E2.Time — Lines

Potential lines to Analyze

  • Oakland Chipotle
  • Entropy (Humans as Line)
  • Craig St. Sushi Fuku

Au Bon Pain

  • Wait > Order > Ticket > Retrieve > Pay

Tazza Café

  • Wait > Order/Pay > Ticket > Wait > Retrieve

Resnik Cafe

  • Wait >Order/Pay > Ticket > Wait > Retrieve



8 employees behind the counter

  • Dedicated grilled
  • Dedicated bagger “Expediters”
  • Dedicated registrar person
  • 2 makers
  • 1 floater maker “Linebacker”
  • 2 chefs

Even when the lines empties out and no one is entering the store, people stay at their stations except when necessary.

My burrito time: 3 mins 50 secs
Grilled always smiles when saying hi
“Hi there, what can I get you started with for today?”
Extremely fast turnover between new and old employees during shift switch
2 different non-gendered shirts (normal tee and buttons)
2 different hats (ball cap and military cap)
  • 2.3BPM
  • 134 BPH
Chipotle’s fastest restaurants currently run more than 350 transactions per hour at lunchtime, which equates to a ludicrous near-six transactions per minute. The nationwide average is currently somewhere between 110 and 120, according to Moran. But they’re getting faster, and faster, and faster. (ref)

People Tracking Approaches


Realtime Multi-Person Pose Estimation (pytorch)


The Business

Chipotle continues to refine the science of burrito velocity

“Over the first three months of 2014, the US Mexican-food chain saw an average increase of seven transactions per hour at both peak lunch and dinner hours — 12 to 1pm and 6 to 7pm, respectively. On Fridays, one of its busiest days of the week, Chipotle fielded 11 more customers per hour at lunchtime on average across its stores, a roughly 10% increase.”
Credit cards, for instance, are better than cash, because they’re faster.

How Chipotle is going to serve burritos faster, and faster, and faster

“Expediters.” That would be the extra person between the one who rolls your burrito and the one who rings up your order. Her job? Getting your drink, asking whether your order is for here or to go, and bagging your food.
“Linebackers.” The people who patrol the countertops, serving-ware, and bins of food, so the ones who are actually serving customers never turn their backs on them.
Mise en place.” What in a regular restaurant means setting out ingredients and utensils ready for use means, in Chipotle’s case, zero tolerance for not having absolutely everything in place ahead of lunch and dinner rush hours.
“Aces in their places.” A commitment to having what each branch considers its top servers in the most important positions at peak times, so there are no trainees working at burrito rush hour.

April 13th

There are three Chipotle restaurants (Pitt Campus, Baum Blvd., East Side/Liberty) in Pittsburgh that I’ve eaten while living in Pittsburgh. There’s also a fourth downtown that I’ve never been to and one close to CMU that open 51 minutes ago. I had previously been planning to survey the location by Pitt Campus, but I recently visited the East Side location and think it will be especially ‘capture-able’ with the method I’ve been investigating.

Use Analysis:

  • to bring benefits from Chipotle across to new location in group project with Marisa.

April 15th

I’ve been playing with OpenTSPS to little success and also stumbled upon something called OpenPTrack. I’m currently preparing tracking equipment to go record the east side chipotle location, but many questions remain:

  1. How will the recorded visuals be converted into useable quantitate information?
  2. What visualization methods will be needed to process the presumed quantitative data?
  3. What stories will be derivable from the quantitate information?
  4. How will I tell those stories?

April 16th

Feeling somewhat defeated on the quantitative-from-visual approach, I’m thinking about using some more visual photographically based approaches (many of these come from the book PHOTOVIZ):

  • Jim Houser
  • Peter Funch, Babel Tales
  • Timo Arnall, Immaterials: Ghost in the Field
  • Dennis Hlysky, Swarms of Birds
  • Bill Lytton, Vision of the Crowd (Frame Averaging)
  • Cy Kuckenbaker, All in the now
  • Menno Aden (Room portraits from above)
  • Michael H. Rohde (Room portraits from below)
  • Marc Dorf (PATH)
  • Adam Magyar, Slit-Scanning in Urban Flow
  • Martin Roemers, Metropolis Asia
  • Stephen Orlando, Motion Exposure
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