User:Saintgraciemay/Fashion Forecasting

Demand forecasting

One of the most significant challenges confronting retailers and wholesalers in any sector is Demand forecasting. Businesses may make informed judgments regarding pricing and company expansion plans thanks to the vital information that accurate demand forecasting provides about prospective earnings in their present market. Future sales may be lost if demand is overestimated; on the other hand, if suppliers are left with a surplus, significant discount strategies may be required, potentially resulting in losses and cash flow difficulties.

Demand forecasting is particularly complicated in the fashion business because of seasonal trends, a lack of data, and overall unpredictability.

Numerous factors must be considered by a smart fashion forecaster, including the political and economic context, geographical demography, customer expectations, market trends, internal corporate plans, and many more. Projecting previous patterns into the future and seeking indicators of change in order to anticipate impending events are the two basic objectives of "forecasting" in this context.

Forecasting methods


 * Usual methods

The primary building block of usual methods is typically a standard forecast, taken from a particular piece of software or the sales from the previous year. The practitioner then revises this standard by taking into consideration the explanatory factors. Pros of this method are that the influence of seasonality and the primary explanatory factors might make the outcome highly accurate. Cons of this method are that if there are too many variables being processed, the analysis will become inaccurate and difficult, making the task exceedingly tiresome. In addition to this, if there are too many elements, the findings will vary depending on the operator's level of expertise.


 * Advanced methods

The existence of historical data is the first factor to consider while developing a forecasting model.

The fashion industry tends to needs forecasts at two levels of data aggregation:

The "family level" allows businesses to plan and arrange mid-term purchases, manufacturing, and supply since it consists of products from the same category (T-shirts, trousers, etc.). There is often historical data for this level of aggregation.

To restock and distribute goods in stores over a shorter time horizon, the "SKU level" is essential. References (SKU)  are fleeting since they are made for a single season only. As a result, historical data are unavailable.

Class and fashion trends

The higher classes' clothes start to lose their distinctiveness as the lower classes progressively emulate them. When this happens, new concepts that serve as the new class markers must take the place of the current trends. As a result, the upper classes start to influence the growth of fashion, while the lower classes serve as “replicators”