Forecasting plays a crucial role in fashion production, serving as the foundation for budgeting, capacity planning, sales projections, inventory management, workforce allocation, and purchasing decisions. It helps businesses anticipate demand and optimize resources effectively.
There are two primary types of forecasting: qualitative and quantitative.
1. Qualitative Forecasting
This method relies on expert judgment and market insights rather than numerical data. Common techniques include:
- Executive Opinion – Decisions based on the experience of industry leaders.
- Market Research – Surveys and customer feedback to predict trends.
- Delphi Method – A structured approach using expert consensus.
2. Quantitative Forecasting
This method uses historical data and statistical models for predictions. It includes:
- Time Series Models, which analyze past trends and patterns:
- Simple Moving Average
- Weighted Moving Average
- Exponential Smoothing
- Trend Adjustment
- Causal Methods, which establish relationships between variables:
- Linear Regression
- Multiple Linear Regression
Example: Forecasting in a Handloom Saree Business
A boutique specializing in handloom sarees analyzes sales data from the past three years. Using a time series model, they apply exponential smoothing to detect seasonal trends. Based on the forecast, they adjust production schedules, ensuring artisans weave the right quantity of sarees in preferred colors and designs. Additionally, a causal method like multiple regression helps them correlate fabric demand with festival seasons, guiding raw material purchases.
By leveraging forecasting techniques, fashion businesses can reduce excess inventory, prevent stockouts, and optimize production efficiency.
