Data collection − Collecting and organising data from various sources, such as sales records, market research reports, and industry trends, to inform the forecasting process.

Data cleaning − Removing errors and inconsistencies from the data to ensure accuracy.

Data analysis − Analysing the collected data to identify trends, patterns, and relationships that can be used to make forecasts

Method selection − Selecting the appropriate forecasting method based on the data analysis and the goals of the forecast. Common methods include time series analysis, regression analysis, and machine learning algorithms.

Model creation − Creating a forecasting model using the selected method and the collected data

Vapdation − Vapdating the model by comparing its predictions with actual demand data and adjusting it as necessary.

Implementation − Implementing the demand forecast by using it to inform production, inventory management, and pricing decisions

Monitoring and updating − Monitoring the performance of the demand forecast and updating it regularly to reflect changes in consumer behaviour and market trends

The demand forecasting process is an ongoing and iterative process that requires continuous monitoring and updating to ensure its accuracy and effectiveness.

Methods

There are several methods used for demand forecasting in the fashion industry; some of the most common are

Time series analysis − This method uses historical sales data to make predictions about future demand. It assumes that future demand patterns will be similar to those observed in the past.

Regression analysis − This method uses regression techniques to identify the relationship between demand and one or more independent variables, such as advertising expenditure, consumer income, or seasonal effects.

Causal models − This method involves identifying the key factors that drive demand and using them to make predictions. It requires a deep understanding of the underlying causes of demand.

Machine learning algorithms − This method uses algorithms such as artificial neural networks and decision trees to identify patterns and relationships in large and complex data sets.

The Delphi method − This method involves gathering opinions and forecasts from experts in the field to make predictions about future demand.

Surveys and focus groups − This method involves collecting data directly from consumers through surveys or focus groups to make predictions about future demand.