Most people's ideas concerning artificial intelligence or AI stem from science fiction and some vague notions about smart technology.
When people hear the term ‘AI’, they usually conjure images of robots thinking and feeling like humans, although current technology is still a long way off.
Experts are still figuring out the complex workings of algorithms and machine learning, and are developing technologies along the way. However, this has not stopped us from experiencing the many uses of artificial intelligence in our daily lives and in business.
But what is AI all about?
Artificial intelligence (a.k.a. machine intelligence) refers to a branch or field of computer science that aims to give software or programs the ability to collect data and correctly interpret that external data. The analyses AI makes are based on pre-set conditions and algorithms or machine learning pattern recognition models. AI then makes decisions based on those analyses.
The concept of artificial intelligence has been around since the 1940s and 1950s, although the field of AI was officially founded as a branch of study only in 1956. However, we have only been recently experiencing the benefits of AI in everyday life.
We see AI at work on some mobile phone applications, such as built-in smart assistants like Alexa, Siri, Google Assistant, and Bixby. Even the contents of our social media feeds are influenced by AI that collects data on our preferences, social media interactions, etc.
Then there’s smart home technology and the Internet of Things (IoT) where Alexa and Bixby come to mind — orchestrating everything: when the lights, home security system, and thermostat should turn on and at what settings, based on the data they collect from users’ everyday behaviour and choices.
AI in manufacturing
Manufacturing has and continues to benefit from advances in technology, including AI. Still, not all manufacturers have readily embraced recent technological innovations, despite the following uses and benefits of AI in manufacturing:
- Predictive maintenance: One of the most common AI implementations in manufacturing involves the maintenance of machinery and production assets. Predictive maintenance utilises data collected from various information points, such as maintenance records, machine sensor data, and weather data, to predict when a machine would have to be serviced. By leveraging real-time asset data combined with historical data, machine operators can make data-driven decisions as to when a machine needs to undergo routine maintenance or be repaired. By doing this, manufacturers can ensure that mission-critical assets continue running at peak performance and minimise or eliminate unplanned downtime.
- Real-time in-line inspection: Image analysis during real-time product quality inspections helps manufacturers ensure they comply with stringent regulatory requirements, particularly in the automotive and consumer products segments. The availability of more affordable high-resolution cameras and AI-based image recognition software and technologies is paving the way for more widespread adoption of real-time in-line inspection.
- Design efficiency and accuracy: The use of AI in designing new automotive models in real time helps to reduce time-to-market for the launch of new model series. With the aid of AI and design software, these systems have the ability to create completely new models that comply with current industry standards and regulatory requirements.
- Asset defect recognition system: By combining human expertise, ingenuity, insight, and AI technology (e.g., machine learning, computer vision, predictive modelling), manufacturing centres can achieve unprecedented levels of quality control. These help drive greater accuracy and efficiency in testing high-precision machinery parts, particularly in automotive and transportation, aerospace and defence, energy, and construction. This technology allows for the intelligent analysis of inspected parts, automatic detection of possible defects, and correcting flaws before the parts can be deployed and used.
As mentioned earlier, not all manufacturers have adopted AI. Small and medium-sized manufacturers (SMMs), in particular, have been slow to adopt innovative AI technology. This hesitation stems from inadequate information and concerns regarding affordability, security, and scalability.
What manufacturers don’t know about AI
Although there’s been a lot of talk regarding the many ways different industries can benefit from the early adoption of AI, most of the buzz tends to involve key industrial players that have the capability to invest in and experiment with new technologies.
But this is not to say that SMMs are restricted from taking advantage of AI and harnessing its capabilities to achieve their own production and business goals. If you’ve been thinking of integrating AI technology into your operations but are uncertain about what steps to take, consider the following:
- AI adoption does not have to be expensive. As with any type of technology, investments are necessary for a company to know and experience the benefits offered by new equipment, tools, devices, or machinery. When it comes to AI implementation, there’s absolutely no need to go ‘wholesale,’ so to speak. You can adopt AI gradually in small, manageable, and affordable ways. If you’re considering using several families of AI-enabled machines, there’s no need to acquire these all at once. Instead, you can focus on the acquisition and rollout of one machine family and master its use before moving on to the next.
- Utilising intelligent machines doesn’t require you to have in-house IT experts. If you do not have a resident IT team that specialises in AI technology, it doesn’t mean you can’t adopt AI now. Your suppliers or vendors would likely provide tech support for the machinery or equipment you purchase from them. Your machine operators might not even need specialised training to operate the new equipment, other than knowing what certain alerts or notifications are for. The basic knowledge is there, so there’s no fundamental need to have internal AI experts. However, you do need someone who understands the KPIs for such technology to oversee the entire process.
- Integrating AI into your operations is something you shouldn’t postpone. SMMs that have difficulty implementing AI technology in their manufacturing operations oft think they are unprepared for it. Some may claim to have AI adoption as one of their goals, but they usually leave it on the back burner. Remember, technological innovation happens at lightning speed, and the longer you tarry, the more you’ll be left behind. In fact, once you start using AI technology, you just might realise that had you taken decisive steps much sooner, you could have avoided expensive design flaws, unexpected downtimes and periods of low production, and safety issues on the floor.
The great thing about new technologies in general, including AI, is that they are designed to make work easier, more efficient, and a whole lot safer. New technologies, when coupled with the increasing availability of more affordable and durable materials, components, and products, also make it much easier for businesses to innovate and improve their operations.
AI adoption — a must for manufacturers
Martin Thomas, European Marketing Manager at Radwell International Ltd explains more. “Early adopters of AI technology have benefitted from and continue to harness its power.
With AI on your side, it becomes possible to come up with flawless (or nearly so) product designs. You can also boost productivity, achieve quality consistency, increase the functional lifespan of your equipment, improve overall equipment effectiveness, minimise unplanned downtime, and make data-informed decisions and forecasts.
Besides, as AI adoption becomes more mainstream, what may be considered disruptive technology now will someday be an operational standard. Therefore, it makes sense to start utilising the technology sooner rather than later so you don’t get left behind.”
