Zara Uses Operations Research to Reengineer Its Global Distribution Process

Transcription

1 Zara Uses Operations Research to Reengineer Its Global Distribution Process Miguel Díaz, CFO Javier García José Manuel Corredoira Marcos Montes Zara, Inditex Group S.A. José Antonio Ramos Juan Correa formerly with Zara Felipe Caro UCLA Anderson School of Management Jérémie Gallien MIT Sloan School of Management

2 Zara s Continuous Business Cycle Distribution Stores Customer feedback and fashion trends Global Warehouses Design Team Production and delivery Suppliers Production planning and specifications

3 Legacy Distribution Process (2006 and Before) store inventory assortment decisions past sales data store managers inventory in stores, past sales requested shipment quantities for each article and size warehouse inventory warehouse distribution team shipments

4 Problems with Legacy Process Store incentives and local optimization Warehouse resources and guidelines last week sales order store stock level

5 New OR-Based Distribution Process assortment decisions assortment decisions store inventory past sales data past sales data store managers forecasting model inventory in stores, past sales requested shipment warehouse inventory demand warehouse quantities for each inventory in stores forecasts inventory article and size warehouse distribution team optimization model shipments shipments Legacy Process New Process

6 The Optimization Model For every article: Expected revenue for upcoming week Value of leftover inventory Max Σ store j Price(j) x Sales(j) + Input_Value x Σ size s Leftover_WH_Inventory(s) Subject to: Shipment(j,s) is integer Σ store j Shipment(j,s) Existing_WH_Inventory(s) Sales(j) = Function[ Store_Inventory(j,s) + Shipment(j,s), Forecast(j,s) ] Inventory availability constraint Leftover_WH_Inventory(s) = Existing_WH_Inventory(s) - Σ store j Shipment(j,s) Inventory-to-sales function Leftover inventory calculation

7 Inventory-To-Sales Function Expected store sales for article that week Saturation Effect Expected demand (Forecast) Exposure Effect Stock of article in store at beginning of week

8 Exposure Effect and Size Display Policy Article with 4 Sizes (Small, Medium, Large, Extra Large) remaining sizes action S M L XL Keep on display M L XL Keep on display S M L Keep on display M L Keep on display S M XL Move to backroom S L XL Move to backroom S M Move to backroom L XL Move to backroom S L Move to backroom M XL Move to backroom S XL Move to backroom S Move to backroom M Move to backroom L Move to backroom XL Move to backroom Entire article is removed to backroom when major size Medium or Large stocks out

9 Stochastic Store Display Model Customer Arrival Process Inventory Sizes Poisson process arrival rates obtained from demand forecasts λ SM λ M SM M λ L L Major sizes (set A) λ XL XL Regular sizes Starting inventory x s of each size s: x s = Store_Inventory(s) + Shipment(s) Stochastic stopping times: The time when a size stocks out: The time when the first major size stocks out: Linear approximation of expected sales function: = Function[ Store_Inventory(s) + Shipment(s), Forecast(s) ]

10 Mathematical Model Formulation Expected revenue for upcoming week Value of leftover inventory Max Subject to: Approximate inventory-to-sales function Inventory availability constraint

11 Pilot Test: Objectives When: 2006 Fall/Winter season Objectives: 1. Prove concept feasibility through actual implementation 2. Refine software interface and model features based on feedback from field users 3. Estimate the model s specific impact on inventory distribution

12 Pilot Test: Matching Procedure 10 test articles that represent Zara s assortment test article control article Each test article was matched with a control article Matching criteria: same type of article same life-cycle similar prior performance

13 Pilot Test: Experiment Design test article control article Arteixo stores Performance comparison Zaragoza stores test article Optimization model computes shipments Shipments determined with legacy process Arteixo warehouse Zaragoza warehouse Shipments determined with legacy process Performance comparison control article

14 Pilot Test: Arteixo Warehouse legacy process for test and control articles PILOT: new process for test article legacy process for control article Arteixo test article M Test M Control MTest M Control control article Life-cycle Start Measuring Point Pilot Starts Life-cycle End

15 Pilot Test: Zaragoza Warehouse legacy process for test and control articles legacy process for test and control articles Zaragoza test article MTest M Control M Test M Control control article Life-cycle Start Measuring Point Life-cycle End

16 Pilot Test: Impact Estimation Methodology 1. Remove anything that happened prior to the pilot MTest M Test M Control M Control for test articles for control articles 2. Eliminate factors that are common to test and control articles (1 st dimension of control) Arteixo: impact of the new process Zaragoza: measurement of impact estimation error (2 nd dimension of control)

17 Pilot Test: Impact Estimation Methodology 1. Remove anything that happened prior to the pilot MTest M Test M Control M Control for test articles for control articles 2. Eliminate factors that are common to test and control articles (1 st dimension of control) ( MTest MTest) ( MControl MControl ) Arteixo: impact of the new process Zaragoza: measurement of impact estimation error (2 nd dimension of control)

18 Pilot Test: Increase in Sales Arteixo Zaragoza Sales Difference -10,0% -7,5% -5,0% -2,5% 0,0% 2,5% 5,0% 7,5% 10,0%

19 Pilot Test: Increase in Sales Arteixo Zaragoza Sales Difference -10,0% -7,5% -5,0% -2,5% 0,0% 2,5% 5,0% 7,5% 10,0%

20 Pilot Test: Increase in Sales Arteixo Zaragoza 0.7 % Average Error Sales Difference -10,0% -7,5% -5,0% -2,5% 0,0% 2,5% 5,0% 7,5% 10,0%

21 Pilot Test: Increase in Sales Arteixo Zaragoza 4.1 % Average Increase 0.7 % Average Error Sales Difference -10,0% -7,5% -5,0% -2,5% 0,0% 2,5% 5,0% 7,5% 10,0%

22 Pilot Test: Increase in Sales = 3 to 4% Increase in Sales Arteixo Zaragoza 4.1 % Average Increase 0.7 % Average Error Sales Difference -10,0% -7,5% -5,0% -2,5% 0,0% 2,5% 5,0% 7,5% 10,0%

23 Realized Financial Impact 3 to 4% Increase in Sales

24 Realized Financial Impact 3 to 4% Increase in Sales Additional Revenue Additional Net Income 2007 $233 M $28 M

25 Realized Financial Impact 3 to 4% Increase in Sales Additional Revenue Additional Net Income 2007 $233 M $28 M 2008 $353 M $42 M

26 Operational Impact Display Cover Ratio days ondisplay per days stores store +3.5% Transshipments and Returns Ratio units transshippedorreturned unitsshipped -19%

27 Conclusion Technical OR achievements: First application of OR to fast fashion Tight MIP formulation of complex stochastic problem Distributed IT implementation with many real-time users Large controlled field experiment to estimate impact Business impact: Substantial measurable increase in sales by 3-4% $350M Shelf exposure time increased by 3.5% Transshipments and returns reduced by 19% Scalable process without major capital investment Human impact: Positive transformation of sixty employees daily lives