All you need to about Fast Fashion and Demand Forecasting.

Fast Fashion :

The fashion industry has seen immense transformation over the past couple decades, primarily due to the following reasons:

• Globalization.
• Technology.
• Cost reduction needs.
• Increasing and fluctuating customer demands.

Hence, the fashion industry is synonymous with rapid change and organization's flexibility and responsiveness. The ephemeral aspect of the fashion industry contrasts with the long manufacturing process. This has given rise to a timely strategy in fashion retailing: Fast Fashion. The difference lies in the fact that in the latter, the forecast decisions are to made with limited historical data, time and high accuracy, with the product life cycle extremely short. It not only shortens lead time but also improves design to attract customers.

Demand Forecasting :

Prediction of a company's existing product sales over time is demand forecasting. It plays a key role in Operations Management as an input for planning activities. Poor forecasting leads to high inventory/stock-outs, rush orders and bullwhip propagating through the supply chain upstream.

Improvement of supply chain performance remains the most dominant factor despite the commendable improvement in forecasting methods.
Forecasting processes are really complex in the fashion industry because they have to take into account several exogenous factors such as uncertain demands, seasonality, product variability, lack of historical data and short life cycles.

Why you should use FFF?

Motivated to improve fast fashion business practices, NeenOpal has integrated GM and ELM models to form the 3F algorithm. The proposed algorithm has been tested with both real and sample datasets. We obtain acceptable forecasting accuracy with 3F algorithm. Our algorithm achieves a reasonable level of forecasting accuracy even if very few historical data points are available.

In fashion industry, the consumer demands are highly volatile as their decisions are based on price of the product. To keep the price to a minimum, companies keep the service level high and manufacture products in low cost countries. This increases lead time and as a result supply chain management has to be optimized to improve and synchronize the flow. In order to perform the above, forecasts becomes important. And for forecasts, it is crucial to know the products and sales features in great detail. And so, the following factors are taken into consideration while making fashion forecasts:

• Horizon
• Life Cycle
• Aggregation
• Seasonality
• Exogenous Variables

3F explained :

Our hybrid algorithm combines two Artificial Intelligence (AI) methods, namely the Extreme Learning Machine (ELM) and Grey Model (GM). This yields reasonably accurate forecasts under very tight time schedule and limited data. This novel algorithm is called the Fast Fashion Forecasting(3F) algorithm.

Service deliverables :

Our algorithm integrates Extreme Learning Machine (ELM) with traditional statistical methods to analyse your data and provides you viable solutions.

• Works increasingly well when demand trend slope is large and the seasonal variance is large.
• Our data mining and extrapolation techniques work even without historical data, based on "pre-sales".
• This will enable you to offer a continuous stream of new merchandise to the market which reflects the latest fashion trend, and will help to capture the hottest design that the market would prefer.