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Carl Graziani SVP Supply Chain, Safeway Inc. John Phillips

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Presentation on theme: "Carl Graziani SVP Supply Chain, Safeway Inc. John Phillips"— Presentation transcript:

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 (http://sourcemap.com) Source: Lumino.so (http://www.luminoso.com/) 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


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