Fashion is a $2.5 trillion global industry that touches everyone on the planet. In fact, if the fashion industry were a country, it would have the seventh-largest economy in the world by GDP. You might expect that one of the world’s biggest industries would also be one of the most advanced when it comes to efficiently matching supply and demand. We recently conducted a survey of global fashion brands and found that for the most part, the way that demand predictions are made in fashion has in fact not changed much over the past few decades.
Inaccurate predictions are costly: it’s estimated that 20% of the garments produced each year are never sold, and with well over 100 billion new items being produced annually, this translates into tens of billions of garments that go to waste.
Granted, the fashion industry faces a unique set of challenges in accurately predicting demand: the globalisation of fashion supply chains has translated into long lead times, so brands need to commit to their assortments and buy depths long before items hit the shop floor; outside variables like the weather or a pandemic can introduce major unpredictability. Still, the vast majority of fashion brands still rely on a combination of historical sales data and buying and merchandising expertise to make their demand predictions. Our research reveals that only 11% of brands are using technology tools to support their forecasting.
Of course, Covid has complicated the business of predicting fashion demand even more. 69% of our survey respondents said that Covid had made demand planning harder. Even so, the pandemic has also provided new opportunities to expand into new product lines and categories. Brands that are positioned to capture real-time consumer insights to understand and quickly embrace new preferences are at a distinct advantage.
As we emerge from the pandemic, it’s clear that historical sales data is no longer as useful as it once was for accurately predicting demand. The brands that have survived the crisis will have to continue adapting and embracing new methods and tools, including better predictive analytics, to get their demand predictions right.
Our full report on how fashion brands predict demand explores the current landscape of demand forecasting in the fashion industry, the key priorities and success metrics of global brands, the challenges that Covid has introduced, and prescriptions for how brands can improve their ability to see into the future. Find the full report here.
