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. Through use of the model, computers could take over the basic number crunching, with humans left to make adjustments based on exceptions such as bad weather or unexpected disruptions in the sales channel.
The emphasis on fast turnaround motivates consumers to purchase items on the spot. Unlike in many clothing stores, where seasonal lines remain on the shelves for weeks or months, a particular style in a Zara store can disappear within a week. Zara speeds up its supply chain by strategically selecting and locating suppliers. A “proximity model” judges not only their geographic placement, but their ability to respond quickly to production orders. About half of the retailer’s production meets the proximity threshold, mostly coming from suppliers in Spain, Portugal and Morocco. From a geographic standpoint, nearly 65 percent of production is sourced in Europe. Zara also buys from suppliers in Asia, but because of the need for speed, their number is “considerably less” than the industry’s average.
The model has yielded additional benefits. Product now spends more time on the sales floor, and less in a back room or warehouse. With a reduction in misallocated inventory, there are fewer returns to the warehouse and transfers between stores. And, as Zara’s distribution network continues to grow, the retailer won’t need to expand its warehouse team as fast as the old process required.
Summary of Oxford Industries
Oxford Industries began in 1942 as a domestic manufacturer of basic, button-down shirts for mid-level retailers, particularly department stores. In recent years, however, the company has shifted its business model to focus on apparel design and marketing, with third-party producers handling manufacturing. As part of this transformation, the Atlanta-based company embraced a brand-focused business strategy. In 2003, Oxford acquired the island-inspired Tommy Bahama operations, followed by the 2004 acquisition of Ben Sherman—a well-known London-based brand made famous by the popularity of its shirts among British rock stars.
Oxford’s legacy business units, Lanier Clothes and Oxford Apparel, also evolved. As one of the leading suppliers of men’s tailored clothing to retailers, Lanier Clothes designs and markets suits, sports coats, suit separates and dress slacks. While continuing to sell these under private labels, it also has licensed a number of well-known brands, including Geoffrey Beene, Kenneth Cole and Dockers. These products span a wide price range and are sold at national chains, department stores, specialty stores and discount retailers throughout the United States. Oxford Apparel’s products range from dress shirts and western wear to suit separates and golf apparel, designed mostly for private-label customers like Lands’ End, Federated Department Stores and Men’s Wearhouse.
Oxford Industries also sells through 55 of its own stores. In the late 1980s, early in its transformation process and prior to the acquisition of Tommy Bahama and Ben Sherman, Oxford realized that it needed to bring its business divisions up to speed with more robust information technology. After completing the implementation of a company-wide enterprise resource planning system, the company contracted with an independent consulting firm to determine where it should invest time and money to further increase operational efficiencies and performance. The result of that in-depth study ultimately led to Oxford Industries’ decision to implement two solutions from JDA Software: Demand Planning and Master Planning.
With so many possible permutations of size, style and color for each of its products, improving forecast accuracy was critical. Prior to implementing JDA Demand, Oxford relied on its retail customers’ demand forecasts for its private-label products, as well as information provided by the company’s own sales associates. If too much or too little product was created based on the retailer’s or the sales associates’ forecast, both Oxford Industries and that customer paid the price via lost sales or markdowns.
JDA Demand enabled the company to better understand consumers’ evolving requirements and current trends, along with historical buying patterns, resulting in the ability to create more accurate forecasts and synchronize demand for replenished product with sources of supply. Oxford Industries can now compare its forecasts with those of its retail customers to ensure that the right amount of product is manufactured, leading to improved collaboration and service levels with its trading partners.
The implementation of JDA Master Planning leveraged the solutions automated functionality to compile product information and production constraints to generate weekly sourcing and inventory plans from style to the SKU level. The solution also simultaneously considered factory capacities including special features, raw-material availability, and manufacturing and customer lead-times. Since Master Planning generated a first version of the supply plan by noon each Monday, Oxford Industries’ planners had four and a half days to resolve any issues to accommodate unplanned demand, which translated to an 85-percent improvement in planning efficiency.
Although the company’s sourcing model has since shifted from a typical manufacturing process to more of a purchase process, manufacturing and customer lead-times, SKU-level decisions and some capacity constraints still need to be factored into the supply planning process. Master Planning provides the tools to let managers manage instead of serving as data-entry technicians.