AI-powered retail drives sustainability

Retailers looking for an edge are turning to the power of artificial intelligence to help them realize the potential of their massive banks of data—and in the process enhance their sustainability efforts while boosting customer service and satisfaction.

BY RANDY SCOTLAND

The use of AI presents the industry with a classic win-win-win finish and one that industry experts are touting as a new best practice.

“Boosting the top and bottom lines with AI is not a distant dream,” con­cludes the recent PwC 2019 AI Predictions study. “AI’s power can help businesses create and market high-quality, personalized, data-driven products and services. Companies can use AI to help with strategy, in­vent new business models and eventually transform their organizations.”

The study, based on a survey of 1,001 top U.S. executives, forecasts an accelerating pace of AI adoption by corporations this year, adding: “Right now the greatest gains for AI are coming from productivity enhance­ments, as companies use AI to automate processes and help employees make better decisions.”

Retail is among those sectors adopting AI as a strategic tool to help analyze reams of data for predictive modeling and efficiency purposes.

“Retailers are already using AI to an­ticipate trends and guide the business to meet them,” the study notes. “Next up is hyper-personalized retail: AI and auto­mation make it feasible for retailers to of­fer a growing number of products or servi­ces made specifically for one individual.”

On the operations front, the retail in­dustry is harnessing AI and big data to bolster the drive for better sustainability.

“Retailers are always focused on mak­ing a much leaner, more accurate supply chain,” observes Dan Mitchell, the Boston-based Director of Retail and CPG Practice with analytics powerhouse SAS.

“On the surface, that probably would not be an obvious sustainability activity,” he says. “But think about what happens when you’re landing inventory in the wrong place. We wind up shipping it from store to store, or from the store back up to the distribution centre down to the store, or we’re shipping the item from one store direct to [the customer’s] home.

“And it’s nice we can move all this inventory around to get it to the cus­tomer, but obviously there’s a carbon footprint involved any time you put a product on a truck and start moving it around. Using AI and big data means you’ve got a never-ending opportunity to create leaner, meaner supply chains. For a lot of retailers I talk to, that’s job number one.”

$1 billion AI investment

SAS recently announced that the company intends to make a billion-dollar investment in AI over the next three years, focusing on research and development, educational initiatives, and services to optimize customer re­turn on AI projects.

The company added that it will be investing in R&D innovation in all core areas of AI, with the aim of making it easier for users with different skill levels to benefit. To that end, SAS is embedding AI capabilities into its SAS Platform and solutions for data management, customer intelli­gence, fraud and security intelligence and risk management, as well as applications for a variety of industries, including retail.

“A big part of that [billion-dollar] investment is around education and giving people the tools and curriculum and access to learn about analytics and AI, and become practitioners and get certifications,” Mitchell says. “There are a lot of places to get that education, but it’s not everywhere.”

He adds: “We need more great smart people who are eager and excited to use AI, and we want to give them the educational tools to do that.”

The good news is that retailers are well positioned to take advantage of all the data and related skill sets they have amassed.

“We’ve moved past not having all the data in a place that we can action on it,” Mitchell says. “So, the next thing is to try and take that data and create the most accurate demand signals possible to really understand what will sell where and when. There is a lot of modelling and techniques that needs to happen to get that. It’s not as simple as just using historical sales data. You have to under­stand what’s going on in the supply chain. Where was the inventory? Were there any price changes or promotions or other effects that influenced demand? Being able to tease apart all of those components that affect historical data, pulling those apart and reassembling them in a picture of what you think the future is going to be—that’s the task at hand. And I feel like all retailers are well versed in that and they’ve started their journey.”

Daunting task for retail

The scope of the job at hand can be daunting, especially given the complexity of the retail business.

“They have many stores, many SKUs, many nodes in the supply chain, whereas maybe on the [consumer product goods] side where they focus their energies they make great strides. For retail, the scale is a chal­lenge,” Mitchell concedes.

Then there’s the need to have the right analytic tools in place. “And probably more important, in concert with that [requirement], is having the actual skilled people in-house to use those tools and use those analytic technologies.”

Mitchell points to the grocery sector as one in particular where, given the perishable nature of the merchandise, sustainability is of paramount concern.

“Some products have bigger carbon footprints than others, and they have a shelf life and they expire. So, there’s a lot of attention right now on trying to predict and optimize what that shelf life is, what is the right amount of inventory, how to factor in spoilage.

“The ultimate way to deal with spoilage and wastage is sell it before it goes bad, right? And the ultimate way to do that is to not have too much of something someplace where people don’t need it. That’s when you definitely start to get into an area where maybe traditional econometric time series forecasts are not sufficient, and you need to use much more sophisticated algorithms.

“That’s where you start getting into machine learning and artificial intelligence, which are really using many different analytical techniques in combination.

“In one part of Canada, produce at a certain temperature and certain humidity might last a lot longer than another type, or in one part of Canada the time it takes to get from a local vendor to the store is a lot different than in another part of Canada. Being able to collect all of that data about that head of cabbage or bushel of oranges and start to understand all the things that affect spoilage is where a lot of the big grocery chains are looking towards.”

Carrefour’s strategic move

To that end, Mitchell noted that retail giant Carrefour announced earlier this year that it had signed on to use SAS’s analytic technology and Viya platform to optimize its supply chain. Car­refour said it was aiming to create a proprietary online and in-store shopping universe where the most suitable merchandise for recognized loyal­ty customers is guaranteed any time, any place.

“Artificial intelligence will free up time for our teams to focus on developing differenti­ated forecasting strategies and best meet our customers’ expectations while reducing waste,” Franck Noel-Fontana, Forecasting Director at Carrefour France, said in a statement.

For retailers, this kind of analytic capability is the way of the future, according to Mitchell.

“There is absolutely no downside in investing [in analytics] on the inventory side. As you know, that’s where most of my equity is and my cash as a retailer is locked up—it’s in the products. Sec­ondarily, it’s all the facilities and operations.

“Using analytics and AI and focusing on sus­tainability when it comes to route optimization, cold chain logistics, refrigeration in the store, energy use in the store—all of those things are initiatives that retailers are taking on.

“First and foremost, it’s saving retailers money. In the process it is absolutely helping retailers move towards sustainability and reducing the carbon footprint.”

The human touch

Another key benefit of AI, Mitchell says, is the fact that it allows retailers to give over rou­tine workday tasks to “intelligent automation” in order to concentrate on projects that require more of a human touch.

“If I can free up more time in my day and take away some of the rote tasks and move me more towards the high-value tasks, all the better.

“When you get into this topic people fear that it is just going to take jobs away, and I don’t see that at all. Where I see it going is supporting retail­ers’ efforts, allowing them to execute and oper­ate at levels they would never have been able to achieve in the past because of this technology.

“Before they might have been planning store clusters. But now, with the help of AI, retailers can plan each store individually. They can spend more time on new product forecasting and de­sign. With the growth of AI, they have time on their side to concentrate on the things that are going to position them for greater success.”