I N S I G H T S I N T O I N D I G O S J O U R N E Y & K E Y S A P F & R C A PA B I L I T I E S J U LY 6,

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1 I N S I G H T S I N T O I N D I G O S J O U R N E Y & K E Y S A P F & R C A PA B I L I T I E S J U LY 6,

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4 $850+ M Revenue Last Fiscal Year Print General Merchandise Toys Trade Books Bargain Books Magazines Calendars Home accessories Fashion accessories Stationary Electronics Gourmet Baby Toys Games American Girl 4

5 9 Quarters of Revenue Growth 5

6 Supply Chain Overview Online DC Vendors Stores 190K Products 1.5K Book Vendors 2K GM & Toy Vendors 130 Product Development Vendors (Supported by NY Design studio) Retail DC 200+ Stores across Canada Forecasting 5.5M Product Locations 44% of all receipts is System Replen 64% of all receipts sourced from DC 50+% of GM receipts is seasonal 30K Products 6

7 2 Why F&R?

8 Prior to SAP F&R System Landscape ERP implemented in 2004 ERP used for forecasting and replenishment SAP Warehouse Management System implemented in 2012 Many custom developments ( ) Latest upgrade to ECC6 in 2013 Stable system Reliable and mature Master Data Challenges Manual preview of system replenishment order Manual analysis and re-orders into the DC Manual adjustment of forecasts Manual adjustment to account for out-of-stocks Manual adjustment of F&R parameters to align with demand changes Manual review of exceptions 8

9 Supply Chain Vision Online DC Integrate Online Demand to drive vendor orders STOP REACTING TO YESTERDAY START PLANNING FOR THE FUTURE Vendors Collaborate and share order forecasts with vendors to improve fill rate Retail DC Initial Buy less and allow more replenishment pull into the Retail DC from vendor to support store order forecasts Stores Allocate less rely more on pull More accurate replen orders Influence forecast for events 9

10 Store Forecasting Challenge Inability to generate accurate forecasts for slow sellers F&R Capability Advanced forecasting algorithms suitable for slow and fast sellers 10

11 Store Forecasting Challenge Category level seasonality not representative for all items within the category F&R Capability Advanced forecast algorithms support Product Location Level Seasonality 11

12 Store Forecasting Challenge Unable to maintain in-stocks for volatile fast sellers F&R Capability Advanced forecast algorithms support dynamic replenishment safety stock for volatile fast sellers 6 5 High Volatility 6 5 Low Volatility Sales Average Sales Average

13 Store Forecasting Challenge Manual effort to catch outliers and correct forecast out of stock items F&R Capability Automatic outlier peak and zero stock correction Outlier Correction Zero Stock Correction

14 Store Forecasting Challenge No systematic way to model the forecast for new item F&R Capability Leveraging reference items to model forecast and order forecast for new items Reference Item 8 New Item F&R 1x sales Journey 2 history

15 Store Forecasting Challenge No systematic solution to build-up stock earlier at the stores F&R Capability Pre-allocation feature within F&R enables shifting forecast to build stock earlier at the stores Pre-allocation Forecast shift for replenishment 15

16 Store Forecasting Challenge Over-forecasting post feature and unable to influence the forecast for the upcoming feature F&R Capability Demand shaping ahead of the feature and capability to ignore feature sales post promotion Future Promo Sales Today Forecast 16

17 Store Forecasting Challenge Over-forecasting post feature and unable to influence the forecast for the upcoming feature F&R Capability Demand shaping ahead of the feature, ability to replicate last promotional lift and capability to ignore feature sales post promotion Re-occuring Promo Sales Today Forecast 17

18 Store Forecasting Challenge Inability to influence the forecast due to non-representative sales history causing over or under forecast F&R Capability Ability to ignore unrepresentative sales history in order to drive better forecast Store Renovation Sales Today Forecast 18

19 Store Replenishment Challenge Unable to use different display quantities for different periods F&R Capability Ability to use time phased display quantities Double Exposure during Easter 19

20 DC Shipment Forecast Challenge Unable to generate an accurate shipment forecast for the DC F&R Capability Multi echelon feature enables accurate shipment forecast for the DC based on store and online sales forecast Store Sales Forecast Store Sales Forecast Online Sales Forecast Store Order Forecast Store Order Forecast Online Order Forecast Aggregated Store and Online Order Forecast 20

21 Vendor Order Forecast Challenge Unable to generate an accurate vendor order forecast for DC sourced items F&R Capability Multi echelon feature enables accurate vendor order forecast for DC sourced items Store Sales Forecast Store Sales Forecast Online Sales Forecast Store Order Forecast Store Order Forecast Online Order Forecast Aggregated Store and Online Order Forecast Vendor Order Forecast 21

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23 Project Timeline & Approach Aug Sep Oct Nov Dec Jan Feb Mar April May Jun July 3 Month Planning Phase 4 Month Realization Phase 2 Month Pilot Phase 3 Month Rollout Process Continuous communication across the organization System People Business Led & Focus on standard capabilities Pilot & Performance Tuning Phased Rollout & Training 23

24 Project Phases in Detail Planning Realization Pilot Rollout Sandbox Environment Prep with UCS Training with UCS prior to Blueprinting Business focused Blueprinting F&R Config and technical design by UCS ECC6 Config and developments Integration Testing Why SAP Data Clean-up F&R? Performance Tuning DIF Assessment and technical support by SAP Product Locations in F&R Production Environment Prep Pilot Team training Parallel Testing (soft go-live) Performance Tuning Pilot Hard Go-live Monitor Integration & daily orders Parameter fine tuning Job scheduling April May June July Phased Training Approach Phased Conversion Continuous Performance Tuning Fine tuning of job schedules Standing meetings within and post rollout week 24

25 Key Lessons Learned Experienced integrator Training led by UCS Solutions before blueprinting Business led blueprinting Early engagement of Basis Minimize Customization Broad & Experienced Pilot Team Large scale parallel run Phased conversion & rollout Extensive training & support Performance tuning 25

26 Impact on Day in Life Inventory Analyst BOOK ONLY Roles using F&R Allocation Analyst BOOK ONLY Less time spent on manual adjustments Less time spent on order review Less time spent on pushes Assistant Planning Manager GM & TOYS More time spent on planning & influencing forecast for future events Vendor order forecasts now supplied by F&R vs. manual effort Focus shifted towards value added work 26

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