How to Forecast Demand Accurately for the Next Season: The Billion-Dollar Fashion Retail Question

January 7 2022

Pre-season planning is one of the most complicated problems in forecasting. Every year, fashion retailers face the challenge of accurately predicting future demand for the next season.

What will be the baseline demand for a new item that will be introduced to the market six months from now on? This is a billion-dollar question.

Fashion retailers need to recognize and accept that uncertainty is a fact of life in demand forecasting. The first step for retailers to handle this is by segmenting products using advanced prescriptive and predictive analytics such as clustering algorithms to segment products and defining a supply chain strategy for each segment.

When planning for items with high forecast error, there is very little information available on what will be prevailing fashion in the future.

Forecasting for basic items such as a white t-shirt is relatively easier than fashion items, as forecasts can be based on the sales history of similar items.

But consider forecasting for a new fashion item such as a floral printed neon dress. That’s when things get more complicated.

Fashion items have short life cycles, long lead times, and no historical data to draw upon. Rapidly changing customer preferences, new competition, macro influences, and ‘see now buy now’ trends make it incredibly hard to predict demand accurately in the long run. That’s why judging how many units a fashion retailer will need to order from the supplier becomes more like guesswork.

Guess wrong, and you will either run out of inventory -which is a deal-breaker for many consumers, or stock too much inventory that will need to be marked down later.

To our knowledge, there isn’t ‘one right way’ to accurately forecast demand for new items in fashion. But these days, data is plentiful and there are different approaches that retailers apply.

Here are 6 commonly used methods.

1. Relying on designers, buyers, and merchandisers’ opinion

Despite all the developments in AI-based demand forecasting, many fashion retailers still use a gut-based approach and trust their buyers, merchandisers, and designers to make pre-season forecasts.

Merchandisers read the market, buyers pay visits to production and design houses, and designers use their personal observations of what people will buy. In this method, long-term forecasts are limited by intuitions. This is more of an art and a creativity-based method rather than anything scientific.

Besides, every designer or buyer can work on a narrow segment of the merchandise. For example, one can be working on the scarfs, whereas the other can be working on the crop tops. Therefore, using this method alone, fashion retailers can’t foresee the effects such as cannibalization or product substitution accurately. 

2. Finding similar items in the past and projecting from there

Fashion retailers might have similar products that are close enough to make comparisons. Think of a retailer who wants to forecast demand for a ‘never-out-of-stock product’ like a black dress for the next season.

Typically, the retailer has access to the historical data of existing or previously sold black dresses for the past few years. Looking at previous years’ data can help in forecasting demand at sufficient levels for existing black dresses. But they can’t be 100% efficient in predicting demand for a new item. Because of the fast-changing nature of the fashion industry, it’s quite impossible to fulfill the demand of tomorrow’s consumers if forecasts are based solely on yesterday’s data of similar products.

3. Working with a trend forecasting agency

Unlike other retail industries, fashion is heavily trend-driven. Fashion retailers can work together with data-driven trend forecasting companies that offer predictive analytics on upcoming trends and products.

About Koray Parkin

Koray Parkin is the Inventory Solutions Director of Invent Analytics. He has 10+ years of data science background. At his role at Invent Analytics, he brings his know-how and expertise in demand forecasting, data analysis, algorithm development, and inventory optimization. 


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