What role does artificial intelligence and machine learning play, when it comes to people receiving their goods when promised?
Have you ever thought about all the steps that take place when you’re buying a piece of clothing online?
From clicking the ‘Add to Cart’ button, to inserting credit card details, to then having your goods shipped to the location of your choice.
It may sound simple, but there are many – arguably complex – micro-processes that need to be specifically completed in order to achieve the ultimate goal.
So, what role does artificial intelligence and machine learning help to play in executing all these processes, considering the supply chain crunch being experienced over recent years?
Raghav Sibal from Manhattan Associates argues systems should have something called ‘Interactive Inventory’.
“As (browsers) are selecting which item colour and size to pick in the cart, they want to know when exactly it’s going to be delivered, or when it can be picked up,” he explains.
“Rather than waiting until the end of the transaction, go to the checkout screen and then find out one out of the four items is unavailable or will come later, Interactive Inventory is the solution to give consumers that real-time information about what to expect from a delivery or pickup standpoint.”
Sibal reveals the system works on 90-day cycles, which takes in caching details about transactions, inventory, possessions and order information.
This then allows the consumer – when interacting with the website – to have real-time information about the retailer’s inventory through the caching mechanism.
“The machine learning part is quite critical here,” Sibal discusses. “When we think about a store, we have to consider many things.
“If a store is going to be the point of fulfillment, we want to make sure that we’re taking into account their order rejection ratio and staff showing up every day.
“All of that historic information is taken into account while picking the right store.”
Now, to get back to buying more clothes.