Modern business analytics tools supported by Big Data technology allow businesses to create business value. Big Data technology comes with the capability of handling an enormous amount of data which helps in generating more accurate analysis and predictions. Larger the dataset, better is the prediction, and firms are leveraging this advantage of the Big Data technology. One such example, where the business has used Big Data analytics results into its business strategy is the Spanish retailer Zara. Big Data heled Zara to raise productivity, improve decision-making and gain a competitive advantage in the fashion industry.
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:-
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. This approach helps firms to avoid big losses arising from the massive investment without evaluating the response from the market.
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 simple and effective IT systems for 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.
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%.
A 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.