Low effort is pointing out just one thing that may or may not even help. This doesn't address the machine learning pieces, 100k sensors per restaurant, fryer sensors, everything else that is listed. It's a chicken sandwich restaurant. How much will all of this really buy over typical weekly/monthly/yearly sales trends?
It's not just CFA. I for some reason watched a Taco Bell video on LinkedIn that layed out one small problem regarding the ability to accept orders, and how a -completely- new k8s microservice architecture addressed it. This smells a lot of the same, mixed in with more tech overoptimism. GE had the same spiels 10 years ago, with better use cases, and realized most of it was a dead end.
I don't know how Chikfilas are near you, but with the ones I've been to there are often lines of cars wrapping around the building a couple times. And when that happens, there are several Chikfila employees walking car-to-car with tablets, pre-emptively taking and charging orders from customers.
Allowing them to batch-make orders from cars that would otherwise have to wait for someone else to get their food to place the order.
Not having to have the stores talk to a remote server to record an order during peak load could be a win. Not sure if it justifies "edge computing" but if a fast food restaurant needed it, it'd be them.
I've never seen anything like that level of congestion at McDonalds or Taco Bell.
Yep, same. Actually, same everywhere I've ever lived in the US, come to think of it.
I think CFA has a cult following, due to politeness and quality. In all my rambling, I guess my point is that CFA could go back to pen and paper, non electric cash only cash registers, and still probably not miss a beat.
Given that we both know there will always be a line blocking the highway, every day but Sunday, what do all the sensors, machine learning, etc really enable? I guess I'm just asking for a bit of pragmatism, even where it obviously isn't my place to say so.
Yeah, but on the other hand, just a few days ago, I went into a Potbelly and had to leave because the point of sale system was "rebooting", and they could neither process credit cards nor even get the cash drawer open to handle sales the old fashioned way. Pretty much everyone in line behind me left too once they realized what was going on. Maybe if they'd had a Kubernetes cluster keeping their local systems online, they wouldn't have missed out on a busy lunch rush.
It sounds like you are skeptical that computers and automation may be useful to streamline and make more efficient what is otherwise a tedious, manual process with many logistical elements.
They also seem to be conveniently located in a lot of places. I never ate there until recently when I moved and wound up with one right next to my gym. Now I eat there multiple times a week, and yes the politeness is a big factor I’d say.
Dual redundant internet(!), the wifi gear to reliably support that many devices, the cost of more expensive equipment, the added cost of support contracts to keep those fancy sensors working, especially since in a lot of locations they're probably the only food service place using them.
...and then the infrastructure of running the cloud services to support things like every restaurant's K8 cluster hitting a git server constantly throughout a day.
...and then there's the overhead of three separate dev ops teams.
See my other comment [1] re: my experience at Potbelly. Restaurant usage is extremely bursty. For a lot of restaurants in business-y areas, you basically have a single spike of traffic from roughly 11:30 to 13:30 that drives >90% of the day's sales. You miss that, and you may as well write off the entire day's revenue. Investing in the redundancy (two internet connections, three servers, etc) to ensure that the point-of-sale system is online for that spike can be worth it, even for a relatively low-margin business. In fact, it might be more important for a low-margin business given the ephemeral nature of restaurant sales. It's not like e.g. Amazon, where if you're down for a few hours it doesn't matter, because customers will wait to buy their knick-knacks. If you're down for a lunch rush, you've lost that day's sales forever. After all, it's not as if customers are going to stay hungry and come back and buy two sandwiches tomorrow. They'll just go somewhere else for lunch.
Other restaurants could use some of the ML stuff. Starbucks for instance. I go there all the time ordered an iced mocha and 1 out of every 5-10 times I get a white mocha and they have to remake it. That’s 7$ they lose out even if it was all profit. This could be easily fixed with a camera and a ML model that ensures the drink is the right order.
It's not just CFA. I for some reason watched a Taco Bell video on LinkedIn that layed out one small problem regarding the ability to accept orders, and how a -completely- new k8s microservice architecture addressed it. This smells a lot of the same, mixed in with more tech overoptimism. GE had the same spiels 10 years ago, with better use cases, and realized most of it was a dead end.