The fashion and apparel retail industry faces many challenges when it comes to forecasting demand.
In recent years many stores have transitioned to eCommerce stores in a bid to extend their reach to digital customers.
Online consumer behavior has been continuously changing with growing trends and tastes due to social media influencers and the speed of purchase with mobile and other device usage.
Impulse buying at point of sale and flash sales means there must be product availability to ensure successful order fulfilment. Understocking as well as overstocking are key issues that businesses can struggle with.
These retailers also have short market lead times and short selling seasons in a marketplace that is extremely competitive.
Seasonal trends since 2013 highlighting winter peaks in demand can be seen in the graph below.
The Impact of Covid 19
Since March 2020, the fashion and apparel retail sector has been particulary volatile due to the severe impact of Covid 19.
According to S&P GlobaL Market Intelligence UK fashion industry revenue could drop 25% in 2020 due to the impact of the pandemic impact. This is an extremely worring statistic for the industry.
As bricks and mortor stores closed during lockdown, the online demand for fashion and apparel experienced significant changes and challenges due to fluctuating demand.
The graph below shows a sharp decline in the interest in men’s suits during lockdown as more people began working from home.
Source: Google Trends
By comparison, good weather in early lockdown correlated with interest in sandals, demonstrating how the weather can impact demand. May 2020 was the sunniest month on record in the UK.
Source: Google Trends
In July 2020 textile, clothing and footwear stores online sales accounted for 30.9% of all sales.
As the industry strives to recover accurate demand forecasting is more important than ever to fashion and apparel eCommerce stores.
These businesses deal with large amounts of product data across a range of variants. If they can analyze data over the past few months along with other key demand impacting factors, they will be better prepared to achieve future sales forecasts.
How can TrueStock help?
As eCommerce stores have been capturing large amounts of data in recent months, TrueStock can look at historical sales, pricing history and other demand driving data from a range of internal and external sources.
Dynamic factors such as seasonality, the weather, events, promotional periods are considered, their importance scored and weighted to predict future sales demand more accurately.
If there has been any unusual sales activity in historical sales or anomalies these are also analyzed.
The learning algorithms process the data at high speed, constantly and automatically improving with time.
The data is then analyzed for errors using six different metrics. This analysis informs how accurate the forecast is proving to be an enormous help to demand planners over the coming months.
Automatic sales forecasting will let companies know about future inventory replenishment quantities, the costs of replenishment, predicted revenue and the costs of missed sales opportunities.
TrueStock’s machine learning method of demand forecasting can enable retailers to have peace of mind with more accurate future sales forecasts proving a greater degree of certainty during these uncertain times.