Smart Suggestions - Part I

Transcript

1. Driver navigation systems for last-mile shipping efficiency Mid-term Presentation | Sricharan Chiruvolu | BL.EN.U4CSE12505
2. Company Overview 2
3. • Hyper-local logistics service. • One of the largest fleet of delivery drivers. • Merchant and driver platforms. • B2B. Food, Groceries…11 others. • 25000 orders a day. • McDonalds, Pizza Hut, KFC…4000 others. • Tech Stack: Rails, MySql, MongoDb, Redis, Angular, Android, Scala, NodeJS and Django. 3
4. Tasks and Projects 4
5. Tasks and Projects 1. Driver reassignment by merchants for a live order. 2. Driver navigation to last-mile customer location. 3. Smart suggest for drivers. 4. Order stockout analytics fixes. (End-term presentation) 5. Cross locality preferences analytics. (End-term presentation) 5
6. Driver Reassignment 6
7. Driver Reassignment • Add an option for the merchant to reassign the driver for an order during cancellation. • Decrease the number of cancellations. • Assign to the next nearest free driver. • Reasons for reassignment: 1. Delivery driver refused to come. 2. Driver didn't come for a long time. • Reassignment workflow. 7
8. start stop Reassign Order? active_leg? && next_driver_available? && correct_cancellation_reason? Cancel Order Order reassign to next free driver active_leg? Cancel Order? Error Message No No Yes Yes
9. Driver Navigation to Customer Location 9
10. Driver Navigation • Last mile navigation. • Capture customer location. • Send customer location for a live trip. • MQTT(MQ Telemetry Transport) and GCM (Google Cloud Messaging) protocols. 10
11. Last-mile navigation 11
12. MQTT activity at Runnr 12
13. Smart suggest 13
14. Smart suggestions • Demand - supply gaps in localities. • Demand prediction and supply input. • Calculating demand and supply percentile. • Surge pricing prerequisite. • Driver application and operations dashboard views. 14
15. Smart suggest on operations dashboard 15
16. Smart suggest on driver application 16
17. Smart suggest on driver application (offline flow) 17
18. Next… • Stockout data fixes: • Find stockout orders and predict stockout quotient. • Cross-locality orders: • Cost prediction (machine learning) 18
19. Thank you. 19 NAME SRICHARAN (BL.EN.U4CSE12505) EXT. GUIDE Mr. MUKUNDA & Mr. MOHIT AGARWAL INT. GUIDE Ms. SANGITA KHARE