I think you are right that this blog post doesn't lay out a specific use case to justify this setup in a restaurant- that does not appear to be the goal of the post. An exploration of problems and requirements is probably what you're looking for.
They did drop a few hints, though. They are just hints, and going by what's in the blog post, a bit of imagination is required to fill in some of the gaps. However, if you do that, I can envision a few possible needs for low-latency edge computing that are interesting and forward-looking:
- Kitchen automation. Sensors that monitor food being cooked, and help implement a pipeline to ensure quality is consistent. (example here is that neural net monitoring the staleness of fries).
- Inventory automation. Monitor and automatically restock items. Track purchases and ingredient usage in real time.
- Data analytics. Collect detailed time-series data about food quality, facilities maintenance, foot traffic, noise levels, security, etc.
These aren't standard restaurant problems. However, it's an interesting approach to scaling data analysis and automation in the physical world. All of these use cases assume the need for "real-time" data, and they assume that such a thing has business value (which is where I think your critique is coming from).
I'm not affiliated with Chick Fil-A, so I don't know how correct any of my speculation is, but that's my takeaway from reading the article.
They did drop a few hints, though. They are just hints, and going by what's in the blog post, a bit of imagination is required to fill in some of the gaps. However, if you do that, I can envision a few possible needs for low-latency edge computing that are interesting and forward-looking:
- Kitchen automation. Sensors that monitor food being cooked, and help implement a pipeline to ensure quality is consistent. (example here is that neural net monitoring the staleness of fries).
- Inventory automation. Monitor and automatically restock items. Track purchases and ingredient usage in real time.
- Data analytics. Collect detailed time-series data about food quality, facilities maintenance, foot traffic, noise levels, security, etc.
These aren't standard restaurant problems. However, it's an interesting approach to scaling data analysis and automation in the physical world. All of these use cases assume the need for "real-time" data, and they assume that such a thing has business value (which is where I think your critique is coming from).
I'm not affiliated with Chick Fil-A, so I don't know how correct any of my speculation is, but that's my takeaway from reading the article.