Taiga Building Products Using AI for Its Supply Chain

Taiga Building Products’ supply chain optimization plans are getting big help on artificial intelligence for automation.

AI and machine learning vendor Adastra announced that it was awarded a $1.1 million investment by SCALE AI, Canada’s supercluster dedicated to strengthening the country’s leadership role in the fields of artificial intelligence and data science, to work on the Taiga project and become part of that company’s just-in-time inventory model.

Taiga operates 15 distribution centers in Canada, 3 distribution centers in the western U.S., and 6 reload stations in the eastern U.S.

“The overall goal of this initiative is to use data inputs that are readily available in disparate systems and use advanced AI techniques to solve three of the most pressing bottlenecks in the building materials supply chain: accurate demand forecasting, maximizing truck loads, and optimizing warehouse layouts,” said Adastra.

After the pandemic and ensuing supply chain problems that hit building materials as hard as the impacts other industries felt, looking for improved performance is a natural desire.

However, this raises a question that goes back a good 20 years at least. Is a focus on just-in-time inventory always good? Or does it create significant problems?

For decades, experts in supply chain have said that too many companies showing interest in just-in-time inventory management, because by reducing lead times on inventory and keeping less on hand, companies were able to take costs off their balance sheets, making them look far more efficient.

But real just-in-time, or JIT, requires extensive information into all parts of a supply chain. The need is not just dealing with bottlenecks but noticing when problems are potentially growing and then bringing in more inventory to cover when signs suggest that availability could become tight.

This isn’t to suggest that Taiga has this issue to deal with. But it is widespread in many companies and industries.

The concern is that technologies like machine learning can get turned into tools to, as the saying used to go in high tech, pave the cow paths. That is to say, companies frequently use technology to do things they way they always have, only faster and more efficiently, with the assumption that added speed can make the old ways work.

Except perhaps the biggest advantage of a new system is the opportunity for a company to rethink how it’s always done undertaken its operations and strategical planning. To do things as you’ve always done them leaves a company locked into variations of what it’s always done. When the traditional meets a major disaster, it can become clear that something different was needed all along.