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Zara – The Use of ‘Big Data’ to Create Business Value
“It’s a big mistake to think before one has the information.” Sherlock Holmes (Sir Arthur Conan Doyle)
… especially the emergence of the so-called “Big Data” makes the problem of insufficient information a thing of the past. The capture of information and its transformation into the understanding of the business as the mainstay of the strategy has long helped the Spanish retailer Zara increase production, improve cutting regret and get a good competitive advantage. Therefore, it surpassed Gap as the largest clothing retailer in the world in 2008.
Zara has been the poster child for the best products because it is able to see trends as they emerge and deliver new products to stores quickly to meet those trends. the needs of its fashion customers. In an industry where the standard take-time – design, manufacture and delivery of new clothes – is nine months, Zara leads the way with as little as two to three weeks. However, the driver behind this successful product is his use of data and analytics to make accurate predictions and decisions. It is enabled by processes and systems designed to combine data, analytics, front-end tools, and people to create business value. Zara’s key differentiators in terms of analytics are:-
– institutionalize the collection and use of business data in real time. Zara’s cross-functional team teams pore over everyday sales and inventory reports, see what’s selling and what’s not, and continue to change their thinking about doing business. Twice-weekly orders from store managers provide additional real-time information on what will be sold;
– Added statistical business information with fine-grained raw business data. Store managers are able to constantly deliver feedback on customer needs and preferences – everything from “the length of this skirt is too long” to “our people consumer does not like the fabric of this dress”. Administrators may also suggest revisions to an existing model or issue a new version or design. The benefit of being seen by retailers is illustrated by the example of a line of slim-fit clothing that does not sell. Feedback from retailers is that women love how the clothes look but can’t fit their sizes when they try on the clothes. Zara recalls products and changes labels with different sizes and sales collapse;
– create a formal and informal planning process. He has entered into the company’s transformation process as he has a close relationship with its 1,400 external suppliers, who work closely with its manufacturers and marketers. As a business case, Zara experimented with a variety of products in small quantities. If they prove to be a crash, production is increased in response to local conditions while at the same time maintaining a lean production and a low level of signs;
– Publish information widely throughout the organization. Designers, designers, business managers and salespeople, as well as everyone involved in the production, were in an open office. This leads to frequent conversations, awkward encounters and eye checks. The entire team can examine the entire business, see how their work fits into the big picture and see opportunities that may be lost at the break of organizational silos;
– to create a simple and accessible information system for everyone. Zara’s in-house IT reflects the way the organization works. It is silo-free as well as accessible to suppliers and vendors who advertise its ease of use and quick response; and
– to create a culture of using information to learn new things and find the right answers. Data analysis is at the core of Zara’s model and its use for decision-making is encouraged so that bad decisions are not punished. The decline for Zara’s new products is reported to be only 1% compared to the industry average of 10%.
A few years ago, Zara entered the virtual land of e-commerce in America, Europe and Japan. With this move, it enters the next generation of analytics for decision-making and business opportunities: track customer behavior through Internet click flow, modify their preferences, and model their behavior in a timely manner in addition to social supervision. -Telephone dating and the unique location of smartphone dating.
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