“It is a capital mistake to theorise before one has data.” Sherlock Holmes (Sir Arthur Conan Doyle)
… particularly as the emergence of the so-called “Big Data” makes the issue of data scarcity a thing of the past. The capture of data and its transformation into business insights as a core element of strategy has long helped the Spanish retailer Zara raise productivity, improve decision-making and gain competitive advantages. As a result, it overtook Gap as the world’s largest clothing retailer in 2008.
Zara has been a poster child for supply chain excellence because of its ability to spot trends as they emerge and to deliver new items to stores quickly to satisfy the needs of its fashion-conscious customers. In an industry where standard lead-time – designing, producing and delivering new garments – is about nine months, Zara leads the way with as little as two to three weeks. However, the driver behind this effective supply chain is its use of data and analytics for accurate forecasting and decision-making. It is enabled through processes and systems built to bring together data, analytics, frontline tools, and people to create business value. Zara’s key differentiating uses of analytics are to:-
– institutionalise the collection and use of real-time statistical market data. Zara’s cross-functional design teams pore over daily sales and inventory reports, to see what is selling and what is not, and continually update their view of the market. Twice-weekly orders from store managers provide further real-time information on what might sell;
– supplement the statistical market data with fined-grained raw market data. Empowered retail managers regularly send word-of-mouth feedback on customer wants and preferences – anything from “the length of this skirt is too long” to “our customers do not like the fabric of this dress”. Managers can also suggest modifications to an existing style or propose entirely new articles or designs. The benefit of insight from stores is epitomised by the example of a line of slim-fit clothes that was not selling. The feedback from the stores was that women loved how the slim-fit clothes looked but couldn’t fit into their usual sizes when they tried on the garments. Zara recalled the items and replaced the labels with the next sizes up and sales exploded;
– create an adaptive and informal planning process. It is ingrained into the company’s flexible supply chain as it maintains strong ties with its 1,400 external suppliers, which work closely with its designers and marketers. Based on market data, Zara experiments with a wide variety of offerings in small batches. If they prove a hit, production is ramped up in response to local conditions while at the same maintaining lean inventories and a low level of markdowns;
– disseminate information widely throughout the organisation. Designers, pattern makers, marketing managers and merchandisers, as well as everyone else involved in the production, are housed on a single open-plan office floor. This enables frequent discussions, serendipitous encounters and visual inspection. The whole team can diagnose the overall market, see how their work fits into the big picture and spot opportunities that might otherwise fall between the cracks of organisational silos;
– build a simple and effective information technology systems available to all. Zara’s in-house IT reflects the way of the organisation. It is silo-free as well as accessible to vendors and suppliers which report it easy to use and quick to provide answers; and
– build a culture of data usage to learn new things and discover the right answers. Data analytics is at the base of Zara’s model and its use for decision-making is encouraged as bad decisions are not severely punished. Failure rates for Zara’s new products are reported to be just 1% versus an industry average of 10%.
Few years ago, Zara entered the virtual ground of e-commerce in the US, Europe and Japan. With this move, it entered the next generation of the use analytics for decision-making and real time marketing: tracking the behaviour of individual customers from Internet click streams, update their preferences, and model their likely behaviour in real time in addition to monitoring social-network conversations and location-specific smartphone interactions.