The worldwide known fast fashion retailer H&M is suffering from a problem with unsold stock worth $4.3 billion. The situation became clear when the Q1 report of the Swedish giant was released. It made analysts and commentators speculating with the company capability to stay competitive. Such statements are problematic, especially for a public company with significant free float.
New York Times recently reported that the problem began emerging last year when H&M declared an unexpected quarterly drop in sales. Industry experts explained the decline with an increased amount of clients preferring to shop online. Karl-Johan Persson, the H&M CEO explained the unsold inventory with expanding e-commerce operations as well as 220 new stores that will be open, so the company actually need it. In the business world of fast-fashion where trends are rapidly changing, such statement is, at least, bold.
How to prevent an overstock business problem?
The sales forecast on a retail level is the fast and simple answer. To make the long story short, a business should start project future sales at a granular level, per single retail and e-commerce point of sale, and then scale up.
It sounds like a piece of pie but in multinational businesses like H&M with +4700 retail locations, we know it is not. In equations with multiple variables, trends are the key. Those tiny percentages of change on different variables, which at a certain point may result in $4.3B overstock. Relatively unsuccessful spring/summer collection might be caused by poor interpretation of fashion trends. Declining retail turnover could be a trend that could be caused by e-commerce prevalence but it might be a signal for unsuccessful designs or poor production quality.
This is why predictive analytics in retail is crucial. Its smart utilization can save not millions but billions of hard-earned dollars. Companies like H&M have it in implemented to one or another extent but we do not anything about its utilization. One out of 4 data scientists said in a recent poll by Kaggle that the result of their work is not used by the businesses that paid for it.
It is widely known that Inditex, a direct competitor of H&M and owner of the Zara brand, strongly relies on analytics in a lot of their business processes. From trend analysis to production analysis in order to make sure that current design trends are interpreted correctly in future collections. Analytics is applied in production to secure the shortest possible supply chain as well as flexibility enabling Inditex to withdraw bad performing designs as soon as possible. This way, the company is securing minimum waste at any stage of the supply chain.
Logistics is another area within fast-fashion businesses where analytics deliver efficiency insights. RFID tags are extremely helpful for data generation while being relatively inexpensive. Such tags are helping brands to be aware of not just stock but also its movement within the retail location. Data like this is hiding insights on customer navigation through the retail location and its design preferences.
Then again, the key is in the data. Are businesses smart enough to dig deep within and to utilize sensibly insights delivered by data analytics?