Convenience store effectiveness model (CSEM)

• US 20040015424A1
• Filed: 07/18/2002
• Published: 01/22/2004
• Est. Priority Date: 07/18/2002
• Status: Active Grant

• Current Assignee: NCR Voyix Corporation
• Original Assignee: NCR Corporation
• Inventors: Poynter, William Douglas, Tarmy, Phinsuda, Cash, Charles Robert
• Granted Patent: US 7,548,879 B2
• Time in Patent Office: Days
• 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

The Convenience Store Effectiveness Model (CSEM) is a self-contained PC desktop application enabling an analyst to quantitatively predict the operational and financial impact of changes to Convenience Store (CStore) and Financial Services Center (FSC) operations. This application, according to the present invention, includes a Simulation Analysis Module and a Financial Analysis Module. The Simulation Module consists of two simulation models: FSC model and CStore model. An analyst can use the CStore model to predict the effect of an unlimited number of changes in store design, customer demand patterns, and checkout procedures on store performance. The Financial Analysis Module allows the user to create a Profit and Loss (P&L) statement showing the cash flows, Net Present Value (NPV), Internal Rate of Return (IRR) for deploying FSCs using simulation results or user input values. An analyst can use the 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 method of quantitatively evaluating alternative convenience store operations using a simulation model, comprising:

inputting parameter values describing convenience store operations into the simulation model;

running the simulation model; and

outputting results from the simulation model.

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 3, wherein the convenience store model includes the ability to model interactions between customers, staff, and service points of a convenience store.

6. 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.

7. 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.

8. 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.

9. 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.

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

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

12. The method of claim 9, wherein the customer demand category has parameters controlling the workload of the convenience store.

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

14. The method of claim 9, wherein the parameters include a number of replications, a stream number identifier and check input option identifier.

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

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

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

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

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

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

21. The method of claim 20, 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.

22. A method of quantitatively evaluating alternative convenience store operations using a simulation model and a financial analysis model, comprising:

inputting parameter values describing convenience store operations into the simulation model and the financial analysis model;

running the simulation model;

running the financial analysis model;

outputting results from the financial analysis model; and

outputting results from the simulation model.

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

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