Spanish-based fast fashion giant Zara has quickly risen to the top of the consumer foodchain). In FY 2014, it sold $19.7B worth of merchandise, narrowly being beaten by H&M’s $20.2B. With the help of data analytics, Zara is steadily rising higher and higher to claim the top spot in the retail industry.
Traditional retailers work by consulting with various industry experts regarding the demand and quantity to be supplied in each city or country. This information would then be used to produce the corresponding amount of merchandise in low-cost areas, where the finished products will be transported to the various shops around the world. Since a lot of guess work is involved in the traditional process, some items would quickly sell out while others with surplus stock would have to be sold at a discounted price.
Zara utilized big data analytics in order to cut costs and take the guess work out of manufacturing their merchandise. Zara’s process begins just like the traditional process – with ordering merchandise from their manufacturer. But instead of ordering various quantities per item, Zara only orders a minimum base amount. Once it’s been shipped out to their various outlets, they begin to collect data from their sales and analyzes this against their supply. This method is also used to determine which types and styles of clothing sell more and serves as their guide for future designs and orders.
A big challenge with this process is ensuring that items will be restocked in a short amount of time. In order to do so, Zara opts to have their merchandise produced in their own factories in Spain or Portugal for quicker communication and faster shipment. Although their productions costs are higher than if they had opted for an Asian supplier, their surplus items have drastically been reduced.
This data analytics driven process has led Zara to selling more items at full-price and minimizing the need to sell items at discounted rates. And with a trendy company which supplies ever-changing fashion needs, excess stock is the last thing you want.