System, method and article of manufacture to optimize inventory and merchandising shelf space utilization

• US 6,341,269 B1
• Filed: 12/30/1999
• Issued: 01/22/2002
• Est. Priority Date: 01/26/1999
• Status: Expired due to Fees

• Current Assignee: Mercari Technologies, Inc.
• Original Assignee: Mercari Technologies, Inc.
• Inventors: Waller, Matthew A., Dulaney, Earl F.
• Primary Examiner(s): Millin, Vincent
• Assistant Examiner(s): PATEL, JAGDISH
• Application Number:
  US09/475,612
• Time in Patent Office: 754 Days
• Field of Search: 705/7, 705/8, 705/28, 705/22
• 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: G06Q 30/0204 Market segmentation

Abstract

The present invention relates to inventory management systems and processes at the retail, wholesale and/or distributor level. The present invention particularly involves a system, method and article of manufacture that optimizes inventory and merchandising shelf space utilization based upon cost and lost sales, with or without considering physical space constraints. In an exemplary embodiment, the system includes a bank of memory, a processor, an input and an output, and a computer program. The system optimizes inventory or store facings using various data and extrapolated computations. The system optimizes inventory using facing optimization which is an approach to shelf inventory management that minimizes the sum of expected annual cost of lost sales and expected annual inventory holding cost. The process of facing optimization requires the assimilation of relevant data for each particular item to be evaluated. The data to be collected include store-level point-of-sale (a.k.a., POS) data, frequency of shelf replenishment, shelf-level order cycle time, space available, space required per SKU, number of units per facing, cost to the retailer of one unit of SKU, price they sell it for, the inventory holding cost factor, and the unit cost of a lost sale. Store-level POS is used to measure the mean of daily sales and the variability of daily sales (a.k.a., standard deviation of demand). The system evaluates these variables when determining the optimal solution for an unconstrained space or a constrained space of a particular facility.

Claims

1. An inventory optimization method to enable a user to select products for a space, said method comprising:

determining at least one optimization analysis objective;

communicating operationally dependent information about various products and importing said operationally dependent information to an inventory database;

identifying a subset of data elements within said database on which to perform an optimization analysis and communicating said subset of data elements to an optimizing computer;

performing an optimization analysis upon said subset of data elements using said optimizing computer to thereby obtain an unconstrained report and a constrained report; and

,

providing said reports to a user to enable the user to select products for the space.

2. The method as recited in claim 1 wherein said method further comprises facilitating multiple user access, viewing and contingent control of said method execution via a computer compatible communications network.

3. The method as recited in claim 1 wherein said at least one optimization analysis objective is chosen from the group including maximizing economic profit, minimizing total cost, maximizing units sold, maximizing sales revenue and maximizing gross margin.

4. The method as recited in claim 3 wherein said at least one optimization analysis objective includes the further step of calculating cost of lost sales.

5. The method as recited in claim 4 wherein said cost of lost sales is determined based upon consumer responses.

6. The method as recited in claim 5 wherein said consumer responses are chosen from a consumer response group including consumers who will go to a competitor, consumers who will never buy the product again, consumers who will never shop the store again, consumers who will make no purchases, consumers who will shop less frequently, consumers who will switch brand, consumers who will switch product, and consumers who will switch size of product or other behavior.

7. A computer readable medium encoded with a computer program for determining optimal space utilization comprising:

a code segment for determining at least one optimization analysis objective;

a code segment for communicating operationally dependent information about various products; and

for importing said operationally dependent information to an optimization process database;

a code segment for identifying a subset of data elements within said database upon which to perform an optimization analysis; and

a code segment for communicating said subset of data elements to said optimization analysis;

a code segment for performing said optimization analysis upon said subset of data elements to produce an unconstrained and a constrained optimization analysis report that enables a user to utilize the space optimally.

8. The program as recited in claim 7 wherein said importing of operationally dependent information to said optimization process database further comprises:

a code segment to provide for selection of files for import;

a code segment to validate said file selection;

a code segment to perform import data transformations; and

a code segment to import said transformed data to said database.

9. The program as recited in claim 7 wherein said identification of said subset of data elements further comprises:

a code segment to display at least a portion of the imported database files; and

,

a code segment to enable a user to specify which of said data elements are to be filtered for subsequent display and further analysis using said optimization analysis.

10. The program as recited in claim 7 wherein said optimization analysis comprises code segments for performing an unconstrained optimization analysis and a constrained optimization analysis upon said subset of data elements.

11. The program as recited in claim 10 wherein said performance of said constrained optimization analysis further comprises:

a code segment to allow the user to update settings for the optimization and to initiate the optimization process;

a code segment to control the linear programming optimization process; and

a code segment to calculate relevant financial and operational metrics.

12. The program as recited in claim 10 wherein said performance of said unconstrained optimization analysis further comprises:

a code segment to allow the user to initiate the optimization process;

a code segment for executing the optimization process; and

,

a code segment to calculate relevant financial and operational metrics.

13. The program as recited in claim 7 wherein said at least one optimization analysis objective is chosen from an optimization analysis group including maximizing economic profit, minimizing total cost, maximizing units sold, maximizing sales revenue and maximizing gross margin.

14. The program as recited in claim 7 wherein said at least one optimization analysis objective includes the further step of calculating cost of lost sales.

15. The program as recited in claim 14 wherein said cost of lost sales is determined based upon consumer responses.

16. The program as recited in claim 13 wherein said consumer responses are chosen from a consumer response group including consumers who will go to a competitor, consumers who will never buy the product again, consumers who will never shop the store again, consumers who will make no purchases, consumers who will shop less frequently, consumers who will switch brand, consumers who will switch product, and consumers who will switch size of product or other behavior.