Manufacturing The Future: AI’s Role In The Factory
Sean McGowanMarch 21st, 20183 minute read
Sean is a technical researcher & writer at Codal, authoring blog posts on topics ranging from UX design to the Internet of Things. Working alongside developers, designers, and marketers, Sean helps support the writing team to ensure Codal produces engaging web content of the highest quality. When not writing about the latest innovations in app design, Sean can be found cooking, watching old movies, or complaining about the shortcomings of his favorite Philadelphia sports teams.
While artificial intelligence has always captured our imaginations, it wasn’t until recently that practical, usable AI tools entered the cultural mainstream. Virtual assistants like Alexa, Siri, and Cortana have quickly become household fixtures, and self-driving cars are all but on the cusp of broader adaptation.
But while we may just be seeing the impact of artificial intelligence on our everyday lives, AI has been quietly revolutionizing other industries for years. Among these sectors, perhaps the one AI has been most prevalent in is manufacturing.
“Smart manufacturing”, or the use of AI and other IoT technology in the production of goods, has been streamlining, expediting, and improving nearly all aspects of traditional manufacturing practices for years now. After surpassing the $200 billion mark in 2017, Trendforce estimates the global smart manufacturing market will increase to over 320B by 2020.
The adoption of artificial intelligence is the natural progression for an industry that has historically relied on cutting-edge technology in the fields of robotics, software, and mechanization. But how exactly are the leading manufacturers harnessing AI, and what are these industry leaders doing to position themselves for continued success in the future?
As an AI and software development agency, Codal examined some of our own work, as well as larger trends in the manufacturing sector, to find out.
Traditionally, the maintenance, repair, and upkeep manufacturing equipment (or the product itself) are time-based. Replacement orders or general servicing are scheduled at regular intervals, whether the machinery actually requires it or not.
This doesn’t just waste labor and add costs—it also fails to address the risk of entirely unexpected or accidental damage, rather than just general wear and tear. By harnessing artificial intelligence, coupled with the appropriate sensors and a robust analytics platform, software development companies can deliver the digital solutions for predictive and proactive maintenance.
Perhaps the best-known example of this breed of digital solution is GE’s Predix, an industrial cloud-based platform that allows manufacturers to remotely monitor the uptime, reliability, and other key performance data of their machinery.
Solutions like Predix allow manufacturers to leverage both physical and virtual technologies in their digital strategy—namely, IoT sensors and machine learning—to automatically determine when equipment actually needs maintenance.
Despite the technological leaps and bounds made in making the factory floor a safer working environment, the addition of some manufacturing robots has presented a new risk. Without a robust AI guiding system or sensors, these automated machines could potentially crash into objects, other equipment, or even people.
But with recent advancements in machine vision and sensing, underpinned by the latest in artificial intelligence, this problem becomes infinitely more solvable. More and more AI systems can be trained to sense what is happening around them, even when that perception can’t be translated into rule-based responses.
A market for collaborative robots, or “co-bots”, has also emerged in the manufacturing sector. Improved artificial intelligence allows co-bots to receive and understand human commands, including instructions foreign to the machine’s native programming.
A More Connected Supply Chain
While most of the AI technology discussed in this article has concerned the internal side of the manufacturer, it also can be a significant boon for external operations, and the supply chain as a whole. While still in the early stages, one of the most exciting ways this could be implemented is through demand-driven production.
By connecting consumer-facing IoT devices with industrial ones, AIs can collect real-time data, project market demand, and set production metrics accordingly. This data-driven approach could inform material sourcing, staffing, fiscal decisions, inventory, energy consumption, and more. By directly tapping into the data being generated by consumers every day, manufacturers can help cut costs across the board.
The Bottom Line
From smart production and safety to an end-to-end supply chain, AI is already shaping the manufacturing industry and will continue to do so in the foreseeable future. In fact, an recent study showed that more than half of surveyed manufacturers have actively been using AI in their workflows for one to three years already.
And of these forward-thinking manufacturing companies, their AI investments have gone into operations across several different avenues. Forty-four percent of AI-adapted manufacturers were focusing their efforts on data analytics, while thirty-one percent were using artificial intelligence in their operations departments.
If you’re interested in learning about how software development agencies partner with manufacturers to equip them with the tools they need for the future, reach out to Codal today.