How does combining fresh and center-store help a retailer uncover opportunities that might not surface if they’re separate?
Hear from Shaina Finch, Head of Solutions Consulting, Symphony RetailAI as she shares with Mike Troy, Editor-in-Chief at Retail Leader how unifying demand forecasting and inventory management for perimeter and center-store reveals opportunities and impacts otherwise hidden.
- Listen to the Podcast: Understand how growth in fresh categories impacts the center store and vice-versa
- Read eBook on fresh item management: Is your supply chain missing half the customer forecast?
- Read Video Transcript
MT: Hello, Mike Troy, Editor of Retail Leader here in Irving, Texas at the Symphony RetailAI Xcelerate Conference. I’m joined by Shaina Finch and you have an interesting area that you have responsibility for, fresh and central store. You don’t often hear those two combined.
SF: Yeah, exactly.
MT: They’re usually considered separate areas. What’s going on with the combination of the two?
SF: Yeah, so typically you see a lot of time across retailers and even wholesalers that they separate the two, they think they need two separate engines, because they’re so, you know, different animals, I would say, right. They require different needs and we can do it in one engine, and so that’s the point. We want to say, hey, combine your fresh and centre store together, and that way, you have less data discrepancy. You really combine the two and you have one version of the truth.
MT: How does combining the two, help a retailer like uncover different insights that might not surface if they’re separate?
SF: Right, so think of a baguette, right. So you have a baguette and then you have the sleeve to the baguette, you have the sticker you put on there. So a lot of times, we see retailers that are managing the actual baguette that they produce in the store or ship in frozen, and then the supply item separately in a different engine. And so they’re running out of stock of that actual sleeve that’s on a baguette. Or for example, if you go to a grocery store sometimes, you see the rotisserie chicken, the bags they come in, and a lot of times, the bags, they run out of the specific bags. They’re wrapping them in aluminium because they ran out of it. So it’s really to be able to tie the two together, from that point of view, for finished goods. But even if you think of produce for example, okay, you may use strawberries to sell directly, you know, from the small case, and then you may use it in your deli and bakery department as well. So you need to know and tie the demand together.
MT: So obviously, very different dynamics, fresh category as a trip driver versus centre store, which is…you hear a lot about, like the decline of centre store and transitioning online. How are you factoring those two things together?
SF: Right, so centre store is absolutely shrinking, that’s for sure. Everyone wants fresh goods, also having the ready prepared meals. So you see centre store shrinking and the perimeter expanding. And essentially, what we can do with our solutions, is to be able to realise the impact. For example, look at, okay, frozen pizzas. If a grocery retailer decides to start making those readymade pizzas, they’re going to have a slight impact to the frozen pizzas, so you need to be able to estimate that impact, so you can forecast correctly and not over stock.
MT: That’s an excellent use case. Are there other examples like that, where a centre store item would have an impact on a fresh item?
SF: Oh, yeah, absolutely. I’m going to be honest, I like to make spinach and artichoke dip and…
MT: Spinach and artichoke.
SF: Yes. But I really actually prefer the canned artichoke, because it’s easier to cut, instead of getting the fresh artichoke.
MT: Okay, makes senses.
SF: So, yeah, so I mean you can have those impacts. So really, with the AI and forecasting, it’s to be able to estimate consumer behaviour and see the impacts to each other, so…
MT: Okay. But if you don’t have that combined, you would miss that?
SF: Yeah, exactly.
MT: Alright, very good. Thank you very much.