ON-SHELF AVAILABILITY SYSTEM AND METHOD

• US 20140032379A1
• Filed: 10/19/2012
• Published: 01/30/2014
• Est. Priority Date: 07/27/2012
• Status: Abandoned Application

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Schuetz, Wolfgang, Martic, Igor
• Application Number:
  US13/655,879
• Publication Number:
  US 20140032379A1
• Time in Patent Office: Days
• US Class Current: 705/28
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

Systems and methods to determine on-shelf availability are provided. In example embodiments, transaction data that includes past sales transactions is accessed. A virtual time scale is determined. The virtual time scale is based on a transformation of the transaction data for a particular product category from a real-time scale to the virtual time scale that provides uniformly distributed sales. At least one estimation parameter of an exponential function based on the virtual time scale and the transaction data is determined. Using the at least one estimation parameter, current sales transaction data is monitored to detect a critical time that indicates a high probability that the particular product category will be out-of-shelf.

Claims

1. A method comprising:

accessing transaction data that includes past sales transactions;

determining, using a processor of a machine, a virtual time scale based on a transformation of the transaction data for a particular product category from a real-time scale to the virtual time scale that provides uniformly distributed sales;

determining at least one estimation parameter of an exponential function based on the virtual time scale and the transaction data; and

using the at least one estimation parameter, monitoring current sales transaction data to detect a critical time that indicates a high probability that the particular product category will be out-of-shelf.

2. The method of claim 1, wherein the at least one estimation parameter is associated with an influencing factor that indicates events that affect sales for the particular product category.

3. The method of claim 2, wherein the influencing factor is selected from the group consisting of price, discount, and promotion.

4. The method of claim 2, further comprising identifying the influencing factor from the transaction data.

5. The method of claim 1, wherein the at least one estimation parameter is associated with a trend.

6. The method of claim 1, wherein the at least one estimation parameter is a base parameter that provides a general characteristic of the exponential function.

7. The method of claim 1, further comprising:

transforming the critical time back to the real-time scale; and

causing a report of the critical time to be presented to a business that provided the transaction data.

8. The method of claim 1, wherein the determining of the virtual time scale comprises:

determining a number of transactions in the real-time scale for over a period of time;

creating a virtual time axis that is the same length as the period of time;

expanding the virtual time axis to have the number of transactions uniformly positioned on the virtual time axis; and

directly assigning each transaction of the number of transactions from the real-time scale to the virtual time axis using linear interpolation.

9. The method of claim 1, wherein the particular product category is a particular product.

10. The method of claim 1, further comprising:

determining an outlier in the transaction data; and

removing the outlier prior to the determining of the at least one estimation parameter.

11. The method of claim 1, further comprising:

determining a boost in the transaction data; and

removing the boost prior to the determining of the at least one estimation parameter.

12. A machine-readable storage medium in communication with at least one processor, the non-transitory machine-readable storage medium storing instructions which, when executed by the at least one processor of a machine, cause the machine to perform operations comprising:

accessing transaction data that includes past sales transactions;

determining, using a processor of a machine, a virtual time scale based on a transformation of the transaction data for a particular product category from a real-time scale to the virtual time scale that provides uniformly distributed sales;

determining at least one estimation parameter of an exponential function based on the virtual time scale and the transaction data; and

using the at least one estimation parameter, monitoring current sales transaction data to detect a critical time that indicates a high probability that the particular product category will be out-of-shelf.

13. The machine-readable storage medium of claim 12, wherein the at least one estimation parameter is associated with an influencing factor that indicates events that affect sales for the particular product category.

14. The machine-readable storage medium of claim 13, wherein the influencing factor is selected from the group consisting of price, discount, and promotion.

15. The machine-readable storage medium of claim 12, wherein the at least one estimation parameter is associated with a trend.

16. The machine-readable storage medium of claim 12, wherein the operations further comprises:

transforming the critical time back to the real-time scale; and

causing a report of the critical time to be presented to a business that provided the transaction data.

17. The machine-readable storage medium of claim 12, wherein the determining of the virtual time scale comprises:

determining a number of transactions in the real-time scale for over a period of time;

creating a virtual time axis that is the same length as the period of time;

expanding the virtual time axis to have the number of transactions uniformly positioned on the virtual time axis; and

directly assigning each transaction of the number of transactions from the real-time scale to the virtual time axis using linear interpolation.

18. The machine-readable storage medium of claim 12, wherein the operations further comprise:

determining an outlier in the transaction data; and

removing the outlier prior to the determining of the at least one estimation parameter.

19. The machine-readable storage medium of claim 12, wherein the operations further comprise:

determining a boost in the transaction data; and

removing the boost prior to the determining of the at least one estimation parameter.

20. A system comprising:

a processor of a machine;

a pattern analysis module to access transaction data that includes past sales transactions and to determine, using the processor of the machine, a virtual time scale based on a transformation of the transaction data for a particular product category from a real-time scale to the virtual time scale that provides uniformly distributed sales;

an estimation module to determine at least one estimation parameter of an exponential function based on the virtual time scale and the transaction data; and

a monitoring module to monitor, using the at least one estimation parameter, current sales transaction data to detect a critical time that indicates a high probability that the particular product category will be out-of-shelf.