Latest / The Operator's Manual with Fexingo: Daily Decisions, Tactical Wins, and Business Execution / How One Retailer Stopped Shrinkage With a Simple Count
Transcript
- Lucas: If these conversations have moved your work forward in some small way, consider supporting the show directly. A couple of dollars a month is genuinely what keeps these going — buy me a coffee dot com slash fexingo, if you've gotten something out of them. Luna: Yeah, it really does help keep the signal to noise ratio high for us both. Thanks for that support. Lucas: Let's get into it. We talk a lot about big strategic pivots, but today I want to look at something smaller and uglier: shrinkage. Specifically, how one regional grocery chain turned around three percent annual losses without firing anyone or buying new technology. Luna: Three percent sounds small until you multiply it against a multi-billion dollar revenue base. That’s real money bleeding out the back door. Lucas: Exactly. Most people assume shrinkage is just shoplifting. But in this case, the data showed that sixty-five percent of the variance came from internal process errors — bad receiving, misplaced stock, and data entry mistakes that compounded over time. The chain had been doing full physical inventories once a year, plus random cycle counts every quarter. Luna: So they were waiting months to find out they were wrong? That seems like flying blind with a broken compass. Lucas: Right. And because the errors accumulated silently, managers couldn't pinpoint when things went wrong. Was it Tuesday? Was it the night shift? They didn't know. So the fix wasn't a new scanner or a security camera system. It was a simple change to the counting rhythm. They shifted to daily spot checks on their top two hundred high-turnover SKUs. Luna: Just two hundred items? That feels manageable compared to auditing an entire warehouse. Lucas: It is. And statistically, those two hundred items represented roughly eighty percent of their shrink risk. By focusing only there, they freed up labor hours elsewhere. Instead of one person spending four hours a week on a massive quarterly count, they assigned five minutes a day to store associates to verify those specific locations. Luna: I love that. It turns a punitive, end-of-year audit into a daily habit. Does that change how the staff views inventory? Lucas: That was the surprise for me. When you do a huge annual count, it’s a punishment. Everyone hates it. It’s stressful, rushed, and prone to error because people are trying to finish fast. Daily spot checks feel like routine maintenance. It’s part of the job flow, not an interruption. Luna: So the psychological shift actually reduces the error rate itself? Because people aren't rushing? Lucas: Precisely. Plus, when a discrepancy pops up on a Tuesday morning, it’s fresh. You can trace it back to yesterday’s shipment. If you wait six months, the trail is cold. The chain reported a forty-two percent reduction in net shrink within the first eight months of rolling this out. Luna: Forty-two percent is huge. But doesn’t this require better visibility into which SKUs are risky? How did they pick the top two hundred? Lucas: They used a simple ABC analysis based on historical variance. Item A was high value and high turnover. Item B was moderate. Item C was low. They focused entirely on A. Interestingly, luxury goods like expensive spirits or cosmetics were also included, even though they weren’t food items, because the theft risk was obvious. Luna: Makes sense. High value plus high movement equals maximum opportunity for error or theft. Why didn’t they do this before? Technology exists to automate this kind of tracking. Lucas: Technology does exist, but implementation takes months and costs hundreds of thousands. This was a process change that cost almost nothing in capital expenditure. It just required discipline. The issue with tech is that it assumes your input data is clean. If your system says you have ten units, but you actually have seven, the tech will tell you you’re short ten. It doesn’t fix the root cause; it just confirms the symptom later. Luna: Garbage in, garbage out. So the daily count acts as a validation layer for the system itself. Lucas: Exactly. It validates the digital record against physical reality every single day. And because it’s frequent, the variance stays near zero. You catch a missing item within twenty-four hours, not within ninety days. Luna: I’m curious about the staffing side. Did you need extra hires, or did you redistribute existing work? Lucas: Redistribution. They took fifteen minutes off the closing routine each day. Closing stores usually involves deep cleaning and restocking. They shortened the cleaning slightly to prioritize the count. It’s a trade-off. Cleanliness scores dropped by maybe one percent, but profit margins improved significantly. Management decided that a slightly less pristine floor was worth the cash recovery. Luna: That’s a tough call for some brands, but financially it’s undeniable. What about false positives? If the count is off by one unit, does that trigger a panic? Lucas: No, they built in a tolerance threshold. If the variance was under two percent for that SKU, it auto-corrected in the system overnight. You only got flagged if the variance jumped above five percent. That kept the noise down and let managers focus on actual anomalies. Luna: So it’s not about perfection. It’s about catching the big leaks early enough to plug them. Lucas: Spot on. Perfection is impossible in retail. You will always have a few misplaced items. The goal is speed of detection. The slower you detect it, the more likely it is to be theft or systemic waste rather than a simple misplacement. Luna: Does this scale to other industries? Say, e-commerce fulfillment centers? Lucas: Absolutely. Fulfillment centers have similar issues with mis-scans and wrong-item shipments. Some large logistics firms have started implementing daily spot checks on their highest-return categories. If customers are returning a specific product line at a high rate, checking that inventory daily prevents sending out defective stock repeatedly. Luna: Interesting. So it’s not just about stopping theft, but improving quality control too. Fewer bad products leave the facility. Lucas: Yes. And that reduces reverse logistics costs, which are notoriously expensive. Shipping a returned item back to the warehouse often costs more than the item itself. Catching the error internally saves that entire supply chain loop. Luna: I think a lot of business owners overlook the hidden cost of returns. They focus on sales volume but ignore the drag on operations. Lucas: It’s a classic case of looking at the top line while ignoring the leaky bucket. If you can tighten the bucket, you don’t need as much water to maintain the same level. The ROI on this change was calculated at roughly eighteen months payback period if they had invested in tech, but since they used process change, it was positive cash flow from month two. Luna: Month two. That’s incredibly fast. Most projects take a year just to prove they work. Lucas: Because the data was immediate. There was no learning curve for software. The learning curve was just telling staff to count differently. Once they understood the routine, the numbers spoke for themselves. Luna: It’s a reminder that sometimes the best tool isn’t a new piece of hardware, but a better question. 'What are we missing?' instead of 'What are we selling?' Lucas: Exactly. And asking that question daily, rather than annually. It changes the culture from reactive to proactive. Luna: I’ll have to remember that framework. Daily spot checks on high-risk assets. Whether it’s inventory or even code deployments in tech. Lucas: There you go. Apply the principle wherever the variance hurts most. Small, frequent corrections beat big, infrequent shocks every time.