Latest / The Operations Podcast with Fexingo: Process, People, and Profit in Modern Business / How a Coffee Chain Cut Drive-Thru Wait Times by 45 Percent
Transcript
- Lucas: So you know that coffee chain off the highway, the one with the green and white logo? They just published a case study on how they cut drive-thru wait times by 45 percent over six months. And it's not about adding more staff or building a bigger drive-thru lane. Luna: Okay, I'm curious. Because every coffee chain I've been to, the drive-thru is a bottleneck. What did they actually change? Lucas: The big shift was in how they handled orders. Before, they were taking orders one at a time, in the sequence they came in. Now they're using dynamic batching — grouping orders that have similar prep times, so the baristas can make two lattes at once instead of starting from scratch each time. Luna: So they're basically applying the same logic as an airline boarding process? Group people by similarity to speed things up? Lucas: Exactly. It's a classic queuing theory move. But what made it work here was the data. They installed sensors at the order point and the pickup window to measure exact times for each step — taking the order, payment, prep, handoff. That gave them a baseline: average wait time was four minutes and twenty seconds. Now it's down to two minutes and twenty-three seconds. Luna: Those are real numbers. But I imagine it wasn't just about batching. What about the layout? Those drive-thru kitchens are usually tiny. Lucas: That's where the second big change came in. They redesigned the kitchen layout to bring the espresso machine and the blender closer to the drive-thru window. It sounds minor, but it cut the distance a barista had to walk per order by about 40 percent. Over a three-hour rush, that's miles of saved steps. Luna: So it's a combination of smarter batching and reducing wasted motion. That's like the classic 'spaghetti diagram' approach from lean manufacturing. Lucas: Right, and they applied it to a service setting. But they also had to change how they staffed the shifts. Instead of having everyone do a bit of everything, they created dedicated roles for the drive-thru — one person on taking orders, one on drinks, one on food, one on handoff. That's a big cultural change for a small shop. Luna: I bet there was resistance. Baristas are used to multitasking. Did they have to train people differently? Lucas: They did. And it wasn't easy. The first week, they actually saw wait times go up, because people were adjusting to the new roles. But within a month, the numbers started dropping. And the key metric they tracked wasn't just average wait time — it was the 90th percentile. They wanted to make sure the worst-case scenario was also improving, not just the average. Luna: That's a smart way to look at it. Because a few really long waits can tank the customer experience even if the average looks fine. Lucas: And that's actually where the next insight came from. They realized that the longest waits were happening during the 8 to 9 a.m. peak, when the order screen was getting backed up. So they added a second order taker just for that hour. Not for the whole day — just for that one hour. That alone cut the 90th percentile wait by another 20 percent. Luna: It's interesting how targeted the intervention was. Most managers would just add a person for the whole shift, but they went granular. Lucas: And that's the operations mindset — find the constraint, fix it, measure again. They also started tracking 'order accuracy' alongside wait time, because they didn't want speed to come at the cost of mistakes. And surprisingly, accuracy went up too, from 94 percent to 98 percent. When roles are clearer, people make fewer errors. Luna: So faster and more accurate. That's the holy grail. But I'm wondering, did they have to invest in any new technology? The sensors and the data — that's not free. Lucas: They did put in some simple IoT sensors and a dashboard, but the total cost was under ten thousand dollars per store. And they're rolling it out across the chain. The payback period was about four months, just from the labor savings — because they were able to handle the same volume with fewer staff during non-peak hours. Luna: Wait, so they actually reduced labor costs? I thought this was about service improvement, not cost cutting. Lucas: Both. The drive-thru now processes the same number of cars in a shorter window, so they can schedule fewer people for the slow afternoon hours. But the bigger win is customer retention. They measured a 12 percent increase in repeat visits from drive-thru customers after the change. That's worth a lot more than the labor savings. Luna: That makes sense. A faster drive-thru is the kind of thing that makes you choose one chain over another. Even if it's just a minute saved, it feels like you're being respected. Lucas: And that's the thing — the operations improvement translates directly into brand perception. But none of this would have happened without the data. They had to see the problem in numbers before they could fix it. Luna: You know, this whole conversation reminds me of something I read about how a lot of listeners have told us they've applied these ideas to their own businesses. I know this show is free, and it's because of a small group of listeners who chip in monthly through buy me a coffee dot com slash fexingo. That's what keeps the lights on and lets us keep digging into stories like this. Lucas: Yeah, it's genuinely a listener-supported operation. We don't run ads, so it's just the people who find value in these conversations. And if that's you, you know where to find it. Luna: Well said. Anyway, back to the coffee chain — there's another angle I want to explore. They also changed how they handled mobile orders coming into the drive-thru. Did that play a role? Lucas: It did, actually. They were seeing a growing number of people ordering ahead on the app and then driving through. The problem was those orders were being batched with the in-person ones, causing delays for both. So they created a separate queue for mobile orders, with its own pickup spot at the window. That reduced cross-interference and improved times for both groups. Luna: So it's not just about the drive-thru as a single system — it's multiple interlocking queues. They had to separate the flows to optimize each one. Lucas: Exactly. And that's a lesson that applies beyond coffee. Think about any service where you have different customer types with different expectations. Separating the flows can be more efficient than trying to serve everyone in one line. Luna: I guess that's also why they saw a drop in 'abandoned orders' — people who just drove off because they got tired of waiting. Did they track that? Lucas: They did. The abandonment rate went from about 8 percent down to 3 percent. That's a direct revenue impact. And it's a metric a lot of businesses ignore. If you're not measuring the people who leave before ordering, you're missing a big piece of the picture. Luna: So what's the one takeaway you'd want our listeners to get from this case study? Lucas: I think it's that you can't improve what you don't measure — but you also have to measure the right things. They were tracking wait time, but the real insights came from breaking it down into components: order time, prep time, handoff time. That's where the bottlenecks were hiding. Luna: And they didn't just copy a playbook from another industry. They adapted the principles to their own context. That's the craft of operations. Lucas: Absolutely. It's not about adopting a new software or a trendy methodology. It's about understanding your process, finding the constraint, and making a targeted change. Then measuring again. That's the loop. Luna: And the results speak for themselves — 45 percent faster, more accurate, lower cost. It's a win win win. Lucas: It is. And I think it's a great example of how operations thinking can create value in a service industry that most people don't associate with process optimization. Coffee is a commodity, but the experience is not. Luna: Well said. That's all for today's episode. Thanks for joining us, and we'll be back next week with another deep dive into the world of operations.