How Brick & Mortar Retailers Can Beat Amazon at Its Own Game

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How Brick and Mortar Retailers Can Beat

Amazon at Its Own GameVivek Farias, CTO, Celect

John Andrews, CEO, Celect

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JOHN ANDREWSCEO, Celect

VP Product, Oracle CommerceVP Product & Marketing, Endeca

VIVEK FARIASCo-Founder & CTO, Celect

Robert N. Noyce Professor, MIT SloanPhD in EE, Stanford University

• Who we are• Predictive analytics SaaS platform

for retail• Based in Boston, MA• Venture-backed, technology out of

MIT• Awards & Recognitions• MIT Computer Science and

Artificial Intelligence (CSAIL) Top 50 Greatest Innovations

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What is Amazon’s Game? A network of DCs and last-mile logistics partners that work well at scale.

Distant fulfillment center

Sortation center Delivery via Carrier or Amazon Flex

Customer

City storefront Delivery via Amazon Flex

Customer

Local fulfillment center

Delivery via Carrier

Customer

1.1 Billion Orders per Year

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What is Amazon’s Game?

• RISK POOLING• Demand for large

chunks of population served out of a relatively small set of Fulfillment Centers• Minimizes unsold

inventory

Indiana FC target area

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You are Not Amazon

Which isn’t a bad thing.

Here’s two reasons why…

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Reason #1

You are the expert in your domain

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Reason #2

You have a physical presence

• 42% of in-store customers showroom, we must accept this reality.

• Store location puts you much closer to the customer.

Locations of Home Depot & Lowes in the Tri-state Area

Source: http://www.planetizen.com/

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Leveraging these Advantages is Hard

You have a hot new line of graphic t-shirts launching next season. Which scenario would you prefer?

Pooled DemandAn average demand of 3,000 units spread across 3 stores

ORDiffused Demand

An average demand of 3,000 units spread across 1,000 stores

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You’re stuck with Diffused Demand and one of two things is happening:

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• Inventory isn’t where you need it, causing stock outs.

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• Inventory isn’t where you need it, causing stock outs.

• Too much inventory was bought, resulting in markdowns.

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The Solution: Virtual Pooling

Transforming an environment with Diffused Demandinto one with Virtually Pooled Demand.

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The Solution: Virtual Pooling

Requires two key ingredients:

1. Predictive Analytics

2. Real-Time Optimization

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Virtual Pooling with a Typical Order Management System (OMS)

1,000 UNITSAVAILABLE

A SIMPLE IN-STORE PURCHASE

WALKS INTO STORE

TO BUY A TV

PURCHASED

999 UNITSAVAILABLE

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Virtual Pooling with a Typical OMS

AN ONLINE PURCHASE

OMS

STORE 1

WAREHOUSE

STORE 2

CUSTOMER ORDERS A TV

ONLINE

• Where do we fulfill the order from?

• A store? A warehouse?

• Which one and how do we avoid a split shipment?

Short time to customerHigh weeks of supply

Long time to customerHigh weeks of supply

Short time to customerLow weeks of supply

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Virtual Pooling: 4 Pieces of the Puzzle

1. Shipping Cost• Closer is better• Aggregated orders are better than split

orders• Will typically conflict with other critical

objectives

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2. Throughput• Process as many orders as possible• Limited processing capacity at a stores• Can raise shipping costs

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3. Delay• Minimize the time it takes to get to a

customer• Can raise shipping costs• Conflicts with the ability to satisfy in-

store demand

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4. Average Weeks-of-Supply• Ship out of locations with many weeks-of-

supply• Related: Ship out onesies• Speeds up inventory turns and maximizes

full price sell-through• Conflicts with shipping costs

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Average Weeks of Supply

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Is Virtual Pooling Really Possible with a Typical OMS?

Issue #1: Typical OMS is purely rules driven.

Issue #2: Works well on a few high priority objectives, but doesn’t scale well beyond that.

Issue #3: There’s no way of ‘sacrificing now’ for a future gain.

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It’s Usually One Extreme or Another

WEE

KS O

F SU

PPLY

(IN

VEN

TORY

)

THROUGHPUT

There’s a balance between the extremes – to maximize inventory turns and utilization.

OMS RULE:Maximize

Throughput

OMS RULE:Maximize Weeks

of Supply

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WEE

KS O

F SU

PPLY

(IN

VEN

TORY

)

Virtual Pooling with a Predictive OMS

8%

5%

Real-time OptimizationTrue Demand across all channels

THROUGHPUT

O

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Case Study: Vertical Fashion Retailer

• Goal: Optimize Order Fulfillment with respect to the following parameters:• Throughput / Units Shipped: Maximize utilization of network

capacity• Shipping Cost: Reduce shipping cost (ship closer and avoid

splitting• Onesies Shipped: Increase fulfillment of returned units not part

of original store assortment• Weeks of Supply: Maximize turns• Average Order Delay: Increase customer satisfaction

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Case Study: Vertical Fashion Retailer

Representative Day Status Quo Celect % Diff.

Throughput (units) 1,171 1,307 11.6%

Unit Shipping Cost $5.05 $4.61 -8.8%

Onesies Shipped 549 777 41.6%

Weeks of Supply 7.6 17.9 135%

Average delay 0.047 days -0.15 days -0.2 days

Comparative Results

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What should you take away?

1. The key to Amazon’s success is better Risk Pooling2. Brick and mortar retailers have fundamentally Diffused

Demand3. Modern predictive analytics can transform this demand

and create Virtually Pooled Demand

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Predictive Analytics for Retail

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The Most Fundamental Task in Retail

The Right Product

The Right Place

The Right Person

The Right Price

The Right Time

Goal: Avoid Stock-outs and Markdowns Solution: True Demand Prediction

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Understand True Demand

A Choice Model tells you what a customer would prefer to buy when given the choice.

Today, you only know what a customer bought

✗✔

✗

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Celect Optimization Platform

AssortmentOptimization

Build robust assortments specifically optimized for

the foot traffic in each individual store

Predictive Personalization

Real-time online recommendations based on individual customer

preferences

Markdown OptimizationOptimize markdowns

while maximizing conversions and

revenues

CELECT OPTIMIZATION PLATFORM

FulfillmentOptimization

Fulfill from stores based on customer demand, without negatively impacting store

assortments

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[email protected]

www.celect.com | blog.celect.com

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How Brick & Mortar Retailers Can Beat Amazon at Its Own Game

Business

Transcript of How Brick & Mortar Retailers Can Beat Amazon at Its Own Game