Predictive Analytics And Machine Learning AI In The Retail Supply Chain


In retail, supply chain efficiency is essential. Inventory management, picking, packing and shipping are all time and resource-intensive processes which can have a dramatic impact on a business’s bottom line.

The problem is these are complex processes, particularly when it comes to large scale operations covering multiple outlets and territories. The fact they are often dependent on outside forces – suppliers, service providers and even weather – make getting it right even more difficult.

This is why retailers – both big and, increasingly, smaller operations too – are keen adopters of Big Data-driven analytics technology. Creating efficiencies in complex systems which involve multiple, often compartmentalized processes is an area where this technology excels. In short, it’s about the ability of machines to make lots of little savings and efficiencies, which together add up to very large ones.

Monte Zweben – CEO of Splice Machine, which provides predictive systems for industry, talked me through three key areas where retailers are increasingly looking towards data-driven analytics in order to drive efficiencies in their supply chains. We also talked about why this approach is going to become increasingly important for businesses in all sectors which want to stay ahead of the pack and foster innovation.

Filling your customers’ needs more quickly

Today’s Internet of Things industry means that everything is connected and capable of collecting and sharing data on how it is operating. This means that everything can be measured and – through the use of advanced analytics tools such as machine learning – rigorously interrogated until it gives up all its secrets on how it works, and, crucially, how it interacts with every other part of an operation.

All of that data can be collected on an inventory – origins, transit routes, times when it is scanned or its location and status are reported by RF (Radio Frequency) tags.

“So, now you can build a machine learning model,” Zweben says, “and that model could make a prediction about any aspect of the operation based on the data it’s got.

“What’s the likelihood you’re not going to be late with this order? What’s the likelihood you’ll be a day late? Five days? It’s basically a classification problem.”

This means that in-depth simulations can be run, allowing the implications and knock-on effects of lateness or missed deadlines to be assessed before they become an issue, even if they can’t be entirely eliminated due to a reliance on external influences. Where this is the case, remedial action can be taken ahead of inconvenience being caused to customers, who are certainly likely to be appreciative of an email apology when a shipment is likely to be delayed, rather than simply to be kept waiting.

Read the source article at Forbes.