Getting the Numbers Right: AI Solutions for Grocery Demand Forecasting
The grocery sector was arguably hit as hard by the pandemic as any and is still dealing with the most persistent hangover—namely ongoing supply shortages, inventory imbalances, and excessive spoilage and waste. But I don’t have to tell you that. You’re living it.
You’re keenly aware that chronic out-of-stocks directly equate to lost sales, while spoilage rates for fresh goods—intrinsically between 7-10%, even in the best of times—have become harder to wrangle than ever.
Nearly two-thirds of shoppers who participated in Zebra’s 16th Annual Global Shopper Study say they are still leaving stores without all the items they wanted, and people I know have anecdotally told me that their weekly grocery trip has turned into a virtual “treasure hunt.” If their preferred varieties of milk, eggs, packaged goods, or other staples aren’t available, they’ve become resigned to go without those items for the week – or go to one of your competitors, hoping they’ll have better luck.
Some shoppers are turning to mobile devices to hunt for what they need. Over one-third of shoppers say they’re checking their apps before going to stores to ensure items are in stock, while half say their decision to buy either in store or online boils down to one thing: where can they get what they need.
With inventory visibility – and availability – as important to your customers as it is to your team, you’ve got to keep searching for areas where you can gain a better strategic foothold.
From my perspective, that falls back to demand forecasting.
Now, I know demand has become less predictable as more customers return to stores. The Shopper Study showed a slight dip in the use of mobile ordering for groceries. But is that decline temporary or is that the new norm? I think it’s temporary given that shoppers also said they increasingly prefer retailers who offer buy online, pick up in store and curbside pickup options. And though more shoppers are returning to stores, they are clearly buying groceries online, too. If they weren’t, you wouldn’t have to be working so hard to continuously adapt and re-calibrate inventory around online or app-based shopping, delivery, curbside pickup and other omnichannel options to remain competitive and preserve margins.
So, knowing that things may never truly stabilize, and that pinpointing those elusive “right numbers” across thousands of SKUs has become increasingly challenging, let’s talk about what you need to change to arrive at more consistently accurate forecasts.
First: Let’s just acknowledge that the tumult of the past three years has wreaked havoc on what you used to mostly do on your own, which is projecting sales based on order quantities, store allotment, pricing and promotions, and evaluating historical performance.
Let’s also accept that outmoded, inaccurate sales forecasts are rapidly giving way to demand forecasting, backed by advanced artificial intelligence (AI) and machine learning, which can aggregate a wealth of internal and external datapoints into right-sized, on-hand inventory serving every store, every SKU, and every customer. When a foundation of data-driven insights tells you what you can expect your customers will want to buy, other planning in turn becomes more reliable.
Feel like this is a lot? That you’re not in a position to play around with AI? That it’s too risky given everything you’re up against?
Well, unbeknownst to many store managers, AI is already making a game-changing difference in their own center-store aisles—at least for Direct Store Delivery (DSD) vendors, as my colleague Jasneet Kohli has previously discussed. From beverage companies to iconic bakery brands, algorithm-based tools have redefined predictive ordering, incorporating a wealth of granular-level daily and weekly datapoints affecting every store delivery—including projected seasonal demand, store promotion schedules, even local weather conditions.
This automated process in turn recommends an optimal order quantity for every stop on a DSD route, which drivers can readily access via a tablet-based user interface. These AI-powered tools make the most efficient use of limited space on every delivery truck, right-sizing individual store deliveries while significantly reducing margin-killing spoilage return rates. They can also incentivize drivers and other workers for doing their jobs more efficiently.
On a scale comparable to grocery, Walgreens—a prime Antuit success story—was among the first nationwide retailers to take the bold leap into data-driven demand forecasting and planning across its 9,000 store locations. A single SKU within one store which may have been once influenced by only a few variables, such as medicines during cold and flu season, may now be potentially nuanced by dozens of intricate real-time internal and external datapoints—from nearby events to local economic conditions to trending social media. This is far more information than a small army of humans and spreadsheets could manually decipher into a data-driven, profit-optimized pricing strategy.
The proven early success of AI-powered demand forecasting among consumer packaged goods companies (CPGs) and drugstores offers strong promise for better managing the larger “universe” of SKUs within the typical supermarket. Syncing inventory and allocation around anticipated customer demand can help reduce those off-putting bare spots on shelves—while at the same time preventing costly excess inventory from clogging warehouses and stockrooms.
Beyond packaged goods in the center-store, we can expect AI-powered demand forecasting to also play an emerging role in the prepared food sections of the store—the bakery and deli, offering grocers greater ability to sync right-sized quantities of raw ingredients and finished product on-hand—where freshness and variety are even more important to discerning customers.
While there may be a bit of trepidation surrounding AI these days, we can point to the positive differences it’s already made for grocers and the retail sector at large by accurately predicting consumer demand and optimizing inventory to better serve the customer—all while strengthening the bottom line.
Whether you’re a grocer or other large retailer, we’d be happy to talk with you about how AI-based software like this can make a real difference toward better serving your customers. You can contact us here when you’re ready.
