Clearance Pricing & Inventory Management for Retail Chains

Transcription

1 Clearance Pricing & Inventory Management for Retail Chains Stephen A. Smith J. C. Penney Professor & Associate Director The Retail Workbench Santa Clara University Dale D. Achabal, Ph.D. L.J. Skaggs Professor & Director Retail Management Institute Santa Clara University

2 The Retail Workbench Founded at Santa Clara University in 1991 Mission: To improve decision making in general merchandise retailing by applying science to the art of retailing Corporate Sponsors: Retail Department and Specialty stores Faculty in Marketing, Operations and MIS from SCU and other universities

3 Drivers of the Retail Industry in the 21 st Century Consumers Demand for Greater Choice Better Information Systems Detailed market info (POS + Web) Desktop computing power

4 Merchandise Trends in Department and Specialty Stores More products in the assortment More fashion merchandise Shorter seasons More private label merchandise

5 Result: Clearance Markdowns(CMDs) increase as % of Sales 33% Markdowns as Percentage of Dollar Sales 31% 26% 21% 16% 11% 6% Based upon Merchandising and Operating Results of Department and Specialty Stores, National Retail Federation

6 Retail Supply Chain Features Long lead time & just 1 order for fashion and private label 75% - 100% of merchandise sent directly to stores for presentation Inter-store transfers not economical

7 Buyers Management of CMDs Viewed as mistakes Hope springs eternal Seasonal demand evaporates Must clear at any price But selling half at 50% off >$ selling all at 80% off

8 Additional Constraints for CMDs Clearance prices must be nonincreasing Inventory must be revalued to each new clearance price (Weekly) Markdown budgets by merchandise category Same markdown at all stores (for simplicity)

9 Analytical Approach to CMD Management Sales Forecasting Model Clearance Price Optimization at Store and Item Level Financial Performance Measurement

10 Sales Forecasting Factors Seasonal variations end of season drop Holidays & Store Events Percent Markdown Advertising Remaining On Hand Inventory Store Presentation Broken assortments

11 Forecasting Model for Weekly Item Sales Baseline Seasonal Mechandising Sales = x x Sales Effect Effects Based on Retail Workbench empirical studies Merchandising Effects tailored to each retailer

12 Example Merchandising Effects Model p = the current percent markdown A = feature advertising space in percentage of a page A 0 = smallest ad size (typically a line list, which is 10% of a page) I = current on hand inventory I 0 = base inventory level sometimes called fixture fill. d(k,t) = 0,1 indicators for store events γ, α, τ, µ(k) = elasticities estimated by regression = k t k d k p e I I A A e d I A p M ), ( ) ( 0 0 ),,, ( µ τ α γ

13 Forecasting Weekly Sales Historical Data Stage 1 Initial Estimation of Model Coefficients Stage 2 New Sales Data Weekly Forecasts & Adjustment of Certain Coefficients Update Coefficients for: Base Sales Price sensitivity

14 Forecasting Model Hierarchy Parameter Type Department or Class Seasonal Variations Items or SubClass Merchandising Effects, Base Sales Store or Metro Area Forecast Allocation Sizes & Colors Forecast Allocation

15 Clearance Price Optimization: Inputs Forecasted Sales for remainder of the season On Hand Inventory at each store Out-Date (End of Season) Unit Salvage Value of Unsold Merchandise

16 Decision Variables p(t) = markdown price in week t I 0 = initial inventory level (may be fixed) I(t) = remaining inventory in week t s(t) = s(p(t),y(i(t)),t) = sales in week t where y(i(t)) = inventory effect on sales

17 Optimal Control Problem: Maximize Gross Margin unit salvage value. and piecewise linear cost a ) ( where ) ( ) ( ) '( subject to ) ( ) ( ) ( ) ( max = = = = + e t t e e t t e c I c s dt t s I t s t I I c s I c dt t s t p e e

18 Solution Properties p p Optimal weekly sales trajectory is proportional to seasonal effects. Optimal price trajectory depends on I(t). P( I( t)) = e e, y e 1/ γ p e + at time 1 γ t e ln y( I( t), y e where come from boundary conditions. = c'( I 0 ) determines I 0. Step function approximation works well for optimal price trajectory.

19 Financial Performance Measures Revenue Capture Rate = Revenue Obtained during CMD Cycle Units at CMD Start Original Retail Price Inventory Sell Through = average % of inventory sold each week frees floor space for newer merchandise

20 Case Study Mid-Size Retailer with over 300 stores: Increased Profitability Capture rate increased by 10-15% > $15 Million per year revenue increase Faster Inventory Conversion = Fresh Assortment Inventory sell through increased by % Shortened Markdown Cycle by 20% Significant Labor Savings on re-pricing Better Markdown Dollar Forecasting - Forecast Error percent cut in half at chain level

21 Spotlight Solutions Results See for more information.

22 Recent Pilot Results Control Group Spotlight Stores Stores $GM / $Revenue 44% 48% % of Inventory Sold 62% 71% Length of Season 13 wks 11 wks

23 Optimal Markdowns are Targeted by Item and Location Higher sell-through and more gross margin can be achieved by matching supply with demand and setting prices optimally None 25% Off 33% Off 40% Off 50% Off 60% Off Typical 1st Markdown Decision Optimal Markdown

24 Additional Benefits of Optimal Markdown Strategy Floor Space Increases for New, Higher Mark Up Goods Higher Sell-Through at 1 st Price Reduction Lower Avg. Inventory Positions Reduced Inventory Carrying Costs Higher Turns Opportunity to Increase Sales Floor Space Utilization with New, Full-Price Merchandise Earlier Increased Customer Satisfaction with a Less-Cluttered Sales Floor

25 Profitability Impacts For a retailer with 33% of revenue from CMDs, typical $GM increase has been 4% of revenue. If yearly revenue = $1 Billion, this equals $40 Million per year!

26 Learning Faster Than The Competition is The Only Way to Gain a Sustainable Competitive Advantage