how brick & mortar retailers can beat amazon at its own game
TRANSCRIPT
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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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Leveraging these Advantages is Hard
You’re stuck with Diffused Demand and one of two things is happening:
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Leveraging these Advantages is Hard
You’re stuck with Diffused Demand and one of two things is happening:
• Inventory isn’t where you need it, causing stock outs.
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Leveraging these Advantages is Hard
You’re stuck with Diffused Demand and one of two things is happening:
• 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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Virtual Pooling: 4 Pieces of the Puzzle
2. Throughput• Process as many orders as possible• Limited processing capacity at a stores• Can raise shipping costs
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Virtual Pooling: 4 Pieces of the Puzzle
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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Virtual Pooling: 4 Pieces of the Puzzle
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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Virtual Pooling: 4 Pieces of the Puzzle
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
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(IN
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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
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PPLY
(IN
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TORY
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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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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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www.celect.com | blog.celect.com
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