Automation: Delivering a New Era of Manufacturing

As the world of technology continues to change, with the potential to transform all industries, manufacturers look for advancements that will help them become more efficient and agile to stay ahead of their competitors and the needs of consumers. As operations begin to recover from the pandemic, the question of how to handle future disruptions is a top concern for all manufacturers. One solution is deploying automation and artificial intelligence (AI) to keep their operations lean, efficient, and productive.

AI in manufacturing is expected to grow at a CAGR of 57% to a value of $16.7 billion by 2026. The growth is mainly attributed to the ongoing transformation in manufacturing, as the Internet of Things (IoT) plays a vital role in adopting AI-based technology. Utilizing AI, automation, and IoT is one of the most critical factors driving post-pandemic manufacturing operations forward. AI-based solutions are essential for helping manufacturers to embrace the digital transformation of their operations.

 

Staying agile and efficient became an issue to solve as the pandemic brought lockdowns, disruptions to supply chains, and labor shortages. Many small and medium-sized companies now understand that automation is essential to proceeding in post-pandemic manufacturing.

 

“There is no question that AI and automation have disrupted the industry,” says Gregory Powers, vice president of digital transformation at Gray Solutions, a Gray company. “From food manufacturing to food service and retail, many companies have already started implementing AI and automation in a variety of ways. What’s more, the advent of COVID-19 has further accelerated adoption and investment in this technology.”

"From food manufacturing to food service and retail, many companies have already started implementing AI and automation in a variety of ways. What's more, the advent of COVID-19 has further accelerated adoption and investment in this technology."
Gregory Powers, Vice President, Digital Transformation, Gray Solutions, A Gray Company

Barriers to Adoption

 

A significant barrier to adopting AI and automation by small and medium companies is the cost of implementation. Companies typically need to invest a large amount of capital upfront for their automation technologies and robots. Many manufacturers are reluctant to do so, especially coming out of the pandemic and running on lean margins.

 

“The key factor is time,” says Drew Goodall, vice president of process integration at Gray Solutions, a Gray company. “If owners are not leveraging these solutions already, when will they be? How far behind their competition do they currently sit? The cost of not adopting these solutions is high.”

 

Robots as a Service

 

Robots as a Service (RaaS) is a good solution to the up-front capital hurdle. It offers a pay-as-you-go plan that allows smaller companies to fund their automation needs, according to what they can afford.

 

With no up-front capital expenditures or concerns about immediate return on investment, scaling up or down is easy to do. This business model is fueling more widespread adoption of automation.

 

Google’s Cloud Robotics Core, Amazon’s AWS RoboMaker, Honda’s RaaS Platform, Fetch Robotics, and Cobalt Robotics are all examples of RaaS.

 

“The robotics market will jump from $115 billion in 2018 to $201 billion by 2022,” says Powers. Many of these robots will be digitally connected to a cloud platform to improve operational efficiency and agility. By the end of 2023, “50% of enterprise customers with robots will switch to third-party robot maintenance providers for faster response, higher availability, and better cost-effectiveness. This makes the case for RaaS very compelling,” adds Powers.

"If owners are not leveraging these solutions already, when will they be? How far behind their competition do they currently sit? The cost of not adopting these solutions is high."
Drew Goodall, Vice President, Process Integration

Gray Solutions, A Gray Company

Last-Mile Delivery

 

Solving the challenges of last-mile delivery has been a priority for logistics companies even before the pandemic. The growth of e-commerce and the ongoing labor shortages across the distribution sector have increased the demand for automation and AI to fill the gap.

 

Last-mile automation in manufacturing has become an emerging multi-billion-dollar market, with same-day delivery expecting to reach a 25% market share by 2025.

 

The rise in online orders has increased the focus on last-mile delivery and contributed to the growth of many distributors in this sector. A significant number of customers willingly pay additional charges for quick delivery, which has expanded the last-mile delivery market and the search for innovative improvements.

 

For example, FedEx recently announced a multi-year, multi-phase agreement with Nuro to test its next-generation autonomous delivery vehicle within FedEx operations. FedEx believes that this and other automated equipment will improve safety, efficiency, and productivity.

 

“FedEx was built on innovation,” says Rebecca Yeung, vice president of advanced technology and innovation for FedEx Corporation. “We are excited to collaborate with industry leaders like Nuro to explore the use of autonomous technologies within our operations.”

 

However, to successfully automate last-mile delivery, robots need deep-learning capabilities created by a combination of technologies including machine vision, AI, cloud computing, edge computing, 5G, sensors, and Industry 4.0 hardware—making the last mile a key innovative focus within the robotics industry.

 

Advanced vision systems incorporated with robots and automated guided vehicle (AGV) technologies have made it easier to improve last-mile delivery. “As more data are analyzed, and algorithms become more sophisticated, adoption of this technology will become more mainstream,” Powers says. “The advanced vision systems are really where AI has been adopted. Repeatable processes, teaching the software what to look for, and applying learning algorithms in the code have given the vision systems the lead on AI.”

 

Moving Forward with Deep Learning

 

COVID has brought about labor shortages, and companies are desperate for workers—another reason to deploy automation and robotics.

 

According to Goodall, “many existing manufacturers are already capturing and archiving troves of production data”—typically through the use of  Internet of Things, AI, and deep learning, which is an  AI function that imitates the neural functions of the human brain when it is processing data and making decisions. Deep learning is predicted to grow dramatically in the next few years. When combined with other Industry 4.0 technologies, deep learning will take AI and automation to the next level, resulting in even greater production efficiencies for manufacturing companies.

 

AI and automation are powerful technologies that, when deployed in manufacturing operations and now with the ability for smaller companies to use those technologies through RaaS platforms, will harness the efficiencies that they bring to operations creating a new era of manufacturing success.

    Some opinions expressed in this article may be those of a contributing author and not necessarily Gray.

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