> The goal of the Restaurant Edge Compute platform was to create a robust platform in each restaurant where our DevOps Product teams could deploy and manage applications to help Operators and Team Members keep pace with ever-growing business, whether in the kitchen, the supply chain, or in directly serving customers.<p>> (Previous article) Our hypothesis: By making smarter kitchen equipment we can collect more data. By applying data to our restaurant, we can build more intelligent systems. By building more intelligent systems, we can better scale our business.<p>I must admit, from an outsiders perspective, it really sounds like a bunch of buzzwords justifying a solution in search of a problem. Their examples of forecasting waffle fries reminds me of a failed startup that forecasted how many checkout lines to open via computer vision (which I can't find on Google). In the end, it turned out it was a lot easier for a human manager to simply open a new line when required, and the computer vision provided the wrong forecasting to be accurate. I wonder what CFAs success criteria and metrics are for this project.<p>Tech-wise, wouldn't it be a lot simpler to do a single node, single application that gets updated via something like RAUC? Especially if you have a small team (which they emphasized), it seems to me like adding a Kubernetes cluster at the edge adds complication without much benefit, other than "redundancy" (how redundant is a single rack with the same power source anyways?). Also, how would they get an important security update to the host, if it becomes necessary?<p>It's a lot of nitpicks, but the project overall is very cool. Sounds like they solved a lot of hard tech problems and executed well on the ops.
They talked about this at a Kubecon iirc. I wasn’t sure if it was an elaborate prank but they were seriously smart folks who patiently explained why they needed to do this and I remember being very impressed.
HN discussion on their initial 2018 blog post: <a href="https://news.ycombinator.com/item?id=17820626" rel="nofollow">https://news.ycombinator.com/item?id=17820626</a> (570 points, 392 comments)
<p><pre><code> Our hypothesis: By making smarter kitchen equipment we can collect more data. By applying data to our restaurant, we can build more intelligent systems. By building more intelligent systems, we can better scale our business.
As a simple example, imagine a forecasting model that attempts to predict how many Waffle Fries (or replace with your favorite Chick-fil-A product) should be cooked over every minute of the day. The forecast is created by an analytics process running in the cloud that uses transaction-level sales data from many restaurants. This forecast can most certainly be produced with a little work. Unfortunately, it is not accurate enough to actually drive food production. Sales in Chick-fil-A restaurants are prone to many traffic spikes and are significantly affected by local events (traffic, sports, weather, etc.).
However, if we were to collect data from our point-of-sale system’s keystrokes in real-time to understand current demand, add data from the fryers about work in progress inventory, and then micro-adjust the initial forecast in-restaurant, we would be able to get a much more accurate picture of what we should cook at any given moment. This data can then be used to give a much more intelligent display to a restaurant team member that is responsible for cooking fries (for example), or perhaps to drive cooking automation in the future.
Goals like this led us to develop an Internet of Things (IOT) platform for our restaurants. To successfully scale our business we need the ability to 1) collect data and 2) use it to drive automation in the restaurant.
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The football game next door is over and the home team won? Start extra burgers in anticipation of hungry fans - great. I buy that.<p>The whole thing can be one app running on an iPad with multiple redundant data plans enabled, esims from AT&T and Verizon or whatever. You're going to need a touchscreen tablet for the POS anyway, no need for additional hardware or Kubernetes.