The demand forecasting system collects and analyzes data related to food ingredients.
It includes past sales records, inventory levels, seasonal elements, and marketing activities.
This data is used to analyze demand patterns and trends to build a forecasting model.

Various forecast models are applied for demand forecasting.
This involves using statistical modeling techniques or machine learning algorithms to predict the variability of demand.
The model is selected based on the characteristics and requirements of the data
and is periodically updated for accurate predictions.

The demand forecasting system provides predicted demand results.
It predicts and provides the quantity of food ingredient orders for a specific period,
and the forecast results can be checked through an online system.

Efficient ordering plans are established based on predicted demand.
This involves considering inventory levels, supplier availability, ordering cycles, etc.,
to determine the optimal order quantity and timing.
This helps minimize waste due to inventory shortages or excess orders and allows
for the adjustment of production plans.