Glenn Taylor from Sourcing Journal recently covered Oracle Retail's portfolio update webinar, leading up to Virtual NRF 2021.
The Covid-19 pandemic’s impact on retail has made agility even more of a priority as consumers shop from different channels and geopolitical headwinds disrupt the supply chain. But the first step in being truly agile comes down to delivering data-driven inventory management that gives all stakeholders, whether that means supply chain managers, store employees and consumers, a chance to view where their products are at all times.
Artificial intelligence and machine learning are now requirements for retailers if they want to better predict demand, create guidance on how to respond to rapid customer changes and give store associates the tools necessary to create more authentic engagements, according to Jeff Warren, vice president, solution management and strategy at Oracle Retail.
“The challenge for us, as we think about creating that space for innovation, is fundamentally at the end of the day, our business is about driving outcomes,” Warren said during a digital event Thursday. “How do we plan better and more compelling assortments with the right price and the right purpose? And how do we drive seamless execution to enable the consumer and customer journeys that we’re ultimately looking to provide?”
Antony Wildey, vice president of solution consulting of Oracle Retail, believes the answers to these questions lie within the company’s move into what it calls “exact inventory” and mobility.
Using the examples of “Ash,” a store associate leveraging a mobile device, as well as “John,” an inventory analyst, Wildey explained how these employees can use the platform to improve both the back and front ends of the retail experience.
“[Ash] is able to go in on her mobile device to look at the customer orders that have come in overnight,” Wildey said. “She’s able to go in, inspect the order, start to see what she needs to… create a pick against the order. She gets recommendations from the system [of] exactly the inventory that we hold around this, and where that inventory is located within the stores, then find where that particular order is, scan the item to create the pic, and finalize that order.”
This finalization process notifies the customer immediately on their mobile device that the order is available for pickup from that store. Wildey noted that Oracle can process when the consumer orders the product from any digital touch point, even through a call center.
This AI-powered recommendation engine serves to enable “John” to make better decisions on where he should move inventory between locations, which came in handy when the pandemic forced stores to close.
“You’ve got a number of stores that had to move into that ‘dark’ format as well as a number of stores that are fully trading to use the system here to get a recommendation on the types of transparent inventory necessary to optimize the inventory that he’s holding,” Wildey said.
He noted that in the specific example highlighted, which was based on work with a customer retailer, the recommendation generated a potential increase in $56,000 in sales for a cost of just $2,000 to move the inventory from one location to another.
Warren pointed to Oracle’s work with Gap Inc. as an example of the industry’s need to simplify and scale back-end operations to improve the shopper experience as an influx of data enters the picture. Oracle initially set up the specialty retailer’s “blueprint for agility” by moving its data and processes from siloed, on-premise applications to its cloud-based SaaS platform, according to Warren.
“If we think about the challenges we’ve seen and tackled in the first phase of this journey together, it was about moving visibility to inventory from siloed and disparate systems in an on-premise environment into the cloud and giving that single view of inventory visibility across more than just the application that it served,” Warren said. “It was about getting a single view of customer, a single view of the order, a single [view] of the price and ultimately bringing all that information into cloud-based services that could be leveraged across the retail landscape.”
Oracle says 500 million consumer transactions flow through its Retail Cloud platform, with 10,000 daily users of its cloud ecosystem.
Warren said the platform’s next step is to bring in other forms of data to improve inventory visibility, including weather, social and Covid-related data. As Oracle continues its “pivot to the customer,” the cloud giant aims to bring more relevant customer information into processes where it historically may not have been integrated, all with the end goal of helping retailers create a better product assortment that serves customer needs.
“We understand that 5 percent of your assortment is driving 45 percent of your markdowns, but what we understand is none of your best customers in a specific customer segment are buying from that assortment, and that we need to actually change our strategy,” Warren said. “We can connect the ability to be able to drive outcomes around going after an increasing growth in market share in specific segments to how you build the assortment, and we can connect these goals with how you actually then start to price and place your inventory.”
At the end of the event, Warren noted that ideally, the Oracle team would have a retailer’s transaction-level data to deliver the best AI-driven business outcomes, typically going one to two years back. But he did say that there’s no specific “right” answer for retailers.
“What remains true no matter what is we want to work with the lowest level of data that you have, which is about a specific interaction between your brand and your consumer,” Warren said.