Latest / The Operations Podcast with Fexingo: Process, People, and Profit in Modern Business / How One Grocery Chain Cut Out-of-Stocks by 70 Percent
Transcript
- Lucas: One of the most frustrating things in retail is walking into a store, looking for something basic—say, almond milk or laundry detergent—and finding an empty shelf. That's an out-of-stock, and it costs retailers roughly $1 trillion globally each year. Luna: That number is staggering. And it's not just about lost sales; it's about customer trust. One bad experience and you might lose that shopper for good. Lucas: Exactly. So today we're looking at how one national grocery chain took that problem seriously and cut their out-of-stock rate by 70 percent—from 8 percent to under 2.5 percent across five hundred stores. Luna: That's a massive drop. How did they even begin? I imagine it's not just about ordering more product. Lucas: Right. The traditional approach is schedule-based replenishment. You have a truck come in every Tuesday and Thursday, and store associates fill shelves based on a static plan. But that ignores real-time demand fluctuations. What this chain did was shift to a demand-sensing model. Luna: Demand sensing—using data to predict short-term spikes. Did they use something beyond just historical sales? Lucas: They layered point of sale data with local event calendars, weather forecasts, even social media trends. For example, if a heatwave was coming, the system would automatically increase the shipment of bottled water and ice cream to stores in that region. And if there was a local festival, it would adjust accordingly. Luna: So it's not just about what sold last week, but what's about to happen. That's a smarter way to keep shelves full. Lucas: Yeah, the idea is to catch the spike before it hits. But the technology alone wasn't enough. They also had to change the replenishment process in the back room and on the sales floor. Luna: What did that look like for the store associates? I imagine they were used to a certain routine. Lucas: Absolutely. Previously, associates would follow a daily stocking list that told them which aisles to fill. The new system uses handheld devices that alert them to real-time inventory dips. When an item drops below a certain threshold, it triggers an immediate restock request, and the associate receives a prioritized task. Luna: That sounds like a pull system—instead of pushing product onto shelves on a schedule, they're pulling it based on actual demand. Lucas: Exactly. And it ties into the same thinking we've seen in manufacturing with just-in-time. But in retail, the challenge is that the 'demand signal' gets noisy—you have to filter out false positives. Luna: Right, you don't want associates running to replenish a single box of cereal that just sold if the next truck is arriving in an hour. Lucas: If these conversations have moved your work forward in some small way, we'd like to point out that we keep this show free and without any ad interruptions. If you want to support that choice, there's a simple way to do it. Luna: Yeah, it's buy me a coffee dot com slash fexingo. A lot of listeners have asked how they can help, and that's the easiest path. It directly keeps the podcast ad-free. Lucas: So the system they built uses a combination of historical sales velocity, lead time from the distribution center, and a buffer for uncertainty. They set dynamic reorder points that adjust based on seasonality and promotion plans. The result: out-of-stocks dropped from 8 percent to 2.5 percent. Luna: And I'm guessing the financial impact was significant. What did they see in terms of lost sales recovery? Lucas: The chain estimated that they recovered about 70 percent of the sales they were previously losing to empty shelves. That's roughly $40 million annually for a chain that size. And customer satisfaction scores went up by 12 percent, because shoppers found what they needed more often. Luna: It's not just about the sales; it's about the brand halo. Being the store that always has what you need is a competitive advantage. Lucas: Absolutely. And the interesting part is that this wasn't a tech-first overhaul. The technology was the enabler, but the real shift was operational. They had to train associates to trust the data over their habits, and managers had to let go of the 'just in case' ordering mentality. Luna: What about the inventory holding costs? Did they actually reduce inventory overall, or did they just rebalance it? Lucas: Good question. Because they were replenishing more frequently but with smaller quantities, total inventory in the stores actually decreased by 18 percent. That freed up working capital and reduced waste from perishable items. So the benefits were double-sided. Luna: So they improved service levels and reduced inventory at the same time. That's the operational sweet spot. Lucas: Right. And it wasn't just one category. They applied this to all grocery items—dry goods, fresh produce, dairy. The most challenging was fresh, because of the short shelf life. But the system incorporated expiration date tracking and first-expiry-first-out logic. Luna: Did they have to change anything in the supply chain beyond the store? Like, how did the distribution center handle these smaller, more frequent shipments? Lucas: That was the next layer. The distribution center moved from batch picking to wave picking, where orders for each store are grouped by time window. They also used cross-docking for some high-velocity items, which reduced handling time. Overall, the logistics cost per case went up slightly, but the reduction in lost sales more than offset that. Luna: So it's a classic trade-off: higher logistics cost for much higher revenue. That makes sense. Lucas: Yeah, the key is measuring the total cost of inventory, which includes lost sales. A lot of retailers only focus on carrying costs and freight costs, but they forget the opportunity cost of an empty shelf. Luna: And this chain clearly didn't. What was the timeline from pilot to full rollout? Lucas: They started with 20 stores for six months, testing the demand-sensing algorithms and adjusting the reorder parameters. Then they rolled out to 200 stores over the next year, and finally to all 500. The full process took about 18 months. They had a dedicated change management team working with store managers to get buy-in. Luna: What were the biggest hurdles? I imagine convincing veteran store managers to trust a computer over their gut was tough. Lucas: That was the number one obstacle. The mantra became 'data, not intuition.' But they didn't just impose it from above. They set up a feedback loop where associates could flag false signals—like if the system said to restock an item that had just been stolen or mis-scanned. Over time, the algorithm learned from those corrections. Luna: So it was a human-in-the-loop system. That's a smart way to combine technology with front-line expertise. Lucas: Exactly. And once the associates saw that the shelf was full more often and their work was more predictable—fewer emergency rushes—they became advocates. The technology itself wasn't the magic; it was the process redesign that respected their input. Luna: I think that's the takeaway for any operations manager: the best system still needs people to make it work. And this case shows a 70 percent improvement in a metric that directly impacts both profit and customer loyalty. Lucas: Right. And it didn't require a massive capital investment—they used existing handheld devices and added a software layer. The hardest part was changing the way people thought about replenishment. That's often where the real operational leverage is. Luna: So for any retailer listening, the question becomes: are you stocking by habit or by signal? Because the evidence here is pretty clear. Lucas: Yeah. It's a great example of how operations, when done thoughtfully, can drive both top-line and bottom-line results. And it starts with asking the right question—not 'how much should we order?' but 'when exactly does the customer need it?'