Case Study Summary of Zara and Oxford Industries
Summary of Zara
Zara specializes in inexpensive fashions for women and men between the ages of 16 and 35. In keeping with the spirit of that demographic, Zara moves quickly. Like many apparel retailers, it has two seasons—fall/winter and spring/summer—but selections change frequently within those periods. Items spend no more than two weeks on the shelf before making way for new merchandise, and stores are replenished twice a week.
With annual growth of around 20 percent in both sales and number of stores, Zara was finding that strategy increasingly difficult to execute. Part of the Inditex group of fashion distributors, it currently has more than 1,100 stores in 68 countries. With so much volume flowing through the supply chain, the company could no longer rely on guesswork by store managers as to how much product it needed to replenish at each location.
In the summer of 2005, Zara heard about research being done on mathematical models for retailing, by professors Jeremie Gallien of the MIT Sloan School of Management and Felipe Caro of the UCLA Anderson School of Management. They were invited to Zara’s headquarters in La Coruna, Spain.
The focus was on making better stock-allocation decisions for Zara’s growing network of stores. A prototype of the resulting model was implemented between March and July of the following year, as part of a six-month internship at Zara by MIT graduate student Juan Correa. Between August and December, researchers ran a live pilot involving distribution of a dozen products to Zara’s stores worldwide. An identical selection of products was dispatched to stores under the old process, for purposes of comparison.
The mathematical model drew on historical sales data plus available stock in the warehouses to come up with a final number for each store. Gallien says the task was exceedingly complex. Each store carries several thousand items in up to eight sizes, with exact quantities to be determined for twice-weekly shipments.
Zara specializes in inexpensive fashions for women and men between the ages of 16 and 35. In keeping with the spirit of that demographic, Zara moves quickly. Like many apparel retailers, it has two seasons—fall/winter and spring/summer—but selections change frequently within those periods. Items spend no more than two weeks on the shelf before making way for new merchandise, and stores are replenished twice a week.
With annual growth of around 20 percent in both sales and number of stores, Zara was finding that strategy increasingly difficult to execute. Part of the Inditex group of fashion distributors, it currently has more than 1,100 stores in 68 countries. With so much volume flowing through the supply chain, the company could no longer rely on guesswork by store managers as to how much product it needed to replenish at each location.
In the summer of 2005, Zara heard about research being done on mathematical models for retailing, by professors Jeremie Gallien of the MIT Sloan School of Management and Felipe Caro of the UCLA Anderson School of Management. They were invited to Zara’s headquarters in La Coruna, Spain.
The focus was on making better stock-allocation decisions for Zara’s growing network of stores. A prototype of the resulting model was implemented between March and July of the following year, as part of a six-month internship at Zara by MIT graduate student Juan Correa. Between August and December, researchers ran a live pilot involving distribution of a dozen products to Zara’s stores worldwide. An identical selection of products was dispatched to stores under the old process, for purposes of comparison.
The mathematical model drew on historical sales data plus available stock in the warehouses to come up with a final number for each store. Gallien says the task was exceedingly complex. Each store carries several thousand items in up to eight sizes, with exact quantities to be determined for twice-weekly shipments.