Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
How Brick and Mortar
Retailers Can Beat
Amazon at Its Own Game
Vivek ...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
JOHN ANDREWS
CEO, Celect
VP Product, Oracle Commerce
VP Product & Mar...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
What is Amazon’s Game?
A network of DCs and last-
mile logistics part...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
What is Amazon’s Game?
• RISK POOLING
• Demand for large
chunks of po...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
You are Not Amazon
Which isn’t a bad thing.
Here’s two reasons why…
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Reason #1
You are the expert in your domain
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Reason #2
You have a physical presence
• 42% of in-store customers
sh...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Leveraging these Advantages is Hard
You have a hot new line of graphi...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Leveraging these Advantages is Hard
You’re stuck with Diffused Demand...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Leveraging these Advantages is Hard
You’re stuck with Diffused Demand...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Leveraging these Advantages is Hard
You’re stuck with Diffused Demand...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
The Solution: Virtual Pooling
Transforming an environment with Diffus...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
The Solution: Virtual Pooling
Requires two key ingredients:
1. Predic...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Virtual Pooling with a Typical Order
Management System (OMS)
1,000 UN...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Virtual Pooling with a Typical OMS
AN ONLINE PURCHASE
OMS
STORE 1
WAR...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Virtual Pooling: 4 Pieces of the Puzzle
1. Shipping Cost
• Closer is ...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Virtual Pooling: 4 Pieces of the Puzzle
2. Throughput
• Process as ma...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Virtual Pooling: 4 Pieces of the Puzzle
3. Delay
• Minimize the time ...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Virtual Pooling: 4 Pieces of the Puzzle
4. Average Weeks-of-Supply
• ...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Virtual Pooling: 4 Pieces of the Puzzle
Average Weeks
of Supply
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Is Virtual Pooling Really Possible with a
Typical OMS?
Issue #1: Typi...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
It’s Usually One Extreme or Another
WEEKSOFSUPPLY
(INVENTORY)
THROUGH...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
WEEKSOFSUPPLY
(INVENTORY)
Virtual Pooling with a Predictive OMS
8%
5%...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Case Study: Vertical Fashion Retailer
• Goal: Optimize Order Fulfillm...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Case Study: Vertical Fashion Retailer
Representative Day Status Quo C...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Case Study: Vertical Fashion Retailer
Representative Day Status Quo C...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
What should you take away?
1. The key to Amazon’s success is better R...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Predictive Analytics for Retail
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
The Most Fundamental Task in Retail
The Right Product
The Right Place...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Understand True Demand
A Choice Model tells you what a customer would...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Celect Optimization Platform
Assortment
Optimization
Build robust ass...
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Questions?
info@celect.com
www.celect.com | blog.celect.com
Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17
Prochain SlideShare
Chargement dans…5
×

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

453 vues

Publié le

From NRF BIG Show 2017.

Celect is a cloud-based, predictive analytics SaaS platform that is helping retailers optimize overall inventory portfolios in stores and across the supply chain.

Learn more about how we are doing this at http://www.celect.com

Publié dans : Business
  • Login to see the comments

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

  1. 1. 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
  2. 2. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  3. 3. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  4. 4. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  5. 5. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 You are Not Amazon Which isn’t a bad thing. Here’s two reasons why…
  6. 6. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Reason #1 You are the expert in your domain
  7. 7. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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/
  8. 8. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  9. 9. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Leveraging these Advantages is Hard You’re stuck with Diffused Demand and one of two things is happening:
  10. 10. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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.
  11. 11. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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.
  12. 12. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 The Solution: Virtual Pooling Transforming an environment with Diffused Demand into one with Virtually Pooled Demand.
  13. 13. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 The Solution: Virtual Pooling Requires two key ingredients: 1. Predictive Analytics 2. Real-Time Optimization
  14. 14. 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
  15. 15. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  16. 16. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  17. 17. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  18. 18. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  19. 19. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  20. 20. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Virtual Pooling: 4 Pieces of the Puzzle Average Weeks of Supply
  21. 21. 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.
  22. 22. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  23. 23. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 WEEKSOFSUPPLY (INVENTORY) Virtual Pooling with a Predictive OMS 8% 5% Real-time Optimization True Demand across all channels THROUGHPUT O
  24. 24. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  25. 25. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  26. 26. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  27. 27. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  28. 28. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Predictive Analytics for Retail
  29. 29. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  30. 30. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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 ✗✔ ✗
  31. 31. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 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
  32. 32. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17 Questions? info@celect.com www.celect.com | blog.celect.com
  33. 33. Retail’sBIGShow2017|#nrf17Retail’sBIGShow2017|#nrf17

×