Your restaurant is experiencing long lines for pickup orders. How would you decrease the wait time?

First, let us think about the structure to the answer:
1. Ask Clarifying questions to narrow scope of problem
2. What are the goals here? apart from reducing wait time?
3. Who are the users? - what is the user journey?
4. Across the journey, what are the users potential issues/pain points
5. Device solutions
6. Summarize

Clarifying Questions:
1. How long have we noticed this issue? Is this seasonal? - Since the last 2 weeks
2. Have we seen an increase in orders coming through contributing to long wait times? - Assume No.
3. Define Long wait times? - Average of 15 minutes per customer/order
4. Where are the orders coming from? - Restaurant App? In person Order for take out? Gig Economy apps like Doordash or Uber eats? - Assume All online options (eliminating in person orders)
5. Who are the people picking up the orders  - Ordering customers as well as delivery drivers
6. Any physical constraints at Restaurant such as lack of labor? - Assume no.

Goals:
The primary goal is to ensure customer satisfaction during pickup and reduce time from order to delivery.

Users:
1. Restaurant and workers (Supply Side)
2. Ordering Customers (Demand Side)
3. Delivery drivers and
4. Delivery Apps
Focussing on the Demand Side as this is the core impacted group - We should also keep in mind the delivery drivers as we think of solutions as this affects them as well.

User Journey:
1. Open App
2. Choose restaurant
3. Order food and pay
4. Get estimate for Pickup
5. Go to restaurant (for self pick-up) or wait for delivery driver to get to the restaurant to pick up food
6. Pickup/ (deliver to customer)
Our current issue starts at step 4 of the customer journey. Here are a few things that can go wrong or cause pain for the customers.

Pain Points:
1. Delivery/pickup estimate is inaccurate causing extended wait times
2. No notifications about delay in pickup readiness
3. no notifications when pickup is ready - need to continuously monitor
4. Increase in foot traffic at restaurant can cause confusion in orders and wrong pickups

Solutions:
1. Ranking long pending orders - Build a ranking system that provides insights and notifications on long pending orders so that they can be serviced ASAP
2. Real Time updates - Provide updated tracking capabilities that uses an ML model to understand order volume at any given time, time per order and provides real time updates to customers on order delays or time to complete etc.
3. Notification to arrive at store - Once order is complete and ready for pickup, provide customer with an alloted number to pickup their order and notifications to leave home to come pick up - there by, this helps customers get their orders correctly as well as reduces time at store/waiting times.

Based on a combination of Impact, reach and effort, I would prioritize the following:
Solution 1: Impact - Medium, Reach - Low (internal feature), Effort - Low --> Priority: P1
Solution 2: Impact - High, Reach - High, Effort - High (requires ML work) --> Priority: MVP
Solution 3: Impact - High, Reach - High, Effort - Medium --> Priority: MVP

Metrics to focus on:
Primary -
Average wait time per order - Track over time to see if this has reduced
Supplementary -
Number of customer cancellations due to long pending orders
Orders trend  - Are we losing orders because of this?
NPS scores - Customer Satisfaction scores

Summary:
In summary - we looked at the different users that would be affected by this problem and chose the demand side of the customers as they are the most impacted. We also looked at the potential painpoints and prioritized them. Based on the pain points, we devised 3 solutions which were graded on a combination of Impact, Reach and Effort and decided that we would need to build out real time notification mechanisms that would allow the customer to know exactly what stage of completion their order is and when they can get to the store to pick up their order.
To understand if our solutions have an impact or not, we will focus on looking at the average wait time per order/customer and see if the trend is going down while also simultaneously tracking metrics like cancellations and order trends.