System and method for managing extra calendar periods in retail

Abstract

Systems (100), methods (200), and other embodiments (600) for providing management of retail forecasts associated with a computer application are described. In one embodiment, historical demand data associated with a retail item sold at a retail location is read from an input data structure associated with the computer application (210). A determination is made as to when and where an extra retail period occurs in a forecast time domain (220, 230). Forecasted demand data is generated for retail periods of the forecast time domain, except the extra retail period, based on the historical demand data (260). Forecasted demand data is generated for the extra retail period based on a portion of the forecasted demand data for the retail periods of the forecast time domain (270). An output data structure is transformed (290) by populating the output data structure with the forecasted demand data for the retail periods, including the extra retail period, of the forecast time domain.

Description

Background technique [0001] Forecasting demand (eg, sales and associated inventory) is a large part of managing a retail business. Retailers often manage their business with a retail calendar that periodically includes an extra week (week 53) (eg, 364 days organized into 13-week quarters) so that year-end remains about the same time of year. The occasional (e.g., every 5 or 6 years) week 53 stems from the fact that there are usually 365 days in a year. In addition, the 53rd week also stems from the influence of leap years, which compensate for the fact that the rotation of the Earth does not correspond exactly to the 365-day year. In effect, week 53 puts the retail calendar in sync with respect to making seasonal comparisons. [0002] The retailer expects to have some control over how week 53 is managed within the time dimension of the forecasting system. The current approach in the retail industry is to perform corrections manually to correctly account for week 53. This i...

AI Technical Summary

Problems solved by technology

This is a time-consuming endeavor for the retailer when forecasts are to be generated for many items and stores, thereby limiting the retailer to the simplest and most straightforward corrections

In many cases, the volume of retail merchandise and stores is so large that tasks can only be accomplished by involving both business users and IT, thereby disrupting normal workflow

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