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 There Is A Lot Of Data For Collaboration S Safeway Data Sharing Programs Data Sharing Programs In Place With CPG Vendors In Marketing & Supply Chain Supply Chain Data Sharing Shopper Insight / Loyalty Data Sharing Share POS And Inventory Data Marketing Data At Household And Segment Level Aimed At Reducing OOS And Inventory, Increasing Sales Decisions On Assortment, Pricing, And Promotion Used By Customer Supply Chain Teams Customer Marketing Teams Typically Cost Nothing To Participate A Fee To Participate S 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 S Data Visibility Core Competencies Innovative Collaborative Comprehensive Strategic • Relatively New Program Even Though Data Sharing Is Not A New Concept • Opportunity For Increased Collaboration Between Supplier & Retailer • Real Time Visibility Can Influence Many Areas Including Assortment, Inventory, Distribution & Promotion • Vendors To Take The Lead In Driving Insights Through Leveraged Data Sharing • Program Enhanced Through Vendor Feedback S PepsiCo & Safeway Are Collaborating Further P P 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 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 P Demand Signal Repository (DSR) Overview Retailer Shares POS Data Account Team Shared Scorecards DSR Cleanses & Stores Data Improved Shopper Experience Dashboards BI Tools Advanced Analytics OOS Phantom Inventory NPI In-Store Execution Promo Execution Supply Chain P 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 Improved Forecasting Approaches Are Resulting From The Safeway / PepsiCo Partnership P 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 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 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 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 Data To Enable Decision Making Increasing Employee Expectations Shortage Of Analytical Talent Analytical IQ For Competitive Differentiation Increasing Customer Expectations Personalization And Hyper Targeting The Data Tsunami Availability Of Data Source: Jim Duffy and Scott Rosenberger, The Future of Consumer Products Companies: Technology – Gaining an Advantage with Advanced Analytics, 2007 S 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 Certain Visuals Are More Impactful Than Others Such As Relative Position, Groupings, Shading, Etc. fonts, weights, sizes and colors S It’s All About The User Experience We Need To Move From Rows And Columns To Something More Natural And Impactful Yesterday Today These Just asconsumers consumersare are employees being preconditioned and need to be totrained learn visually the same way fonts, weights, sizes and colors S Companies Are Investing Significantly In Visualization Capabilities Deloitte Has Made Significant Investment In Our Visualization Because We See And Capabilities Other Leading Consumer Companies Such As P&G Data VisualizationProducts As A Critical Step To Understanding AreDeveloping Also Making Similar Bets With And Deeper Insights “Business Spheres” (~50 Locations). S Familiar Visualization Examples Source: SourceMap (http://sourcemap.com) Source: Lumino.so (http://www.luminoso.com/) S 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 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 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 Parallel chord Calendar View Heat Maps 1. Consider A Technique To Visualize The Data Geo Spatial Often Used For Highly Dimensionalized Data Sets P 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 Opportunity What stores aremeet key drivers? Store does not in-stock target Reason Code NE Not Enough NO Not Ordered SA Sold After SI Item doesWarehouse not meet target What items key drivers? WA are Adjustment Stocking Issue WS Warehouse Short Analysis Review store orders, forecast assumptions, POS Review display inventory Validate available inventory Action Force-out Product Adjust Store Inventory Review Store Ops Ensure VMI Reorders Determine Recovery P Reduce Out Of Stocks & Improve Days Of Supply How This Visualization Works Effect Cause Sun Brand N Day of Week Brand Item Velocity DOS OOS Reason High OOS Percentage Lost Dollars Wed Brand 2 Med Med WH Short Med Med Mon Brand 1 Low Low Not Ordered Low Low High Stocking Issues High High Whereas Here There Is No Obvious Trend…maybe You Have To Dig Deeper Groupings Show Trends, Even If Just One Color P 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? Please select the DISTRICT: District 1 District 2 District 3 District 4 District 5 District 6 District 7 District 8 We are going to begin by filtering our dataset. We can hide in stock data Please select the TRADEMARK: 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 3.39% OOS rate. Let’s dig in further XX District: District 6 YY Trademark: Product 6 P Reduce Out Of Stocks & Improve Days Of Supply Change Thread Coloring To Develop Insights 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 P 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 2272. Need to get a macro view across all products Highlighted stocking issues P Reduce Out Of Stocks & Improve Days Of Supply Zoom Out And See Bigger Picture 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 XX this district over this period YY District 6 Product 1, Product 2 Product 3, Product 4, Product 5, Product 6, Product 7, Product 8, Product 9, Product 10 P Reduce Out Of Stocks & Improve Days Of Supply One Location Is Causing A Significant Number Of Lost Sales 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 XX District 6 Product 1, Product 2 YY Product 3, Product 4, Product 5, Product 6, Product 7, Product 8, Product 9, Product 10 P Reduce Out Of Stocks & Improve Days Of Supply Develop Actionable Insight Removed all other stores to focus analysis on store #2272 Highlighted threads related to 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 XX District 6 Product 1, Product 2 YY Product 3, Product 4, Product 5, Product 6, Product 7, Product 8, Product 9, Product 10 P 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 S 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 Any Questions? P P