Demand forecasting in the presence of unobserved lost-sales

Patent metadata

Field	Value
Publication number	US-2016155137-A1
Application number	US-201414556294-A
Country	US
Kind code	A1
Filing date	Dec 1, 2014
Priority date	Dec 1, 2014
Publication date	Jun 2, 2016
Grant date	—

Abstract

A demand forecasting system includes a market information processing module that processes a historical sales dataset to provide a lost market rate probability dataset, a lost-sales forecasting module that processes the lost market rate probability dataset to provide a lost-sales dataset, a market size forecasting module that processes the lost market rate probability dataset to provide a market size dataset as a function of the lost market rate probability, a demand forecasting module that processes the lost-sales dataset and the historical sales dataset to provide a demand dataset and a market share dataset as functions of the lost-sales dataset and the historical sales dataset and a best fit optimization module that processes the market size dataset and the market share dataset to provide a set of best fit parameters for the market size and the market share or the demand. A corresponding method is also described.

First claim

What is claimed is:

1 . A system for forecasting demand, the system comprising:
a data retrieval module configured to retrieve a historical sales dataset and corresponding sales attributes dataset for a set of sellable commodities comprising one or more sellable commodities; the data retrieval module further configured to retrieve a functional form dataset for determining a market size model and a market share model for the set of sellable commodities; a market information processing module configured to process the historical sales dataset to provide a lost market rate probability dataset for the set of sellable commodities; a lost-sales forecasting module configured to process the lost market rate probability dataset to provide a lost-sales dataset as a function of the lost market rate probability for the set of sellable commodities; a market size forecasting module configured to process the lost market rate probability dataset to provide a market size dataset as a function of the lost market rate probability for the set of sellable commodities; and a demand forecasting module configured to process the lost-sales dataset and the historical sales dataset to provide a demand dataset and a market share dataset as functions of the lost-sales dataset and the historical sales dataset for the set of sellable commodities.

2 . The system of claim 1 , further comprising a best fit optimization module configured to process the market size dataset and the market share dataset with a best fit optimization module to provide a set of best fit parameters for the market size and the market share or the demand for the set of sellable commodities.

3 . The system of claim 2 , wherein the best fit optimization module is further configured to process the market size dataset and the market share dataset using mixed integer linear programming.

4 . The system of claim 1 , further comprising a data presentation module configured to present datasets to one or more users.

5 . The system of claim 1 , wherein the demand forecasting module is further configured to process the sales attributes dataset to provide a demand dataset and a market share dataset.

6 . The system of claim 1 , wherein the market size forecasting module and the lost-sales forecasting module are further configured to process the lost market rate probability model by modeling the market size and the lost-sales as piecewise linear functions of the lost market rate probability.

7 . The system of claim 1 , wherein the market information processing module is further configured to process the historical sales dataset by modeling the lost market rate probability as a hard-maximum linear approximation of an attractiveness of a no-purchase choice.

8 . The system of claim 1 , wherein the lost-sales comprise multiple components that can be further isolated by jointly computing a market share of each lost-sales component.

9 . The system of claim 1 , wherein the historical sales dataset includes incomplete information on lost-sales.

10 . A method for forecasting demand, executed by a computer, comprising:
retrieving with a data retrieval module a historical sales dataset and corresponding sales attributes dataset for a set of sellable commodities comprising one or more sellable commodities; retrieving with the data retrieval module a functional form dataset for determining a market size model and a market share model for the set of sellable commodities; processing the historical sales dataset with a market information processing module to provide a lost market rate probability dataset for the set of sellable commodities; processing the lost market rate probability dataset with a market size forecasting module and a lost-sales forecasting module to provide a market size dataset and a lost-sales dataset as functions of the lost market rate probability for the set of sellable commodities; and processing the lost-sales dataset and the historical sales dataset with a demand forecasting module to provide a demand dataset and a market share dataset as functions of the lost-sales dataset and the historical sales dataset for the set of sellable commodities.

11 . The method of claim 10 , further comprising processing the market size dataset and the market share dataset with a best fit optimization module to provide a set of best fit parameters for the market size and the market share or the demand for the set of sellable commodities.

12 . The method of claim 11 , wherein processing the market size dataset and the market share dataset to provide a set of best fit parameters for the market size and the market share or the demand for the set of sellable commodities comprises mixed integer linear programming.

13 . The method of claim 10 , wherein processing the historical sales dataset to provide a lost market rate probability dataset for the set of sellable commodities comprises global optimization by using an SOS-2 variable.

14 . The method of claim 10 , wherein processing the lost market rate probability model to provide a market size dataset and a lost-sales dataset as functions of the lost market rate probability for the set of sellable commodities comprises modeling the market size and the lost-sales as piecewise linear functions of the lost market rate probability.

15 . The method of claim 10 , wherein the historical sales dataset includes incomplete information on lost-sales.

16 . The method of claim 10 , wherein the lost market rate probability is modeled as a hard-maximum linear approximation of an attractiveness of a no-purchase choice.

17 . The method of claim 10 , wherein the lost-sales comprise multiple components that can be further isolated by jointly computing a market share of each lost-sales component.

18 . The method of claim 10 , wherein processing the lost-sales dataset and the historical sales dataset with a demand forecasting module further comprises processing the sales attributes dataset to provide a demand dataset and a market share dataset.

Assignees

• IBM

Inventors

• Harsha Pavithra
• Subramanian Shivaram

Classifications

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title