James Finch, chief operating officer of Eigen Innovations, shows the equipment his company installs in industrial production facilities for organizations of all sizes across Canada. This AI-powered technological transformation is taking hold of even the most traditional industries, including manufacturing.Steve MacGillivray
Let’s say you own a small factory that makes parts for the automotive industry. Before your car widget is shipped to customers, the last step in production is quality inspection.
Until recently, this job had to be done by one of your employees, individually scrutinizing each piece to make sure it was up to standard. It was labour intensive, often onerous work that made it hard to recruit people willing to do the job – and if something sub-par slipped through and made it to market, it could be a liability for your bottom line.
Now that artificial intelligence solutions are increasingly accessible, businesses in traditional industries such as manufacturing are riding the wave of digital transformation.
Fredericton-based Eigen Innovations develops software that enables automated inspection using machine vision – when a camera interprets data and makes decisions based on what it ‘sees’ – and AI. Its technology enhances a factory’s inspection process, whether it’s fully automating the workflow or assisting an employee throughout.
“Then there are parts of processes where it’s not possible to have a person inspect,” says James Finch, Eigen’s chief operating officer. “For example, we do a lot of work with thermal imaging, which is typically looking at things that an operator could not see anyway.”
In practice, this looks less like a robot picking up every individual piece, and more like industrial-grade cameras mounted on the assembly line, taking pictures of every component, and then accepting or rejecting them by comparing them with an AI model that has been trained on what to look for.
“Once you’ve developed an application with a particular customer, one of the advantages of AI is that it can be much more scalable than if you were going and deploying custom vision systems,” Mr. Finch says. “If a customer has 10 lines of a similar process, once we’ve got the first one on the first line, there’s a scalability aspect to carrying it over to the other lines.”
Eigen Innovation, which works with multinationals and small businesses alike, says automating quality inspection using AI can have a significant impact, whatever the size of the firm.
“You’re certainly reducing any quality concerns from end customers. You’re also reducing scrap on the line,” says Mr. Finch, who adds his company has also been able to help increase the “line rate,” or output. “Overall, it’s a productivity advantage.”
It’s particularly helpful in an industry that has struggled with labour shortages, he says. “If you’re having trouble finding or retaining labour, you want to have the folks on your team working in areas that add the most value to the product. If you can automate inspection and re-allocate inspectors to more productive areas, or have them contributing to assembly rather than verifying parts, it helps ease the labour challenge and gives the operators potentially more interesting work.”
Small businesses in Canada are increasingly using AI to close gaps left by labour shortages or cost-cutting measures.
Winnipeg-based Farmers Edge is revolutionizing soil testing, a cornerstone of agricultural decision-making that helps farmers know exactly what kind of dirt they’re working with. For example: Do they need to add certain nutrients? Is there a PH imbalance that could impact the health of their crop?
“Soil testing guides fertility, crop input efficiency, and ultimately yield at the end of the day,” says Kris Kinnaird, director of farm and retail growth at Farmers Edge Laboratories, the soil-testing arm of a business that also offers end-to-end managed technology services.
He explains that traditionally, soil testing is done by going out into a field and taking a one-inch probe’s worth of soil that gets sent to a lab for analysis. Farmers Edge, on the other hand, uses predictive software powered by AI that’s trained on more than 25 million tests processed over the past 15 years to perform what’s called a “virtual” soil test.
The process starts with a real soil sample from the farm, which is then fed various inputs – such as the weather, whether any nutrients have been added, or what the last crop yield was – to further train the model as time goes on. This enables a farmer to make crucial decisions without physically testing the soil in a field in a given year, because the technology can predict outcomes based on that initial real soil sample, current conditions and the trends observed in those millions of other tests that feed the AI model.
“The outcome of that is a more efficient path to getting results. Although soil testing is so important, it’s often the first item that’s cut when the budgets are tight,” Mr. Kinnaird says. “Right now, the economics of agriculture are very tight. Land and crop inputs are very high, commodity prices are very low.”
These AI-driven virtual soil tests mean farmers have an alternative way to access crucial information. “And not an alternative that’s guessing,” adds Mr. Kinnaird, who says his company has been using this model for about seven years, trialling it on more than 10-million acres.
It is a much more cost effective option to use a virtual model after taking just one physical soil sample, as opposed to doing the physical test every year. “It’s getting the same output you would get from that actual soil test, but you may save 50 per cent of what you would have paid to go collect that soil from the field,” he says, pointing out that it can also help farms looking to scale and grow. “Using a virtual soil test and a predictive soil model, they could maintain their current investment but get maybe two times the results.”
These Canadian businesses are seizing on a particular opportunity when it comes to digital transformation within more traditional industries, says Ahmad Ovais, a partner in consulting at BDO Canada.
“Many of the small to medium-sized organizations in these sectors have had relatively lower data and AI investments, making them rich with modernization and automation opportunities. For example, enhancing customer service with 24/7 support, improving operations through predictive maintenance, automating processes to reduce downtime, and generating insights for rapid financial or operational planning.”
It’s something Mr. Ovais is seeing first-hand in his own work with Canadian companies. “For example, one of our Canadian manufacturing clients was struggling with sales and product support staff manually reviewing thousands of customer requests, leading to slow response times, lower service quality and missed opportunities.
“To solve this, our data and AI team built a personalized AI solution to automatically intake, extract and analyze customer-specific requests, assess customer intent, and modernize the business process. This delivered significant efficiencies and improved the customer experience.”
As AI improves and becomes more ubiquitous, digital transformation is also increasingly becoming a non-negotiable for small to medium-sized businesses that want to remain competitive, Mr. Ovais says.
“Competitors who act now will lower costs, serve customers faster and attract the next generation of talent who expect digital tools,” he explains. “The real risk is being left behind – losing customers to better experiences, losing staff to more modern firms, and losing margin to businesses that can do more with less.”
Even in the most traditional industries, Mr. Ovais concludes, ignoring the potential that AI holds can be likened to “refusing to switch from horse-and-buggy when rail cars became affordable – the longer we wait, the harder it is to catch up.”