Convenience store effectiveness model (CSEM)

• US 7,548,879 B2
• Filed: 07/18/2002
• Issued: 06/16/2009
• Est. Priority Date: 07/18/2002
• Status: Expired due to Fees

• Current Assignee: NCR Voyix Corporation
• Original Assignee: NCR Corporation
• Inventors: Tarmy, Phinsuda, Cash, Charles Robert, Poynter, William Douglas
• Primary Examiner(s): Kazimi; Hani
• Assistant Examiner(s): TINKLER, MURIEL S
• Application Number:
  US10/197,476
• Publication Number:
  US 20040015424A1
• Time in Patent Office: 2,525 Days
• Field of Search: 705/35, 705/16, 705/42, 705/43, 705/70, 705/7, 705/8, 705/9, 705/10, 703/22, 235/383
• US Class Current: 705/35
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

Convenience Store Effectiveness Model (CSEM) is a self-contained PC application to quantitatively predict operational and financial impact of changes to Convenience Store (CStore) and Financial Services Center (FSC) operations. CSEM includes Simulation Analysis Module and Financial Analysis Module. Simulation Analysis Module includes FSC model and CStore model. CStore model predicts the effect of an unlimited number of changes in store design, customer demand patterns, and checkout procedures on store performance. Financial Analysis Module creates a Profit and Loss (P&L) statement showing cash flows, Net Present Value (NPV), and Internal Rate of Return (IRR) for deploying FSCs using simulation results or user input values. An analyst can use CSEM to provide a sound and quantified basis for developing a business case for investing in new technologies, i.e., FSC, or other design and procedure changes in a convenience store environment.

Claims

1. A computer-implemented method of quantitatively evaluating alternative convenience store and alternative financial services center operations using a simulation model, comprising:

inputting parameter values describing the alternative convenience store and the alternative financial services center operations into the simulation model wherein the operations represent complex interactions between customers, staff and convenience store service points;

running the simulation model by the computer wherein the parameter values describing the alternative convenience store and the alternative financial services center operations are transformed into performance results where the transformation is performed in either an unlimited arrival mode or a limited arrival mode; and

outputting the results from the simulation model where the results predict the effect of the alternative financial services center'"'"'s operations on the alternative convenience store.

2. The method of claim 1, wherein the input parameters are listed in a data input dictionary used to define the parameters used in the simulation model.

3. The method of claim 1, wherein the simulation model includes one of a financial service center model and a convenience store model.

4. The method of claim 3, wherein the financial service center model includes the ability to model changes in financial service center design, transaction features, and transaction times.

5. The method of claim 1, wherein the simulation model simulates a number and type of service points, transaction times, customer arrival patterns, number of items purchased, and personnel schedules.

6. The method of claim 3, wherein the convenience store model includes simulation parameter categories of model parameters, customer demand, transaction characteristics, labor schedule, and configuration.

7. The method of claim 3, wherein the financial service center model includes simulation parameter categories of model parameters, customer demand, transaction probabilities, ATM transactions, check transactions, bill payment transactions, money order transactions, wire transfer transactions, phone card transactions, and general transactions.

8. The method of claim 1, wherein the parameters are divided into a customer demand category, a transaction characteristics category, a labor schedule category, a configuration category, and a financial parameters category.

9. The method of claim 8, wherein the configuration category includes parameters defining the length and resources in a scenario.

10. The method of claim 9, wherein the resources include a number of checkout counters and number of financial service centers.

11. The method of claim 8, wherein the customer demand category has parameters controlling the workload of the alternative convenience store.

12. The method of claim 11, wherein the parameters that control the workload include a number of customer arrivals, where customers go, and number of items purchased.

13. The method of claim 8, wherein the parameters include a number of replications, a stream number identifier and check input option identifier.

14. The method of claim 1, further comprising editing the input parameter values.

15. The method of claim 1, wherein the input parameter values include a value and a range.

16. The method of claim 1, further comprising one of outputting a report and displaying an animation of the results of the simulation.

17. The method of claim 1, further comprising repeating said running step and step outputting step.

18. The method of claim 1, wherein the results of said outputting step include performance measurements for each type of resource.

19. The method of claim 3, wherein the convenience store model includes non-scalar parameters.

20. The method of claim 19, wherein the non-scalar parameters include an expected number of arrivals, a balking probability, a customer decision matrix, a cashier schedule, a supervisor schedule, a number of items purchased distribution, and a number of gallons of gas purchased distribution.

21. A computer-implemented method of quantitatively evaluating alternative convenience store and financial services center operations using a simulation model and a financial analysis model, comprising:

inputting parameter values describing the alternative convenience store operations into the simulation model and the financial analysis model, wherein the simulation model comprises a financial services center model and a convenience store simulation model and wherein the operations represent complex interactions between customers, staff and convenience store service points;

running the simulation model by the computer;

running the financial analysis model;

transforming the parameter values describing the alternative convenience store operations into performance results where the transforming step is performed in either an unlimited arrival mode or a limited arrival mode and uses both the simulation model and the financial analysis model;

outputting the results from the financial analysis model; and

outputting the results from the simulation model wherein the results from the two models predict the effect of the alternative convenience store and financial services center operations.

22. The method of claim 21, wherein the financial analysis model enables the user to create a profit and loss statement for the alternative convenience store.

23. The method of claim 22, wherein the profit and loss statement includes a cash flow, a net present value, and an internal rate of return.