Supply chain sessions #1 - Systems Analytics Global Leaders' Seminars: Professor Christopher Tang, UCLA (October 7, 2020)

Key Notes

• Faster Delivery, Smart Order Assignments on On-demand meal delivery platform

• Mobile / Internet-based / They do not own content
• High value, Zero ownership of assets
• Platform types (Ecommerce / Resource sharing)
• Value creation / Markets creation

• Money made by commissions / Free for customer
• Impact and benefits - Wider impact, New jobs creation

• Scopus / Google Scholar for papers
• Meal delivery platforms
• Order meals, App, list restaurants, delivery partner

• Platform overview
• Three-sided market
• Platform = Customers, Restaurants, Drivers
• Charge customers + Restaurants
• Required time (area of research)

• Platform operations
• App overview
• Price
• Order time 
• Delivery Time Estimation
• Preorder for next day 

• Polling to schedule drivers 
• Wait time at restaurants
• On-time / Delayed Delivery
• Factors affecting delivery performance
• Future customer perspectives

• Behavior for future orders
• Shangai city data

Insights

• Distribution for Long orders by time(By Distance)
• Distribution for Short orders by time  (By Distance)
• Peak Hours
• Average delivery time for different routes
• Average delivery time for different restaurants
• %% of Ontime delivery by time
• %% of Early vs Late orders
• Repeating orders 
• Pricing by peak hour / demand / rule based / recommendation based
• Offer conversions
• Revenue metrics by Restaurants
• Revenue metrics by Days
• Revenue metrics by Customer Segments
• Driver delivery metrics 
• Congestion levels of Traffic / Temperature / Seasonality
• Driver familiarity with the route

Use cases

• Customer churn
• Driver churn
• Next order prediction based on intervals
• Repeat order customer
• Rare customer
• Empirical work before prescriptive
• Performance on future orders
• Survival analysis

Models

Aalen Additive model

• Regression model for delivery performance time with features like experience, restaurant distance, order estimated time, waiting time

 

• Delivery experience vs Future orders relationship

Estimate delivery time for order

Driver location estimation for assignment

Total delivery time for order

Total delay

Maximum future orders

Discourage late delivery with incentives 

 

Predicting Shipping Time with Machine Learning

Package Delivery Time dataset

Using Machine Learning computing, we developed a model capable of improving the predictability of shipping times

Key Notes

The important milestones for this project are, in chronological order:

• The booking date, which is the date when the transport from origin to final destination is booked (called “book date” in the dataset);
• The receive date, which is the date when the cargo is received by the shipper at the port of origin (called “Actual Receipt Date” in the dataset);
• The gate-in date, which is the date when the cargo enters the yard where it will wait to be loaded on the vessel (called “Gate In Origin-Actual” in the dataset);
• The vessel departure date, which is the date when the cargo leaves the port of origin (called “Vessel Actual Departure” in the dataset)
• The vessel arrival date, which is the date when the cargo arrives at the port of destination (called “Vessel Actual Arrival-Actual” in the dataset)
• The unload date, which is the date when the cargo is unloaded from the vessel (“Container Unload From Vessel-Actual”)

Selecting relevant external factors

• Chinese New Year
• Port Congestion US
• Weather
• Positioning of the Container on the vessel

Selecting features for the Machine Learning model

• Average time spent at Port of origin (Port of origin mean) - The average is based on the different shipper and port combinations
• Standard deviation of time spent at Port of origin (Port of origin SD) - The standard deviation is based on the different shipper and port combinations
• Average time spent at Port of destination (Port of destination mean)
• Standard deviation of time spent at Port of destination (Port of destination SD)
• Average time spent per Route (Route mean)
• Schedule
• Origin Service
• Holiday
• Quarter
• Expected Time to port
• Port of origin late
• Port of origin latest
• Capacity of port of destination
• Late departure

From startup - https://nextbillion.ai/blog

Key Metrics

• Delivery time = Promised ETA ± Threshold time for the territory
• Promise kept metric = (Number of accurate orders / total orders of the day) X 100%
• For the end-consumer, the role played by an ETA begins even before the service request is placed
• For the delivery agent/driver, an accurate ETA sets a service promise that needs to be met.
• A great metric to track the impact of ETAs on pricing is the % of orders/rides that are priced accurately.

Keep Thinking!!!