Zara is a key player in the fast fashion retail business. It got to this position thanks to its approach towards data analytics. Zara is huge, In FY 2014 it sold $19.7bn worth of merchandise, slightly behind H&M’s $20.2bn and ahead of giants such as Uniqlo ($16.6bn) and GAP ($16.4bn) (http://www.forbes.com/sites/walterloeb/2015/03/30/zara-leads-in-fast-fashion/). And sales are constantly growing, in the first half of 2015 Zara revenues were up 17% compared to H12014. Behind these impressive numbers is a revolution in the world of fashion, which Zara is spearheading. Zara and its fast fashion competitors are leveraging data analytics to change the approach towards supply chain management and inventory.
Traditionally, retailers would do their best to estimate demand for the different SKUs, mostly based on industry experts’ opinions. Next, retailers would manufacture in China or another low-cost market, stock stores, and, well, that’s it. From then on models that sold well would quickly be out of stock, and models that didn’t sell as well would have large inventories and later on be discounted and sold with thinner margins. Early on in the season retailers had some room for re-stocking, but because of the long cycle time from order to distribution, the ability to adapt to demand was quite limited.
Zara solved this problem with an adaptive, data-driven supply chain management. Zara’s process starts in a similar way to the traditional retailers – with an initial order. The difference is that instead of ordering the bulk of the quantity for the season, Zara only orders a small amount of merchandise. Once the merchandise hits the stores, Zara collects sales data and analyzes each SKU’s sales against supply. Zara does even more, it analyzes performance of features of different SKUs. For example, they might identify that pants with patches sell better than pants without patches, or that certain colors or fits move faster than others. Zara then uses these insights to guide their following orders. They will design and manufacture models that have the most popular features to satisfy demand.
The key to making this process happen is short-cycle, small-batch manufacturing. Instead of in the far east, Zara manufactures about half of its merchandise in company-owned facilities in Spain and Portugal (http://www.nytimes.com), reducing the production cycle from a few months to a few weeks. While this might be a more expensive production process, Zara still succeeds in maintaining profitability – Inditex’s (Zara’s parent company) gross margin was 56.9% in Q22015, compared to GAP’s 37.4% (https://ycharts.com). Zara is able to maintain these high margins and capture significant value by (1) reducing the amount of inventory and the cost associated with holding excess inventory, and (2) selling less items on discount thanks to its flexible production process.
With this data-driven approach Zara is able to create more value for its customers. Zara gives customers the models they want, when they want them. While in the traditional world the most popular models are quickly out-of-stock and only the duds are available, in the world of fast fashion supply is highly adaptive and caters to the evolving taste of the consumers. Customers know that items they buy at Zara are new and trendy. As mentioned above, Zara is able to drive value capture by selling more full-priced items and running less discounts. Customers know that batches are small and if they don’t buy the item they like now it will soon be gone (replaced by other trendy, popular items, but still gone). Thus, they will be less likely to wait for end-of-season sales and will be more willing to pay full price.
While Zara was a pioneer in leveraging data to guide production, many companies have been shifting towards the fast fashion model. For example, Uniqlo has recently entered the US market and plans to further expand globally. Uniqlo leverages the fast fashion model but it also has a strong portfolio of basic items that provides stability and is less privy to effect of trends (e.g. the Uniqlo sweater collection). Zara’s ability to remain a strong player in the market depends on many factors (e.g. brands strength, cost structure, marketing) but it also heavily depends on the quality of its insights, which helps drive up margins. As more and more companies adopt this model, Zara must further develop its data-analytics skills and demand-prediction abilities to remain ahead of its competition.