Latest / The Operations Podcast with Fexingo: Process, People, and Profit in Modern Business / How a Coffee Chain Cut Energy Costs by 30 Percent with Smart Scheduling
Transcript
- Lucas: Luna, here’s a number that stopped me: the average quick-service coffee shop spends about seven percent of its total operating costs on electricity alone. Luna: Seven percent — that’s higher than I would have guessed. Most people think labor or food cost is the whole story. Lucas: Right — and that seven percent is heavily concentrated. A single espresso machine pulling fifteen amps, an oven that cycles at five kilowatts, refrigeration that runs twenty-four-seven. The equipment doesn’t care if you’re serving one customer or fifty — it’s drawing power almost all the time. Luna: So the classic move is ‘turn things off when you don’t need them,’ but in a coffee shop that’s tricky. You can’t kill the espresso machine during the morning rush. Lucas: Exactly. And that’s what makes this case study I found so smart. A regional chain called Brewline — one hundred forty-seven stores across the Pacific Northwest — decided to get surgical about their electricity load. They weren’t going to buy solar panels or new HVAC systems. They were going to change the schedule. Luna: Schedule of what? The equipment runtime? Lucas: Yes. They put smart plugs on every piece of equipment that could be turned off for at least part of the day — ovens, warming trays, backup refrigeration, even the lighting in the back of house. And they logged actual energy draw per device, per half-hour, for seven months. Then they ran a load-shedding algorithm that basically said: ‘Given your traffic pattern and your utility’s time of use rates, when should each device be on, and when should it be off?’ Luna: That’s a lot of data — seven months’ worth across nearly a hundred fifty stores. Lucas: It was, but they already had point of sale data by the half-hour, so they could correlate traffic with energy use. And what they found was a pattern that probably exists in a lot of retail operations. The morning rush from six to nine a.m. — high traffic, all equipment running. Then a steep drop-off from nine-thirty to eleven. Then a second, smaller lunch bump. Then an afternoon lull from two to four. And a final evening bump from five to seven. Luna: So the classic three-peak day. But most shops leave the ovens on all day because it’s easier than cycling them. Lucas: Exactly — convenience over cost. Brewline’s algorithm said: turn off the sandwich ovens from nine-thirty to eleven, and again from two to four. That’s almost four hours a day where those ovens are not drawing five kilowatts each. And because the ovens are well-insulated, they come back up to temperature in about four minutes. So the impact on customer wait time was negligible. Luna: What about refrigeration? That’s usually the biggest base load in a commercial kitchen. Lucas: Great question. Brewline didn’t turn off main refrigeration — too risky for food safety. But they identified backup refrigeration for dairy and backup cold-storage units that were only half-full. Those got cycled off during the lowest-traffic hours: midnight to five a.m. The algorithm also staggered defrost cycles so that multiple units weren’t drawing peak power simultaneously. That alone cut their demand charges — the fee utilities charge for your highest fifteen-minute usage — by twenty-eight percent. Luna: Demand charges are brutal. I’ve seen restaurants where demand charges account for forty percent of the electric bill. Lucas: Brewline’s were about thirty-five percent before the trial. After the smart scheduling, their total electric bill dropped by twenty-two percent across the fleet. That’s one point two million dollars a year — from a software change. No capital expenditure. Luna: If today was actually useful to you — and I hope it is — the way these stay ad-free is listener support. Buy me a coffee dot com slash fexingo. It’s how we keep doing deep-dive operations cases without selling anything. Lucas: Yeah, that’s exactly right. And it’s low-key — one coffee’s worth helps. Okay — back to the numbers. Brewline also found something unexpected. Luna: What’s that? Lucas: The equipment that was on a timer or a thermostat — like the front of house lights — was already fairly efficient. But the manual switches — the stuff employees had to remember to turn off — those were the worst offenders. Stores where the manager was diligent saved more, but on average, employees forgot to turn off the warming tray after the breakfast rush about forty percent of the time. Luna: So the smart plugs automated what humans were inconsistently doing. That’s a classic process improvement — remove the reliance on memory. Lucas: And the smart plugs paid for themselves in under four months. Each plug cost about thirty-five dollars. They put one on each controllable device — an average of eight per store. So about two hundred eighty dollars per store, and they saved roughly five thousand dollars per store per year in electricity. Luna: That’s an eighteen-to-one return. Most process improvements don’t come close to that. Lucas: No, they don’t. And here’s the part I love: Brewline didn’t need a data scientist. They used a simple rule-based algorithm — if traffic below X threshold for Y minutes, and equipment has Z warm-up time, then turn off. No machine learning, no artificial intelligence. Just good operations thinking with real data. Luna: That makes it replicable. Any chain with half-hourly sales data and smart plugs could do this. Lucas: And that’s the point. The chain’s operations director told a trade publication that they’re now rolling this out across all new stores, and they’re working with their utility to get a rebate for demand reduction. The utility is happy because it defers the need to build new substations. Brewline is happy because they’re saving money. And the customer never notices a thing. Luna: So the takeaway isn’t ‘buy solar panels.’ It’s ‘look at your load profile and your schedule together.’ Lucas: Yes. Most businesses run equipment on a fixed schedule that was set years ago and never questioned. Light timers, oven schedules, HVAC setbacks — these are often set once and forgotten. Brewline’s insight was that the schedule should match the demand pattern, not the other way around. Luna: I’m thinking of all the retail and food-service operations that could apply this. Even a single-store owner could do a simplified version. Lucas: Absolutely. The barrier is not technology. It’s taking the time to measure what you’re actually using and then having the discipline to change the schedule. Brewline’s project took one operations manager and an intern about three months to set up. The chain’s CEO said it was the highest roi project they’d done in five years. Luna: I wonder if there’s a second-order effect — like, does the equipment last longer if it’s cycled off? Lucas: They actually tracked that. For ovens and warming trays, cycling doesn’t seem to increase wear because the thermal expansion is within design limits. But for compressors in refrigeration, they saw a small increase in starts, but overall run-time went down, so net effect was neutral. The bigger savings was on lighting — LEDs last longer when they’re off, but that’s marginal. Luna: So the main lever is pure energy cost. One point two million dollars a year for a regional chain is real money. Lucas: And that’s just electricity. They’re now looking at natural gas for the ovens — same principle, but with gas valves instead of smart plugs. Early estimates suggest another fifteen percent on the gas bill. Luna: It’s almost too simple. Why don’t more companies do this? Lucas: I think it’s visibility. Electricity is a line item on the P&L, but it’s rarely broken down by device. Most managers see a total bill and shrug. Brewline gave each store manager a dashboard showing their energy use by device and how it compared to the chain average. That gamification alone drove a five percent reduction before the algorithm even kicked in — just managers paying attention. Luna: Feedback loops. Put a number on something, and people want to improve it. Lucas: Exactly. That’s the operations lesson: you can’t manage what you don’t measure. And in this case, the measurement cost almost nothing — smart plugs are cheap, and the data is already there. The hard part is deciding to look. Luna: Alright — I’m going to check my own office’s energy schedule. I bet we have space heaters running all night. Lucas: That’s the spirit. And if you find something, let us know. We might do a follow-up on how small-scale operations are applying these same ideas. Luna: Sounds good. Next episode, we’re looking at a hospital that cut surgical instrument reprocessing time by forty percent — using nothing but a kanban system. Lucas: That’s a good one. See you then.