Mendocino Farms.
A field-based operations analysis identifying the hidden post-order queue as the main service constraint during peak periods.
The visible line was not the real bottleneck.
Mendocino Farms balances food quality, hospitality, and fast-casual speed. At the Totem Lake location, the visible ordering line often moved quickly, but guests still waited after ordering while food was prepared, checked, staged, and handed off.
The operational problem was improving post-order flow without sacrificing food quality, order accuracy, or the guest experience.
Where the wait formed.
Guests moved through ordering quickly, then entered a less visible post-order queue while food was produced, checked, staged, and handed off.
Guest arrival
Customer enters and decides what to order.
FOH ordering
Order is placed and sent downstream.
BOH production
Food is prepared and customized.
Accuracy check
Order is checked before staging.
Pickup / handoff
Finished orders are staged for dine-in, mobile, or delivery.
Exit
Guest leaves after receiving the order.
What I did.
Mapped the service flow
Tracked the customer journey from arrival and ordering through production, pickup, and exit.
Measured the constraint
Compared order-taking time, post-order wait, staffing, throughput, and work in process across observation periods.
Connected data to experience
Used queue psychology and customer-review patterns to explain why the hidden wait felt worse than the visible line.
Built recommendations
Focused improvements on BOH flow, pickup clarity, ticket sequencing, forecasting, and PDCA testing.
The constraint was downstream.
Front-of-house ordering was not the main constraint. The largest share of time happened after the register, where food production, checking, staging, and handoff created the hidden queue.
FOH improved. BOH wait stayed flat.
From lunch to dinner, FOH order time dropped, but post-order wait barely moved.
Adding front-of-house speed pushed guests into the same downstream constraint faster.
Recommendations.
Make the hidden queue visible
Use clearer wait expectations and order-progress communication.
Separate pickup by channel
Reduce confusion between dine-in, mobile, and delivery handoff.
Clarify ticket sequencing
Define how walk-in, app, pickup, and delivery orders are prioritized during peak periods.
Use seating as a buffer
Move guests away from the pickup area while BOH works at a steady pace.
Forecast by service period
Plan around morning, lunch, dinner, and known local demand patterns instead of only daily totals.
Test through PDCA
Pilot one change at a time and measure post-order wait, WIP, remakes, congestion, and staff feedback.
This project shows how I turn field observations into a clear operations diagnosis. I used process data, customer experience analysis, and operations frameworks to identify the real constraint and recommend practical improvements.
NotesThis was an academic operations analysis project about Mendocino Farms. It was not sponsored by Mendocino Farms, and I did not work for the company. The recommendation is based on observation, operations analysis, interview notes, and course project work.