Predictive demand analysis is an analytical method used in supply management to anticipate a company’s future product or service requirements. It is based on historical data and statistical models. This approach is based on the use of advanced techniques such as :
- machine learning,
- time series,
- regression models to identify trends,
- diagrams,
- and factors influencing future demand.
Some key points about predictive demand analysis :
1. Use of historical data: Predictive demand analysis uses historical data on sales, seasonal trends, past promotions and special events.
2. Forecast modeling : Historical data is used to create forecasting models that can be used to predict future demand. These models can be simple, such as linear regression models, or more complex, such as neural networks or machine learning methods.
3. Factors influencing demand: In addition to historical data, predictive demand analysis can take into account other factors that could influence future demand, such as
- economic trends,
- weather conditions,
- consumer behavior,
- or competitors’ actions.
4. Improved planning: By using accurate forecasts of future demand, companies can improve their supply chain planning by anticipating demand fluctuations, adjusting stock levels, and planning production and distribution more efficiently.
5. Reduce costs and improve customer service: By accurately predicting future demand, companies can reduce the costs associated with excess or insufficient inventory, while improving customer satisfaction by ensuring adequate product availability.
