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

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Presentation on theme: "1 PepsiCo & Safeway A “Big Data” Collaboration To Reduce Out-Of-Stocks Using Visualization Techniques Carl Graziani SVP Supply Chain, Safeway Inc. John."— Presentation transcript:

1 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 2 There Is A Lot Of Data For Collaboration S

3 3 Data Sharing Programs In Place With CPG Vendors In Marketing & Supply Chain SharePOS And Inventory DataMarketing Data At Household And Segment Level Aimed AtReducing OOS And Inventory,Decisions On Assortment, Increasing SalesPricing, And Promotion Supply Chain Data Sharing Shopper Insight / Loyalty Data Sharing Used ByCustomer Supply Chain TeamsCustomer Marketing Teams Typically CostNothing To ParticipateA Fee To Participate Safeway Data Sharing Programs S

4 4  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 20 Vendors Are Now Receiving Data From Safeway Safeway Data Visibility Program S

5 5 Data Visibility Core Competencies S

6 6 PepsiCo & Safeway Are Collaborating Further P

7 7 P

8 8  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 360° Retail Execution™ Delivers “Big Data” For Driving Performance P

9 9  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 PepsiCo Believes In The Power Of Data & Analytics To Drive Supply Chain P

10 10 Retailer Shares POS Data DSR Cleanses & Stores 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 ImprovedShopperExperience Demand Signal Repository (DSR) Overview P

11 11 Filling Distribution Voids Through Scripted Replenishments Field Teams Are Leveraging Gap Scans Increasing Forecast Accuracy & Driving Supply Chain Efficiencies Through True CPFR & VMI Improved Forecasting Approaches Are Resulting From The Safeway / PepsiCo Partnership Examples Of Driving Value Through Shared Data P

12 12 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 Filling Distribution Voids Through Scripted Replenishments P

13 13 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 Filling Distribution Voids Through Scripted Replenishments P

14 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 S

15 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 Source: Jim Duffy and Scott Rosenberger, The Future of Consumer Products Companies: Technology – Gaining an Advantage with Advanced Analytics, 2007 S

16 16 Why Visualization? 70% Of Our Sensing Receptors Are Dedicated To Vision Market Forces Highlight The Growth Of Data, A Need For Talent, Changing Expectations & Improving Decision Making fonts, weights, sizes and colors Certain Visuals Are More Impactful Than Others Such As Relative Position, Groupings, Shading, Etc. S

17 17 It’s All About The User Experience We Need To Move From Rows And Columns To Something More Natural And Impactful fonts, weights, sizes and colors YesterdayToday Just as consumers are being preconditioned to learn visually These consumers are employees and need to be trained the same way S

18 18 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). Companies Are Investing Significantly In Visualization Capabilities S

19 19 Source: Lumino.so (http://www.luminoso.com/) Source: SourceMap (http://sourcemap.com) Familiar Visualization Examples S

20 20 On August 28 th, 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 PepsiCo, Safeway & Deloitte Visualization Design Session S

21 21  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? Reduce Out Of Stocks & Improve Days Of Supply Challenges And Questions To Address P

22 22 StreamgraphForce-directed graphsTree MapsSunburst Word Tag CloudBubble ChartMany Eye Bubble ChartTime Series Analysis Geo SpatialParallel chordCalendar ViewHeat Maps 1. Consider A Technique To Visualize The Data Often Used For Highly Dimensionalized Data Sets Reduce Out Of Stocks & Improve Days Of Supply P

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

24 24 Store does not meet in-stock target What items are key drivers? NENE Not Enough Not Ordered Sold After Stocking Issue Warehouse Adjustment Warehouse Short NONO SASA SISI WSWS WAWA Review store orders, forecast assumptions, POS Validate available inventory Review display inventory Force-out Product Adjust Store Inventory Review Store Ops Ensure VMI Reorders Determine Recovery Item does not meet target What stores are key drivers? P

25 25 How This Visualization Works 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 Cause Effect Day of WeekOOS Percentage Brand Item Velocity DOS OOS Reason Lost Dollars Groupings Show Trends, Even If Just One Color Whereas Here There Is No Obvious Trend…maybe You Have To Dig Deeper Reduce Out Of Stocks & Improve Days Of Supply P

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

27 27 So far we: 1.Filtered to show only OOS products 2.Changed color to reflect OOS Reason Code So far we: 1.Filtered to show only OOS products 2.Changed color to reflect OOS Reason Code More “stocking issues” than expected…if we highlight these threads we can see them more clearly Change Thread Coloring To Develop Insights Reduce Out Of Stocks & Improve Days Of Supply P

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

29 29 Let’s back up and look at the big picture: All products All OOS Entire district 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 dig into store 2272 Over $4000 in lost sales for this district over this period Reduce Out Of Stocks & Improve Days Of Supply Zoom Out And See Bigger Picture P

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

31 31 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 Reduce Out Of Stocks & Improve Days Of Supply Develop Actionable Insight P

32 32  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 What We Learned: Collaboration Is Essential S

33 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 P

34 34 Any Questions? P

35 35 P


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