Automatic event shifting of demand patterns using multi-variable regression

A method for forecasting product demand for a plurality of products in a forecast year, the method comprising the steps of:

• storing within a data warehouse historical demand data for a plurality of products;
• identifying an event influencing demand for said plurality of products, said event occurring at a different period of time in a prior year than in said forecast year;
• analyzing said historical demand data for said plurality of products to determine a demand component associated with said event;
• removing from said historical demand data said demand component associated with said event;
• re-applying said demand component associated with said event to a period of time in said prior year corresponding to the period of time said event occurs in said forecast year to create a shifted historical demand for said plurality of products;
• and
• calculating a forecast demand for said plurality of products from said shifted historical demand for said plurality of products.

A method for forecasting product demand for a product in a forecast year, the method comprising the steps of:

• storing within a data warehouse historical weekly demand data for a product;
• identifying an event influencing demand for said product, said event occurring at a different week in a prior year than in said forecast year;
• analyzing said historical weekly product demand data for said product to determine a demand component associated with said event;
• removing from said historical weekly demand data said demand component associated with said event;
• re-applying said demand component associated with said event a week in said prior year corresponding to the week said event occurs in said forecast year to create a shifted historical weekly demand for said product;
• and
• calculating a weekly demand forecast for said product from said shifted historical weekly demand for said product.

A method for forecasting product demand for a product in a forecast year, the method comprising the steps of:

• storing within a data warehouse historical weekly demand data for a product;
• identifying an event influencing demand for said product, said event occurring at a different week in a prior year than in said forecast year;
• analyzing said historical weekly product demand data for said product to determine a demand component associated with said event;
• removing from said historical weekly demand data said demand component associated with said event;
• re-applying said demand component associated with said event a week in said prior year corresponding to the week said event occurs in said forecast year to create a shifted historical weekly demand for said product;
• creating a season profile from said product from said shifted historical weekly demand for said product, said seasonal profile comprising a series of weekly seasonal factors;
• and
• calculating a weekly demand forecast for said product from said seasonal factors and an average weekly sales value for said product.

A method for forecasting product demand for a product in a forecast year, the method comprising the steps of:

• storing within a data warehouse historical weekly demand data for a product;
• identifying an event influencing demand for said product, said event occurring at a different week in a prior year than in said forecast year;
• analyzing said historical weekly product demand data for said product to determine a demand component and event uplift factor associated with said event;
• removing from said historical weekly demand data said demand component associated with said event to create a revised historical weekly demand for said product;
• creating a season profile from said product from said revised historical weekly demand for said product, said seasonal profile comprising a series of weekly seasonal factors;
• and
• calculating a weekly demand forecast for said product from said seasonal factors, an average weekly sales value for said product, and said event uplift factor.