldn’t before, and which come natural to humans.
  • Improving the process of training robots is scalable in ways that training humans isn’t.
  • AI and ML enables predictive maintenance, the capability to determine performance, breakdown and operating conditions of equipment or machine on a real-time basis.
  • AI and ML can engender collaborative robots (cobot). Cobots can observe what a human operator is doing, learn the task, and autonomously start performing.

Newer machines can now handle a much wider range of objects and tasks like never before. For instance, 3D industrial cameras are taken to new heights with the backing of AI, as it can help machines determine depth and distance, and general image recognition in a way that was formerly exclusive to the human eye. 

Since ML closely resembles human learning, the need for human intervention (such as for the creation of new programs or updates) becomes reduced as the machines are capable of handling new parts on their own. Since information is generally stored on the cloud, robots can learn from each other through shared knowledge. As more data is gathered through operation, accuracy also increases and becomes more enhanced. This translates to less of a need for surrounding equipment (such as shaker tables and feeders) to be needed for each robot, which plays a major role in savings and scalability for manufacturers. 

In addition to scalability, manufacturers can also enjoy the benefits of energy efficiency with machines that are optimized accordingly. Through the usage of predictive AI algorithms to conduct ongoing energy surveys and dynamically control each air compressor, and the whole system, manufacturers can dramatically reduce the carbon footprint of their facilities.

Humans and robots joining forces

Robots are now capable of doing far more than grasp and assemble objects. They can make their own decisions and solve problems based on their skill sets, while human operators solely focus on high-level commands. While these developments, paired with sci-fi movies, may make it appear as though robots are going to take over the world and take jobs away from humans, that isn’t necessarily the case. 

They simply help humans do their jobs better. 

The best results come from the pairing of human intelligence with machine intelligence. Humans bring creativity and ingenuity, while industrial robots bring speed, strength, and accuracy. As summed up by Patrick Sobalvarro on WeForum, “The idea of a fully automated ‘lights-out factory’ with no production workers—one requiring only machine programming and maintenance—has proven to be a dead end. So much of what happens in a factory requires human ingenuity, learning, and adaptability. As products have become more varied and customized to local markets and customer needs, the economics of full automation make no sense.” With the support of necessary regulatory oversight, machines with AI-based components can also enable sustainable development, thereby helping manufacturers dramatically reduce the carbon footprint of their facilities.

The post-pandemic world sparked many changes in manufacturing, not only for the health and safety of workers, but also to ramp things up in supply chains in response to ever-changing market needs. In order to stay relevant and compete in the evolving global market, manufacturers need to transform the way they produce their products. The most complex challenges stem from demands for higher product variability, mass customization, quality expectations, and faster product cycles. This is all the more reason why manufacturing processes are faster, more efficient, and more cost-effective when humans and robots work together. 

While the advantages of humans working together with robots were known well before the pandemic, the crisis made the pairing crucial as manufacturers began to reopen their facilities, for improved productivity, quality of output, and working conditions. 

Both AI and ML technologies are making impactful strides in manufacturing, and there is no time like the present for manufacturers to get on board and explore ways to transform their processes to benefit across all fronts.

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