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

Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
How Brick and Mortar
Retailers Can Beat
Amazon at Its Own Game
Vivek Farias, CTO, Celect
John Andrews, CEO, Celect

JOHN ANDREWS
CEO, Celect
VP Product, Oracle Commerce
VP Product & Marketing, Endeca
VIVEK FARIAS
Co-Founder & CTO, Celect
Robert N. Noyce Professor, MIT Sloan
PhD 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

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

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

You are Not Amazon
Which isn’t a bad thing.
Here’s two reasons why…

Reason #1
You are the expert in your domain

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/

Leveraging these Advantages is Hard
You have a hot new line of graphic t-shirts launching next
season. Which scenario would you prefer?
Pooled Demand
An average demand of 3,000 units spread across 3 stores
OR
Diffused Demand
An average demand of 3,000 units spread across 1,000 stores

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

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

• Inventory isn’t where you need it,
causing stock outs.
• Too much inventory was bought,
resulting in markdowns.

The Solution: Virtual Pooling
Transforming an environment with Diffused Demand
into one with Virtually Pooled Demand.

The Solution: Virtual Pooling
Requires two key ingredients:
1. Predictive Analytics
2. Real-Time Optimization

Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Virtual Pooling with a Typical Order
Management System (OMS)
1,000 UNITS
AVAILABLE
A SIMPLE IN-STORE PURCHASE
WALKS INTO STORE
TO BUY A TV
PURCHASED
999 UNITS
AVAILABLE

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 customer
High weeks of supply
Long time to customer
High weeks of supply
Short time to customer
Low weeks of supply

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

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

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

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

Average Weeks
of Supply

Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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.

It’s Usually One Extreme or Another
WEEKSOFSUPPLY
(INVENTORY)
THROUGHPUT
There’s a balance between the
extremes – to maximize inventory
turns and utilization.
OMS RULE:
Maximize
Throughput
OMS RULE:
Maximize Weeks
of Supply

WEEKSOFSUPPLY
(INVENTORY)
Virtual Pooling with a Predictive OMS
8%
5%
Real-time Optimization
True Demand across all channels
THROUGHPUT
O

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

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

What should you take away?
1. The key to Amazon’s success is better Risk Pooling
2. Brick and mortar retailers have fundamentally Diffused
Demand
3. Modern predictive analytics can transform this demand
and create Virtually Pooled Demand

Predictive Analytics for Retail

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

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
✗✔
✗

Celect Optimization Platform
Assortment
Optimization
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
Optimization
Optimize markdowns
while maximizing
conversions and
revenues
CELECT OPTIMIZATION PLATFORM
Fulfillment
Optimization
Fulfill from stores based on
customer demand, without
negatively impacting store
assortments

Questions?
info@celect.com
www.celect.com | blog.celect.com

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

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