delivering logistics insights at instacart...i economics, logistics and data supply & demand...

Delivering Logistics Insights at Instacart

Eric Rynerson

Senior Data Scientist

Instacart

# T C 1 8

Agenda

Personal Introduction

Instacart Overview

Logistics at Instacart: Systems and Goals

Logistics at Instacart: Metrics and the Efficiency Frontier

Example: Realtime Staffing

Example: Cancelation forecast

I Economics, Logistics and Data

Supply & Demand Engineering, Logistics, Shopper Incentives

Student Retention, Operational Efficiency

Logistics, Customer Retention, Operational Efficiency

Mathematical Economics

Agenda

Personal Introduction

Instacart Overview

Logistics at Instacart: Systems and Goals

Logistics at Instacart: Metrics and the Efficiency Frontier

Example: Realtime Staffing

Example: Cancelation forecast

Instacart Overview: Customer view

Instacart Overview: Shopping for a customer

We deliver millions of orders on a few hours’ notice

Agenda

Personal Introduction

Instacart Overview

Logistics at Instacart: Systems and Goals

Logistics at Instacart: Metrics and the Efficiency Frontier

Example: Realtime Staffing

Example: Cancelation forecast

Our logistics systems aim to meet unpredictable demand with maximum consistency and efficiency

Shopper Locations During Delivery

San Francisco Austin Boston Miami

Marketplace forecast estimates scale of demand

Supply Planning system provides advanced notice

Realtime systems adjust targets and pricing leadingup to shifts, delivery windows

Agenda

Personal Introduction

Instacart Overview

Logistics at Instacart: Systems and Goals

Logistics at Instacart: Metrics and the Efficiency Frontier

Example: Realtime Staffing

Example: Cancelation forecast

What missed opportunity looks like for us

Supply < Demand: Lost DeliveriesSupply > Demand: Idleness

The Efficiency Frontier describes the set of best-case scenarios we face. Moving the curve is a goal of our team

We use Tableau to view the real Efficiency Curve

Single city’s results over 2+ week period demonstrates range of outcomes

Difference in outcomes is due to imperfect forecasts and targeting

Tradeoff between utilization and availability is reliable

Dashboard audience includes range of stakeholders working on efficiency

Data Scientists

Machine Learning Engineers

Software

Engineers

Product Managers

Agenda

Personal Introduction

Instacart Overview

Logistics at Instacart: Systems and Goals

Logistics at Instacart: Metrics and the Efficiency Frontier

Example: Realtime Staffing

Example: Cancelation forecast

Realtime signals used to revise demand forecastclose to shift hour

Realtime Signals Generate Improved Forecast

Scaling tools allow short-term staffing changes

Upscaling usually entails asking shoppers already on a shift if they can extend it

Downscaling entails communicating anticipated slowness to shoppers

Both are to be used sparingly!

Realtime demand forecasts means better, earlier signals

Realtime staffing provided opportunity to move the frontier

City/day randomization used to create A/B test to measure impact before rollout decision

(see our blog for details)

Full rollout shown in this presentation for simplicity

Week before launch of new model shown alone in dark blue at right

Realtime staffing → improvement in tradeoff

Light blue:

Week prior to launch

Dark blue:

Week after launch

Higher utilization with the same or better availability!

Improvement in Efficiency Frontier itself

(up and to the right)

Agenda

Personal Introduction

Instacart Overview

Logistics at Instacart: Systems and Goals

Logistics at Instacart: Metrics and the Efficiency Frontier

Example: Realtime Staffing

Example: Cancelation forecast

We forecast cancelation to staff appropriately

Shoppers often cancel their shift at the last minute so we overstaff proportionately

Cancelation forecast bias meant we were overstaffed more often than not

High availability of delivery windows, but poor utilization

Fixing the bug allowed us to make the desired tradeoff

Forecasted cancelation

Actual cancelation

Staffed over target

Removed cancelation bias --> lateral shift

Only lateral movement along frontier

This is due to improved ability to hit target

No change in Efficiency Frontier itself

Higher shopper utilization comes at expense of lower availability for customers

No order left behind; no shopper left idle

L E AR N M O R E O N O U R B L O G !

https://tech.instacart.com/no-order-left-behind-no-shopper-left-idle-24ba0600f04f

Space, Time and Grocerieshttps://tech.instacart.com/space-time-and-groceries-a315925acf3a

Leveraging Elastic Demand for Forecastinghttps://tech.instacart.com/leveraging-elastic-demand-for-forecasting-6278b45f805f

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Thank you!eric@instacart.com