Logistics conference

First edition: covering fleet management and mobility

Predicting Order Batchability

Submitted by sunil rathee (@ratheesunil) on Saturday, 20 October 2018

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Technical level

Intermediate

Section

Crisp talk

Status

Confirmed & Scheduled

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Abstract

For a business looking to provide delightful user experience in the online food delivery space, it is paramount important to meet the promises. This talk will be mainly focusing on the time compliant deliveries at high demand and demonstrates how not meeting the promises effect the business in the short or long run in terms of Churn, Repeat, Net Promoter Score(NPS), etc. We will discuss about the necessity of batching the orders and its advantage and disadvantages on the system. Finally we will go through how we build a Deep Learning system, FOPS, to predict the orders which are going to batch and its effect on the overall environment of the online food delivery space

Outline

We will talk about how we solved the problem with the help of Machine Learning/AI, Operations and Engineering.
Content of the talk will be around:
1) Batching Overview.
2) Problems with the existing framework
3) Customer Experience angle in batching
4) First Order batching Prediction
5) How First Order batching Prediction turned the numbers towards Swiggy
6) Next Steps

Speaker bio

Senior Data Scientist at Swiggy

Slides

https://drive.google.com/file/d/1mePWQLKrQrHbEI07UhetZS15pVMsDNo_/view?usp=sharing

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