Taiga Building Products uses AI for its supply chain
Taiga Building Products’ supply chain optimization plans have great help on artificial intelligence for automation.
Artificial intelligence and machine learning provider Adastra announced that it has secured a $1.1 million investment from SCALE AI, Canada’s supercluster dedicated to strengthening the country’s leadership role in the areas of artificial intelligence and data science, to work on the Taiga project and be part of the just-in-time inventory model.
Taiga operates 15 distribution centers in Canada, 3 distribution centers in the western United States and 6 charging stations in the eastern United States.
“The overall goal of this initiative is to use readily available data inputs in disparate systems and employ advanced AI techniques to solve three of the most pressing supply chain bottlenecks in building materials: accurate demand forecasting, maximizing truck loads, and optimizing warehouse layouts,” said Adastra.
After the pandemic and the resulting supply chain issues, which have hit building materials as hard as the impacts felt by other industries, the pursuit of performance improvements is a natural desire.
However, this raises a question that goes back at least 20 good years. Is the focus on just-in-time inventory still good? Or does it create significant problems?
For decades, supply chain experts have said that too many companies care about just-in-time inventory management because by reducing lead times and keeping less on hand, companies were able to reduce the costs of their balance sheets, which made them seem much more efficient.
But true just-in-time, or JIT, requires detailed information about all parts of a supply chain. The need is not just to address bottlenecks, but to notice when problems potentially increase, and then bring in more inventory to cover signs that suggest availability might become tight.
That doesn’t mean Taiga has this issue to deal with. But it is prevalent in many businesses and industries.
The worry is that technologies like machine learning can turn into tools to, as they say in high tech, pave the way for cows. Simply put, businesses frequently use technology to do things the way they always have, but faster and more efficiently, assuming that increased speed can make old ways work.
Except perhaps the greatest benefit of a new system is the ability for a business to rethink the way it has always undertaken its operations and strategic planning. Doing things the way you’ve always done leaves a business 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.