Planning is one of fashion and retail’s most complex undercurrents. Traditionally a sort of black art, blending intuition, prediction, and commercial acumen, planning has morphed to become almost completely a science: data-driven, exact, and assembled from a larger range of variables than a human could ever have managed.

At the same time, the tools that retailers use to plan their ranges, assortments, margins, materials, and other elements have also advanced. Where once planning was done in spreadsheets, like an accounting exercise, today the technology sector offers planning tools that are driven by machine learning models, and that can make recommendations based on a near-real-time market realities.

Many retailers, though, still manage their planning processes manually, or in ad-hoc systems – primarily because they do not realise the depth and scope of planning platforms available in 2021, or how important machine learning is going to be for the future of planning and forecasting.

And a similar change is happening in the field of trend analysis. Identifying future trends and factoring these into the next cycle of product design and development used to be a relative structured exercise. Runway shows scheduled according to a fixed calendar would provide the templates and themes a full year in advance, leaving designers and buyers the task of translating those pillars into products and collections that would chime with their target demographic.

Today, trend is truly universal, and totally seasonless. From street style feeds to individual influencers, the ability to shape the future of fashion has been democratised. And instead of being relatively predictable, trend is now reactive – running minute to minute, and shedding even the ghost of a concrete calendar.

Needless to say, keeping a finger on the pulse of fashion is an impossible task for the traditional, intuition-driven trend analysis approach. There are simply too many variables, all changing constantly, for brands and retailers to have any hope of keeping up without turning to new sources and solutions.

In this world, what is the role of the trend service provider? What used to be something of an art – with expert predictors earning recognition – could potentially become yet another data science discipline. Are brands and retailers willing to trust machine learning models to make trend recommendations?

Plan to visit The Interline throughout January 2021 for the latest on intelligent, data-driven planning, forecasting, and trend prediction tools, and insights into how the fashion industry is responding to the need to become more agile and data-driven, rather than relying on historical insights and intuition.