Latest / The Operator's Manual with Fexingo: Daily Decisions, Tactical Wins, and Business Execution / How One Grocery Chain Cut Food Waste by 30 Percent
Transcript
- Lucas: So Americans throw out about 30 to 40 percent of the food supply every year. That's roughly 133 billion pounds. And the grocery industry is a big part of that — supermarkets toss perfectly edible food because it didn't sell before the expiration date. Luna: And it's not just a waste problem. It's a money problem. Grocers operate on razor-thin margins, typically around 2 to 3 percent. Every pound of wasted food is profit that could have been made. Lucas: Exactly. So today I want to talk about one regional grocery chain that tackled this head-on. SmartWay Markets — a chain of about 400 stores across the Midwest — decided they were going to cut food waste by 30 percent in 18 months. And they did it. Luna: I've heard of them. They're not as big as Kroger or Albertsons, but they've been known for operational experiments. What did they do differently? Lucas: They focused on one thing: the gap between what the store orders and what customers actually buy. Most grocers use historical sales data to place orders. But that ignores weather, local events, even day-of-week patterns. SmartWay implemented a demand sensing platform that incorporated real-time data — things like the local high school football game schedule or a heatwave forecast. Luna: So they're using machine learning to predict demand at the store-shelf level. How granular did they get? Lucas: Perishable item level. They started with dairy, produce, and deli — the highest-waste categories. The system would look at 60-plus variables and suggest order quantities for each store. They also added dynamic pricing on items approaching their sell-by date. Instead of a blanket 50 percent off, the system would calculate the optimal markdown — sometimes 20 percent was enough to clear it, sometimes 30. Luna: I've seen the typical approach: slap a 50 percent off sticker on everything a day before expiration. But that trains customers to wait for the markdown. You end up cannibalizing full-price sales. Lucas: Right. That's exactly the problem. SmartWay's dynamic pricing avoided that. They found that a 30 percent markdown on items with two days left moved just as much volume as a 50 percent markdown would have, but with 20 percent more revenue. And because the demand sensor predicted the right quantity, they reduced overstock by 15 percent in those categories. Luna: So less waste and more full-price sales. What was the bottom line impact? Lucas: Over 18 months, they cut food waste by 30 percent — exactly as targeted. That translated to about $50 million in annual savings across the chain. On a margin of 2 to 3 percent, that's significant. It's the equivalent of adding hundreds of millions in revenue without opening a single new store. Luna: And it's not just the money. There's a brand angle too. Consumers are increasingly aware of waste. SmartWay marketed this as 'SmartFresh' and put signage in stores. It probably drove traffic. Lucas: It did. They saw a 5 percent increase in customer satisfaction scores in pilot stores. But here's the key operational detail: the success depended on the store employees. The system could suggest a markdown, but if the staff didn't apply the stickers correctly or at the right time, it didn't work. SmartWay invested heavily in training — every department manager got a two-hour session on how to read the system's recommendations and when to override them. Luna: So the human element wasn't eliminated. It was augmented. They gave workers a tool but also the judgment to use it. Lucas: Exactly. One example: the system might suggest a markdown on yogurt at 3 PM, but the dairy manager knows that the after-school rush hits at 4 PM, so she holds off. The system learns from that override and adjusts. It's a feedback loop. Luna: And that loop is what makes it scalable. If you just drop a black box algorithm into a store without training, people ignore it. SmartWay understood that. Lucas: They did. And they measured everything. They tracked not just waste reduction but also labor hours — because applying markdown stickers takes time. They found that the new system actually reduced labor because they weren't slashing prices manually on everything. The system printed the labels automatically. Luna: So it became a net labor saver. That's the kind of operational win that gets buy-in from store managers. Lucas: Right. And within 12 months, they rolled it out to all 400 stores. The technology cost about $5 million upfront, plus ongoing licensing. So the payback period was less than one year. Luna: That's a no-brainer for any CFO. I'm curious — did they face any pushback from suppliers? Because if you're ordering less, some suppliers might not be happy. Lucas: That's a great question. Some dairy suppliers did push back initially because order volumes dropped. But SmartWay argued that the reduced volatility in orders actually helped suppliers plan their own production. And because waste was lower, SmartWay could commit to longer-term contracts at stable volumes. Most suppliers came around. Luna: So it's a win-win. The chain saves money, suppliers get predictability, and less food ends up in landfills. If today was actually useful to you, the way these stays ad-free is listener support — buy me a coffee dot com slash fexingo. Just a small way to keep these kinds of deep dives coming. Lucas: Yeah, we really appreciate that. Now, one thing I want to emphasize: SmartWay didn't invent demand sensing. The technology has existed for years. What they did was execute on it — training, rollout, measurement. It's the discipline of implementation, not the novelty of the idea. Luna: And that's the thread through so many of our episodes. It's not about discovering a secret. It's about doing the work to make a known solution actually work at scale. Lucas: Exactly. So what's the takeaway for a smaller operator? You don't need a $5 million system. SmartWay's approach can be scaled down. A single store could start with a simple spreadsheet that tracks sell-through rates by time of day, and manually adjust ordering. The principle is the same: use data to align supply with actual demand, not historical averages. Luna: And don't forget the employee training piece. Even the best algorithm is useless if the person on the floor doesn't trust it or know how to use it. Lucas: Right. So for anyone listening who runs a retail operation — whether it's groceries, clothing, or electronics — ask yourself: what is your waste? And what is the gap between what you order and what you sell? Chances are, closing that gap by even 10 percent is worth real money. Luna: And if you want to see the full details, SmartWay actually published a case study with some of the numbers. We'll link to it in the show notes. Lucas: We will. Next time on The Operator's Manual, we're going to look at how one logistics company used a single software change to cut delivery delays by 25 percent. Until then, keep running your operations with intention.