How AI Is Shaping the Future of Manufacturing

The power of artificial intelligence to overturn every aspect of contemporary life has been simmering in the wings for many years now. Yet, it has been the transition from 2022 to 2023 which has borne witness to a sudden and powerful advancement in both its applicability and global impact.

For many, the powerhouse behind this explosion has been the now renowned artificial intelligence company OpenAI, debuting both their image generation platform DALL-E and their text generator Chat GPT. Both have skyrocketed in popularity, not just within technological circles, but also across the mainstream, too. 

However, this is not the only form of mark that AI is making on the world.

When the phrase ‘digital manufacturing’ is uttered, many may think most immediately of the image of an automated production line, continuously passing products from machine to machine Though this is certainly one use-case for autonomous, AI-driven technology, there are many more different stages of the workflow which are undergoing intelligent permeation. 

In this article, we dive into some of the most innovative and diverse ways in which artificial intelligence is being equipped in manufacturing contexts, industry-wide. 

1 – Cyber Security

Although IIoT, cloud computing and other network-based innovations have pioneered unforeseen efficiency and productivity in manufacturing sectors, the rising implementation of these systems come as a double edged sword. Indeed, ironically, the reliance of businesses on these methods can result in fatal downtime with just as heavy an impact. 

Why is this the case? Put simply, the broader a digitally connected ‘web’ of devices and data becomes, the more susceptible it can be to cyber security attacks. In fact, numerous studies show cyberattacks to be the greatest threat to digitally based businesses, leading to the largest loss of money, due to the sheer level of disruption which widespread digital failure can catalyse. 

The breadth of data exchange necessary to protect makes it exceedingly difficult, if not impossible, to manually ward off cybersecurity hazards. As a result, this digital danger calls for a digital solution.

Artificial intelligence can serve as a powerful tool to pinpoint vulnerabilities in the IIoT network and identify threats or attacks before they can wreak widespread havoc. Able to regularly trawl through immense datasets and across ever widening webs of interconnected devices, this technology’s implementation is making an enormous difference in bolstering manufacturing operations, especially in high-security industries, such as defence and aerospace.

Nonetheless, there remains room for improvement. Although AI may be able to powerfully counter cyberattacks, its digital nature also inherently makes it vulnerable to exactly the same risks as the data it protects. The future of establishing impermeable cybersecurity in manufacturing, then, relies on the establishment of innovative and essential mitigations against AI’s own infiltration. 

2 – Customisable Automation

One of the most prominent characteristics of manufacturing sectors on the whole are their changeability. Inherently intertwined with the contingencies that face our world, whether regarding smaller scale, business specific circumstances, or unexpected global phenomena such as geopolitical complexities or global pandemics, it is critical that adaptability is built into manufacturing business models. 

For this reason, the agility of AI is becoming increasingly valuable as an asset, particularly where customisation is possible. According to a Forbes interview with Jensen Huang, the CEO of artificial intelligence company NVIDIA, “businesses [are becoming] interested in manufacturing their own company-specific AI”, with there being “a monumental business opportunity for customisable AI applications”. 

But what does it actually mean for AI to be customisable?

Rather than following preset rules, necessitating the adaptation of businesses to inclinations and functions they have no control over, user-configurable AI enables companies to shape artificial intelligence to their own business models and ends, allowing them to reap company-specific rewards.

Digital employees offer a prime example of this phenomenon. In light of the labour shortage continuing to grasp manufacturing sectors industry-wide as we forge a path into 2023, the ability to create custom workers using artificial intelligence is changing the scope for productivity.

From decreased turnover rates to heightened engagement from human employees, no longer required to direct their attention towards dull, repetitive tasks, the ability to shape AI around which operative gaps need to be closed is proving to be extremely profitable. 

3 – Actionable Data-driven Insights

Although digital workers shape value by adopting typically human-led jobs, AI can also provide value as a stimulus for human innovation, particularly where driving important business decisions is concerned.

Big data collection and analysis is precisely what AI feeds on. As CSU global explains, “AI systems work by combining large sets of data with intelligent, iterative processing algorithms to learn from patterns and features in the data that they analyse”. As a result, AI is positioned just as perfectly to relay their findings to humans for human assessment as they are to independently learn from them. 

Most advantageously, the insights afforded by AI would be near impossible for humans to source independently, or at least in such a short amount of time. By the time that a human was able to identify a pattern in an ever growing dataset, it is likely that the circumstances owing to the pattern would have moved on, limiting the opportunity to grow from them. 

On the other hand, AI can reach into the ever flowing well of data and extract the information that matters most, from anticipating emerging industry trends and identifying subtle but powerful consumer preferences to predicting the upcoming malfunction of machines.

Autonomy is, ultimately, the most important value-add of AI. However, the type of ‘autonomy’ which such technology can instil is varied, ranging all the way from smart automation to the increased cognizance of business owners regarding the effectiveness, success and direction of their operations. 

Can Chat GPT Be Used in Manufacturing?

Manufacturing-specific AI is growing and undeniably thriving, particularly in light of the accelerating Industry 4.0 movement. However, it remains interesting to consider whether more popular forms of artificial intelligence can also extend advantages within a manufacturing context. 

Perhaps unsurprisingly, much of the benefit which this tool can offer to manufacturing sectors surrounds streamlining B2B communications, such as automating chatbot responses, or generating necessary documents, from safety guidelines to machine manuals. 

However, digging deeper into potential use-cases, many might argue that ChatGPT could also be used to streamline production processes themselves. If hooked up to historical and live machine data, the platform could generate extremely context-specific notifications and warnings for operators, infusing transparency into real-time progress reports. 

Furthermore, looped directly into IIoT, Chat GPT could also be used to raise business suggestions such as technical process improvements, drawing articulated conclusions from raw process data. 

It is worth noting that, at the time of this article’s writing, Chat GPT cannot be entirely relied on for complete accuracy. As Ian Bogost writes for The Atlantic, “GPT and other large language models are aesthetic instruments rather than epistemological ones.” However, as its functionalities, rhythms and capabilities solidify, perhaps its application within manufacturing contexts to will become more widespread as its precision is honed. 

A Glance Into The Future

Digitisation is no longer just a ‘trend’ in manufacturing. Rapidly, it is becoming considered mainstream. With artificial intelligence becoming more accessible and consequently more profitable, we are undeniably entering into what Google Cloud dubs “the golden age of AI”, tying it into this industry-wide movement.  

However, the depth and breadth of AI’s applications within manufacturing also inevitably creates an environment of constant growth and exploration. In fact, with the datasets used by AI existing in a state of constant flux, artificial intelligence is designed to keep extending and unfolding this ‘golden age’ of discovery. 

AMFG, having announced its development of the first solution for autonomous manufacturing, stands at the very centre of this evolution. Find out how you can digitise your workflows and take control over your business success today. 

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Enjoyed this? Check out our previous article, ‘Industry 4.0 vs. Industry 5.0: Understanding the Real Difference’.