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One Size Does Not Fit All Using Size Analytics to Drive Sales Gale Weisenfeld VP, Planning / MIO Federated Department Stores April 28, 2006.

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Presentation on theme: "One Size Does Not Fit All Using Size Analytics to Drive Sales Gale Weisenfeld VP, Planning / MIO Federated Department Stores April 28, 2006."— Presentation transcript:

1 One Size Does Not Fit All Using Size Analytics to Drive Sales Gale Weisenfeld VP, Planning / MIO Federated Department Stores April 28, 2006

2 Agenda Introduction –Company Overview –Business Challenge Business Solution –Role of Technology –Implementation –Critical Success Factors Collaboration –Change in the Supply Chain –CPFR The Numbers Tell The Story –Business Example –Lessons Learned Future Plans

3 About Federated Department Stores One of the nations leading department store retailers Acquisition of May Company will create 800+ store operation with projected retail sales in excess of $27 billion in 48 states, Guam, & Puerto Rico Operates under the names of Macys, Bloomingdales, Macys.com, Bloomingdales By Mail Support operation of Macys Merchandising Group is responsible for all aspects of Federated Private Label & management of FDS merchandising systems

4 Business Challenge Accelerate Federated Department Stores comp-store sales growth and increase the companys profitability levels

5 Why Customers Dont Buy Have you ever gone shopping and found the perfect blouse / sweater / pants / shoes only to be told sorry, we dont have your size This is a missed sales opportunity of the worst kind when the customer is in our stores, wants to make a purchase, and cant because we dont have her size While alternate product offerings at the style or color level might be substituted to complete a sale, this is seldom the case for size stockouts

6 Business Solution Federateds Size Selling Initiative was launched in 2002 to develop and implement solutions that resolve the issue of size stockouts Drive regular price sales Reduce markdowns Improve margins Create a better shopping experience

7 Using Technology to Analyze Sales Phase 1: Best Single Prepack Size Profiling Application (SPA) developed by Macys Merchandising Group & Federated Systems Group Adjust Fashion Prepacks to better match customer demand Track rate of sale on items to the size level

8 Size Measurement Calculations Improve match rate between orders & sales Analyze % of sales by size Analyze % of stock by size Objective: Determine how did we sell the product when it first came in to our stores BEFORE we began to run out of sizes (Fresh Sales)

9 Calculating Match Rates Variance = Stock % - Sales % Match Rate = 100% - Absolute Value of the Variance Example: 100% - 20% = 80% Match Rate Size Sales % Stock % Variance % S 12% 17% 5% M 28% 33% 5% L 38% 33% -5% XL 22% 17% -5% Total 100% 20%

10 Applying Match Rates to Fashion Prepacks Fashion Prepack quantity = 12 units SizeOriginal Prepack New Prepack S21 M43 L45 XL23 Match Rate80%86%

11 Using Technology to Analyze Sales Phase 2: Multi-Prepack Create location level clusters based on historical sales patterns Every store doesnt need the same 12 pack but how many prepacks is the right number –Every store isnt an equal contributor to sales –Consider sales contribution by door cluster, volume of program, & overall cost to supply chain when determining optimum number of prepacks

12 How many ways can you sort a 12 pack? Prepack A Prepack B Prepack C Prepack D Prepack E S21230 M43353 L45535 XL23214

13 Why 12? Who was it that said 12 was the right number anyway? –12 is an even number –As children, we learn to count by twos –12 hangars fits on a straight arm of a 4-way –The box holds 12 pieces Sometimes a slightly smaller (10 or 11) or slightly larger (13 or 14) prepack quantity can yield a significantly better match rate

14 Using Technology to Analyze Sales Phase 3: ROI – Return on Investment Reporting Questions we were asking ourselves –Did we make the right changes? –Do we have additional opportunities? –Are we making a difference? Evaluate sales performance against new prepacks Determine if further prepack refinement is necessary

15 Using Technology to Analyze Sales ROI Reporting includes WAS / IS comparison to gauge ongoing impact of pack changes to key business metrics: –Regular Price Sales & Sell Thru by Size –Average Unit Retail by Size –Stockout at X % by Size –Markdowns by Size By Location results are also available

16 System Development & Implementation Phase 1: Best Single Prepack (S02) Phase 2: Multi Prepack w/Location Clustering (S 03) Phase 3: ROI Reporting (S04) Application currently deployed to approximately 100 users across all Federated divisions

17 Critical Success Factors Incorporation of size analysis into the daily business process –Senior Sponsor (President, MMG) –Size analysis is not a special one-time project –Timing, coordination with market schedule –Increased workload before market meetings –Change Management culture change with significant impact to business process –Clear definition of roles / responsibilities

18 Critical Success Factors Collaboration –After gaining experience with Private Label and having wins to talk about, Federated is actively analyzing size selling for branded vendors and sharing our findings with our vendors –Buying and allocating the right sizes is only the beginning of the process A genuine partnership between retailer and supplier is critical in order to maximize sales by size

19 Collaboration Introducing Change to the Supply Chain –Vendors need to evaluate their ability to implement change in the supply chain to support multi-prepack offerings –Resistance & Obstacles to Change This is hard This is expensive Im not the right person to make this happen Can my company execute this accurately?

20 Collaboration Achieving Change in the Supply Chain –Vendors are migrating to a multi-prepack business model –Vendors offering multi-prepacks have made modifications to reflect different / additional packs –If there is only a national pack (best single prepack), FDS is defining the pack Mutual Benefits –Our vendors will share in the benefits of the work being done with size analytics via increased regular price sales and improved margins

21 CPFR Collaboration-Planning-Forecasting-Replenishment CPFR diagrams & slides are courtesy of Russ Brown, Russell Corporation Fall 2005 AAFA ISC Presentation

22 CPFR Collaboration-Planning-Forecasting-Replenishment

23 CPFR involves more than you might think….

24 CPFR - - The cycle expanded

25 Where does CPFR fit in?

26 CPFR starts with a joint agreement

27 CPFR encompasses review and understanding of strategies & data on both sides of the partnership

28 The forecast starts it all. Is size analytics part of this process?

29 CPFR - - Manage the exceptions Identify and resolve exceptions to the forecasts –Orders –Receipts –Fill rates –Sales

30 What have we learned? Our work with size analytics is ongoing Customers sizes are ever-changing Size Specs matter –When is a large not a large? –Know your fit model Things to consider –Vendor –Lifestyle –Price Point –Personality of the product –Its not just about the numbers –Collaboration is key

31 Current Sizing Priorities Collaboration with our market vendors –Now more than ever, as Macys becomes a national department store, we need right product, right place, right time, AND right size –The lead time to implement change in the supply chain is long, frequently 9-12 months. We need to work together to implement change faster. Integration of size analytics into Federated merchandising systems in order to gain workload efficiencies –Ordering –Allocation

32 Discussion / Questions Gale Weisenfeld VP, Planning / MIO Federated Department Stores


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