Methods for reducing retail out-of-stocks using store-level RFID data

• US 20070061210A1
• Filed: 09/11/2006
• Published: 03/15/2007
• Est. Priority Date: 09/09/2005
• Status: Abandoned Application

• Current Assignee: TrueDemand Software, Inc. (Acosta Holdco, Inc.)
• Original Assignee: TrueDemand Software, Inc. (Acosta Holdco, Inc.)
• Inventors: Weng, Jie, Lee, Calvin, Kuppahally, Suresh, Jayaraman, Baskar, Chen, Li, Blanchard, Raymond, Peters, Eric
• Application Number:
  US11/519,449
• Publication Number:
  US 20070061210A1
• Time in Patent Office: Days
• US Class Current: 705/22
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 20/203 Inventory monitoring
• CPC Class Codes: G07F 9/026 for alarm, monitoring and auditing in vending machines or means for indication, e.g. when empty

Abstract

Methods and systems for predicting out-of-stock occurrences and for assigning root causes to out-of-stock occurrences are described. In one implementation, inventory data and point of sale data are collected. An expected lost sales value is determined. A true demand is determined based on the point-of-sale data and the expected lost sales value. A probability of an out-of-stock occurrence is determined based on the inventory data. In another implementation, an out-of-stock occurrence is identified. The out-of-stock occurrence is classified, and one or more root causes are assigned to the out-of-stock occurrence.

Claims

1. A computer-implemented method, comprising:

collecting inventory data and point-of-sale (POS) data;

determining an expected lost sales value;

determining a true demand based on the POS data and the expected lost sales value; and

determining a probability of an out-of-stock (OOS) occurrence based on the inventory data.

2. The method of claim 1, further comprising:

identifying an OOS prevention measure, thereby reducing one of the probability of the OOS occurrence or the expected lost sales value to less a respective specified threshold.

3. The method of claim 1, wherein collecting inventory data comprises collecting inventory movement data.

4. The method of claim 3, wherein collecting inventory movement data comprises tracking inventory movement using radio frequency identification (RFID).

5. The method of claim 1, wherein determining the probability of an OOS occurrence comprises determining at least one of a probability of a store OOS occurrence or a probability of a floor OOS occurrence.

6. The method of claim 1, wherein identifying an OOS prevention measure comprises determining an optimal floor capacity.

7. The method of claim 1, wherein identifying an OOS prevention measure comprises determining an optimal number of floor replenishment trips.

8. The method of claim 1, wherein identifying an OOS, prevention measure comprises determining a store replenishment time.

9. A system, comprising:

one or more processors;

one or more sets of instructions configured for execution by the one or more processors;

the one or more sets of instructions comprising instructions;

to collect inventory data and point-of-sale (POS) data;

to determine an expected lost sales value;

to determine a true demand based on the POS data and the expected lost sales value; and

to determine a probability of an out-of-stock (OOS) occurrence based on the inventory data.

10. A computer-readable medium having stored thereon instructions, which, when executed by a processor, causes the processor to perform the operations of:

collecting inventory data and point-of-sale (POS) data;

determining an expected lost sales value;

determining a true demand based on the POS data and the expected lost sales value; and

determining a probability of an out-of-stock (OOS) occurrence based on the inventory data.

11. A system, comprising:

means for collecting inventory data and point-of-sale (POS) data;

means for determining an expected lost sales value;

means for determining a true demand based on the POS data and the expected lost sales value; and

means for determining a probability of an out-of-stock (OOS) occurrence based on the inventory data.

12. A computer-implemented method, comprising:

identifying an out-of-stock (OOS) occurrence;

classifying the OOS occurrence; and

assigning one or more root causes to the OOS occurrence.

13. The method of claim 12, further comprising:

collecting data.

14. The method of claim 13, wherein collecting data comprises collecting at least one of the group consisting of:

warehouse inventory data, store inventory data, backroom inventory data, floor inventory data, point-of-sale data, inventory movement data, and forecast and replenishment data.

15. The method of claim 12, wherein classifying the OOS occurrence comprises:

classifying the OOS occurrence as a store OOS if a store inventory is zero and a floor inventory is zero; and

classifying the OOS occurrence as a floor OOS if the store inventory is not zero and the floor inventory is zero.

16. The method of claim 12, wherein classifying the OOS occurrence comprises:

identifying one or more root cause conditions; and

mapping at least a subset of the root cause conditions to the OOS occurrence.

17. The method of claim 12, further comprising:

determining a lost sales value for the OOS occurrence;

analyzing the OOS occurrence and the lost sales value; and

identifying one or more OOS prevention actions based on the analyzing.

18. The method of claim 12, further comprising:

upon identification of an additional OOS occurrence, performing the classifying and the assigning steps with respect to the additional OOS occurrence.

19. A system, comprising:

one or more processors;

one or more sets of instructions configured for execution by the one or more processors;

the one or more sets of instructions comprising instructions;

to identify an out-of-stock (OOS) occurrence;

to classify the OOS occurrence; and

to assign one or more root causes to the OOS occurrence.

20. A computer-readable medium having stored thereon instructions, which, when executed by a processor, causes the processor to perform the operations of:

identifying an out-of-stock (OOS) occurrence;

classifying the OOS occurrence; and

assigning one or more root causes to the OOS occurrence.

21. A system, comprising:

means for identifying an out-of-stock (OOS) occurrence;

means for classifying the OOS occurrence; and

means for assigning one or more root causes to the OOS occurrence.

22. A computer-implemented method, comprising:

determining a store inventory and a floor inventory over a time period;

identifying and classifying one or more OOS occurrences within the time period based on the store inventory and the floor inventory;

identifying one or more root cause conditions present during the time period, including applying one or more root cause condition rules;

mapping each identified OOS occurrence to at least a subset of the identified root cause conditions;

assigning one or more root causes to each identified OOS occurrence based on the mapping;

estimating, for each identified OOS occurrence, a respective lost sales value;

analyzing the identified OOS occurrences and lost sales values; and

identifying one or more OOS prevention actions based on the analyzing.