1. PepsiCo & Safeway A “Big Data” Collaboration To Reduce Out-Of-Stocks Using Visualization Techniques
Carl Graziani
SVP Supply Chain, Safeway Inc.
John Phillips
SVP, Customer Supply Chain &
Global GTM, PepsiCo

2. There Is A Lot Of Data For Collaboration
Every year ~40M shoppers come through the doors of ~1600 stores under the 8 Safeway banners, which equates to ~1.4billion transactions a year with ~eight items per transaction.
Safeway holds ~seven billion transactions with 56 billion line item records.
Hundreds of suppliers provide 100,0000 products for shoppers S

3. Safeway Data Sharing Programs
Data Sharing Programs In Place With CPG Vendors In Marketing & Supply Chain
Share POS And Inventory Data Marketing Data At Household And Segment Level
Aimed At Reducing OOS And Inventory, Decisions On Assortment, Increasing Sales Pricing, And Promotion
Supply Chain Data Sharing
Shopper Insight / Loyalty Data Sharing
Used By Customer Supply Chain Teams Customer Marketing Teams
Typically Cost Nothing To Participate A Fee To Participate
Data sharing is not only being used on the customer loyalty/marketing side of the business, but also on the Supply Chain side.
On the SC side, the categories have to be right, the supply chain has to be right, and the shelf has to be right
Collaboration and coordination are essential to execution S

4. Safeway Data Visibility Program
20 Vendors Are Now Receiving Data From Safeway
Collaborative Process With Safeway Supply Chain To Request Firm Orders To Reduce OOS’s, Distribution Voids & Pre-Event Allocations
Working With PepsiCo & Deloitte On A Data Visualization Program
Collaborative Process With Safeway Marketing Groups For Specific Competitive Responses
Vendors Are Beginning To Report Fourth Quarter Benefits Back To Safeway
Data Visibility is a strategic Safeway platform for sharing data with vendors
Launched And In Place With 20 key Suppliers
Safeway’s Expectation That Suppliers Come To The Table With Insights & Execution Plans
Examples include OOS reductions, filling distribution voids, firming out product for promos, etc. S

5. Data Visibility Core Competencies
Relatively New Program Even Though Data Sharing Is Not A New Concept
Innovative
Opportunity For Increased Collaboration Between Supplier & Retailer
Collaborative
Real Time Visibility Can Influence Many Areas Including Assortment, Inventory, Distribution & Promotion
Comprehensive
Vendors To Take The Lead In Driving Insights Through Leveraged Data Sharing
Program Enhanced Through Vendor Feedback
Strategic S

6. PepsiCo & Safeway Are Collaborating Further
PepsiCo Is Key Collaborative Partner
PepsiCo’s use of the Data Visibility pushes Safeway to be better
DV caused us to re-examine and adjust current methodologies and processes P

7. PepsiCo’s BIG BRANDS and BIG PROMISE requires that we use BIG DATA

8. 360° Retail Execution™ Delivers “Big Data” For Driving Performance
Every Item / Every Store / Every Day
31+ Retailers Sharing Daily Data
53,234+ Retail Stores
130 Million Saleable Units Every Week
Enterprise Program Driven From Center
Annotated With Attributes & Hierarchies
Activated With Account Teams, Supply Chain Field Execution P

9. PepsiCo Believes In The Power Of Data & Analytics To Drive Supply Chain
Near Real-time Data & Dashboards
Identifies Actual & Predictive OOS & Overstock Issues At SKU/ Store Level
Enables Root Cause Analysis
Actionable Tasks Prioritized By Profitability
Drive Sales & Execution
New Product Introductions
Closing Distribution Voids
Promotion Execution & Effectiveness
Store Merchandising & Replenishment
Order & Shipment Forecasts
Retail Pricing Compliance
“360° Retail Execution” is the name of PepsiCo’s DDSN program…
Active in retailers across multiple channels (i.e., Grocery, Drug, Mass, Club, Military…) P

10. Demand Signal Repository (DSR) Overview
Retailer Shares POS Data
DSR
Cleanses & Stores Data
OOS
Phantom Inventory
Promo Execution
NPI
Dashboards
Shared Scorecards
Joint Value Creation
In-Store Execution
Alerts
Advanced Analytics
BI Tools
Supply Chain
Account Team
Improved
Shopper
Experience
Quick overview of a DSR for anyone in the audience who would like a quick refresher… P

11. Examples Of Driving Value Through Shared Data
Filling Distribution Voids Through Scripted Replenishments
Field Teams Are Leveraging Gap Scans
Increasing Forecast Accuracy & Driving Supply Chain Efficiencies Through True CPFR & VMI
Safeway and Pepsi are veterans in data sharing are driving value with supply chain and in-store performance from their collaboration
Scripted Replenishments Are Filling Distribution Voids
Visibility to Safeway Gap Scans Helps Local Field Teams Put Closure Plans In Place
Thru True CPFR/VMI Delivers Is Increasing Forecast Accuracy & Driving Supply Chain Efficiencies
Better Forecasting (Carl Graziani)
Improved Forecasting Approaches Are Resulting From The Safeway / PepsiCo Partnership P

12. Filling Distribution Voids Through Scripted Replenishments
PepsiCo 360° Analytics Reveal D-Voids
Item-Store-Day Analysis
Planogram Compared To Sell-thru
Missing Items Identified
Potential Lost Sales Calculated
PepsiCo & Safeway Resolve D-Voids
Jointly Develop DC Force-Shipments
Determine Which Products
Agree On Quantities Needed P

13. Filling Distribution Voids Through Scripted Replenishments
Opportunities Identified
12 Brands Analyzed
Weekly Store Lost Sales Amounted To Several Thousand Dollars Per Item
Actions Taken
Safeway Pushed 2,339 Store/SKUs Across 49 Items
PepsiCo VMI Replenished DC Inventory
Results
Recaptured Sales = $500K -$1.5M P

14. Collaboration Has Led To A Change In Safeway’s Internal Processes, Resulting In Benefits Along The Entire Supply Chain
Higher Forecast Accuracy
MAPE Reduced 20%
Bias Reduced 15%
Improved Store In-stocks
Less Days Of Supply
DOS Reduced 15% YOY
Shorter Order Lead Time
Improved Service Levels
+1.1% Service Level Improvement
Feedback from Pepsi led Safeway to revisit its forecasting processes and create a forecasting center of excellence.
Learned from Pepsi that Safeway was sharing 3 different forecasts, from several different functional groups, plus Pepsi had their own forecast, so there were a total of 4 forecasts.
Pepsi was having to compare and contrast the different forecasts. This took a large amount of resources.
It was unclear which forecast was the best.
Pepsi and Safeway worked together on identifying the most accurate forecast. Discussions occurred around how each forecast was generated.
Safeway established a forecast center of excellence with an objective of defining the most accurate forecast and sending that ONE to Pepsi (and other vendors.)
Continued collaboration led to a forecast that was ~50% more accurate. S

15. We Have Data But Need Analytics & Visualizations
Companies That Excel In Advanced Analytics Also Excel In Financial Performance With Profit Margins In The Range Of 19 To 73% Higher Than Those Of Other Companies
Shortage Of Analytical Talent
Data To Enable Decision Making
The Data Tsunami
Analytical IQ For Competitive Differentiation
Personalization And Hyper Targeting
Increasing Customer Expectations
Increasing Employee Expectations
Availability Of Data S
Source: Jim Duffy and Scott Rosenberger, The Future of Consumer Products Companies: Technology – Gaining an Advantage with Advanced Analytics, 2007

16. 70% Of Our Sensing Receptors Are Dedicated To Vision
Why Visualization?
Market Forces Highlight The Growth Of Data, A Need For Talent, Changing Expectations & Improving Decision Making
70% Of Our Sensing Receptors Are Dedicated To Vision
fonts, weights, sizes and colors
Certain Visuals Are More Impactful Than Others Such As Relative Position, Groupings, Shading, Etc.
Feedback from Pepsi led Safeway to revisit its forecasting processes and create a forecasting center of excellence.
Learned from Pepsi that Safeway was sharing 3 different forecasts, from several different functional groups, plus Pepsi had their own forecast, so there were a total of 4 forecasts.
Pepsi was having to compare and contrast the different forecasts. This took a large amount of resources.
It was unclear which forecast was the best.
Pepsi and Safeway worked together on identifying the most accurate forecast. Discussions occurred around how each forecast was generated.
Safeway established a forecast center of excellence with an objective of defining the most accurate forecast and sending that ONE to Pepsi (and other vendors.)
Continued collaboration led to a forecast that was ~50% more accurate. S

17. It’s All About The User Experience
We Need To Move From Rows And Columns To Something More Natural And Impactful
Yesterday
Today
Just as consumers are being preconditioned to learn visually
These consumers are employees and need to be trained the same way
Feedback from Pepsi led Safeway to revisit its forecasting processes and create a forecasting center of excellence.
Learned from Pepsi that Safeway was sharing 3 different forecasts, from several different functional groups, plus Pepsi had their own forecast, so there were a total of 4 forecasts.
Pepsi was having to compare and contrast the different forecasts. This took a large amount of resources.
It was unclear which forecast was the best.
Pepsi and Safeway worked together on identifying the most accurate forecast. Discussions occurred around how each forecast was generated.
Safeway established a forecast center of excellence with an objective of defining the most accurate forecast and sending that ONE to Pepsi (and other vendors.)
Continued collaboration led to a forecast that was ~50% more accurate.
fonts, weights, sizes and colors S

18. Companies Are Investing Significantly In Visualization Capabilities
Deloitte Has Made Significant Investment In Our Visualization Capabilities Because We See Visualization As A Critical Step To Understanding Data And Developing Deeper Insights
And Other Leading Consumer Products Companies Such As P&G Are Also Making Similar Bets With “Business Spheres” (~50 Locations). S

19. Familiar Visualization Examples
Source: SourceMap (
Source: Lumino.so ( S

20. PepsiCo, Safeway & Deloitte Visualization Design Session
On August 28th, 2012 PepsiCo And Safeway Came To The HIVE (Deloitte’s Highly Immersive Visual Environment) To Design And Rapidly Prototype New Ways To Visualize Key Challenges S

21. Reduce Out Of Stocks & Improve Days Of Supply Challenges And Questions To Address
What Locations Are Causing The Most Significant Challenges & What Are Those Challenges?
What Causal Variables Are Impacting My Days Of Supply & Out Of Stock Performance?
What Brands Are Most Impacted By Out Of Stock & Days Of Supply Performance?
What Are My Total Lost Dollar Sales For A Particular Set Of Events? P

22. Reduce Out Of Stocks & Improve Days Of Supply
Streamgraph
Force-directed graphs
Tree Maps
Sunburst
Word Tag Cloud
Bubble Chart
Many Eye Bubble Chart
Time Series Analysis
Geo Spatial
Parallel chord
Calendar View
Heat Maps
1. Consider A Technique To Visualize The Data
Often Used For Highly Dimensionalized Data Sets P

23. Reduce Out Of Stocks & Improve Days Of Supply
Rapid Prototype
2. Identify Casual Variables That Impact Out Of Stocks And Days Of Supply
3. Showcases Trends Between Those Factors P

24. Store does not meet in-stock target Item does not meet target
Opportunity
Reason Code
Analysis
Action
Store does not meet in-stock target
What stores are key drivers?
Item does not meet target NE SA WA
Warehouse Adjustment
What items are key drivers?
Not Enough
Sold After NO SI
Stocking Issue WS
Warehouse Short
Not Ordered
Review store orders, forecast assumptions, POS
Review display inventory
Validate available inventory
Force-out Product
Adjust Store Inventory
Review Store Ops
Ensure VMI Reorders
Determine Recovery P

25. Reduce Out Of Stocks & Improve Days Of Supply
How This Visualization Works
Cause
Effect
Day of Week
OOS Percentage
Brand
Lost Dollars
Item Velocity
DOS
OOS Reason
Sun
Mon
Wed
Brand N
Brand 1
Brand 2
High
Low
Med
High
Low
Med
Stocking Issues
Not Ordered
WH Short
High
Low
Med
High
Low
Med
Whereas Here There Is No Obvious Trend…maybe You Have To Dig Deeper
Groupings Show Trends, Even If Just One Color P

26. Reduce Out Of Stocks & Improve Days Of Supply
Diving Down Into A Specific Product
Root Cause Identification: What are the key reason codes for out-of-stocks in select districts?
Corrective Action: If root causes can be identified, can corrective actions be put in place to reduce or remedy the out-of-stocks?
District 1
District 2
District 3
District 4
District 5
District 6
District 7
District 8
Please select the DISTRICT:
We are going to begin by filtering our dataset. We can hide in stock data
Product 1
Product 2
Product 3
Product 4
Product 5
Product 6
Product 7
Product 8
Product 9
Product 10
Product 11
Product 12
Product 13
Please select the TRADEMARK:
3.39% OOS rate. Let’s dig in further
Select Dataset
to Visualize
XX District: District 6
YY Trademark: Product 6 P

27. Reduce Out Of Stocks & Improve Days Of Supply
Change Thread Coloring To Develop Insights
So far we:
Filtered to show only OOS products
Changed color to reflect OOS Reason Code
More “stocking issues” than expected…if we highlight these threads we can see them more clearly P

28. Reduce Out Of Stocks & Improve Days Of Supply
Following A Thread To Develop An Insight
Interesting, almost all of the stocking issues are from store Need to get a macro view across all products
Highlighted stocking issues P

29. Reduce Out Of Stocks & Improve Days Of Supply
Zoom Out And See Bigger Picture XX
District 6 YY
Product 1, Product 2
Product 3, Product 4, Product 5, Product 6,
Product 7, Product 8, Product 9, Product 10
Let’s back up and look at the big picture:
All products
All OOS
Entire district
Let’s dig into store 2272
Over $4000 in lost sales for this district over this period P

30. Reduce Out Of Stocks & Improve Days Of Supply
One Location Is Causing A Significant Number Of Lost Sales XX
District 6 YY
Product 1, Product 2
Product 3, Product 4, Product 5, Product 6,
Product 7, Product 8, Product 9, Product 10
Highlighted threads related to store #2272
About 1/5 of the lost sales dollars for this region come from one store! That’s 3X higher than the average P

31. Reduce Out Of Stocks & Improve Days Of Supply
Develop Actionable Insight XX
District 6 YY
Product 1, Product 2
Product 3, Product 4, Product 5, Product 6,
Product 7, Product 8, Product 9, Product 10
Removed all other stores to focus analysis on store #2272
1/4 of the issues for this store occur on Saturday. We are exploring solutions with PepsiCo for alternate delivery or incremental storage
Highlighted threads related to store #2272 P

32. What We Learned: Collaboration Is Essential
Retailers / Supplies Share The Shelf
The Magic Comes From Sharing
Data: Must Be Free & Open
Insights: Joint Interest In Analysis
Actions: Aligned On Plans / KPIs
Data Visibility & 360 Retail Execution Building “Big Data” Muscle
More Data Streams Are Coming With Digital Couponing, Etc.
Data Has Value Through Collaboration
Retailers and Suppliers share common goals at the shelf – keeping shoppers satisfied with great products
The magic is not just in sharing data – but in sharing actions and insights around the data
PepsiCo and Safeway already collaborating, and have been for some time
Both Safeway and PepsiCo are making huge strides in their organization to create processes around Data sharing
Believe in keeping data sources free and open S

33. Safeway & PepsiCo Will Build On Successes Using “Big Data” & Visualization Techniques
Supply Chain Remains An Opportunity For Improved Productivity Within CPG
Data Sharing Provides A Foundation For Retailer/Supplier Collaboration
New Data Visualization Techniques Will Make Use Of Data More Intuitive
CPG Industry Needs To Develop Analytical Competencies in Their Supply Chains
Next Steps
Expanding collaboration efforts to include data visualization
Update the scorecards, measures, processes, and structures
Expand use of program and leverage data visibility to drive additional value P

34. Any Questions? P

35. P