The Top 5 Applications of AI in the Supply Chain: Part 2

Peter Jackson, Matrix Chief Digital Officer, explores the way AI could positively impact the supply chain in part 2 of our AI focused blog.

It’s an exciting time for supply chain businesses as AI starts to be a viable way to improve efficiencies, reduce costs and gain competitive advantages, but it is still an emerging tech surrounded by a lot of hype. It’s not a silver bullet and implementing AI requires access to quality real-time data, investment into data scientists and developers, deployment of sensors and more.

The most important step any supply chain business can take now to implementing an AI strategy is to identify their biggest challenges, understand what data they have available throughout their organisation and find a good data scientist to start organising, modelling and training the data to help solve these challenges.


4. Logistics automation

“Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost” 2016

A logical application for AI in the supply chain is autonomous vehicles for logistics and shipping. AI will not just be able to control vehicles but also calculate the most efficient routes and react quickly to any unforeseen circumstances. For businesses, this translates to shorter lead times, fewer delays, and decreased transportation costs, as well as a more environmentally friendly solution due to the increased efficiencies. It’s now feasible for a fully autonomous lorry to be packed by a warehouse robot and delivered to another warehouse without a human being involved.

5. Manufacturing

It is now not uncommon to hear about factories where all the manufacturing is carried out by robots that not only build the products but also build, test and inspect themselves.  As AI has advanced some factories are now evolving – their robots no longer perform monotonous mechanical tasks (e.g. screwing a cap on a bottle) but are intelligent and can learn to dynamically evolve and collaborate with other robots in the factory to autonomously come up with better ways of making a product. In the future, intelligent factories will likely build, maintain and evolve on their own.



Part one of the blog looked at the first three points; how AI could impact predictive demand forecasting, product development and product inspections, which you can read now.