System and method for weather adapted, business performance forecasting Number:7,103,560 from the United States Patent and Trademark Office (PTO) owispatent A system and method for forecasting future retail performance are described herein. The system includes a storage device that stores a sales history database, a weather history database, and a weather forecast database. An analyzer determines the extent to which past retail performance of a plurality of products at a plurality of locations was affected by weather using the sales history database and the weather history database. A configurator, coupled to the analyzer, estimates expected future retail performance of the products at the stores for a plurality of future time periods using the weather forecast database and results produced by the analyzer. A graphical user interface, coupled to the analyzer and the configurator, enables users to view and manipulate results produced by the analyzer and the configurator to thereby forecast future retail performance of the products at the locations.<H forecasting, performance, System, and, meth, 7103560.html, Patent, pto, 7103560

Title: System and method for weather adapted, business performance forecasting

Abstract: A system and method for forecasting future retail performance are described herein. The system includes a storage device that stores a sales history database, a weather history database, and a weather forecast database. An analyzer determines the extent to which past retail performance of a plurality of products at a plurality of locations was affected by weather using the sales history database and the weather history database. A configurator, coupled to the analyzer, estimates expected future retail performance of the products at the stores for a plurality of future time periods using the weather forecast database and results produced by the analyzer. A graphical user interface, coupled to the analyzer and the configurator, enables users to view and manipulate results produced by the analyzer and the configurator to thereby forecast future retail performance of the products at the locations.

Patent Number: 7,103,560 Issued on 09/05/2006 to Fox, et al.

Inventors: Fox; Frederic D. (Wayne, PA), Pearson; Douglas R. (Wyomissing Hills, PA), Caine; Diane (Newark, PA), Mann; Steven (Philadelphia, PA), Shapiro; Ron M. (Franklin, MI), Light; Harve C. (Buckingham, MI), Rodriguez; Suzzane M. (Troy, MI)
Assignee: Planalytics, Inc. (Wayne, PA)

Appl. No.: 09/097,714

Filed: June 16, 1998

Related U.S. Patent Documents


Application Number	Filing Date	Patent Number	Issue Date
08588248	Jan., 1996	5832456

Current U.S. Class: 705/10 ; 705/1; 705/11; 705/7; 705/8; 705/9

Current International Class: G07G 1/00 (20060101)

Field of Search: 705/1,7-11,22,26 707/1,10,100

References Cited [Referenced By]

U.S. Patent Documents


4040629	August 1977	Kelly
5377095	December 1994	Maeda et al.
5491629	February 1996	Fox et al.
5521813	May 1996	Fox et al.
5796932	August 1998	Fox et al.
5832456	November 1998	Fox et al.
6021402	February 2000	Takriti
6418417	July 2002	Corby et al.
6584447	June 2003	Fox et al.

Foreign Patent Documents


2 751 774	Jul., 1996	FR

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Primary Examiner: Poinvil; Frantzy
Assistant Examiner: Kanof; Pedro
Attorney, Agent or Firm: Sterne, Kessler, Goldstein & Fox P.L.L.C.

Parent Case Text

CROSS-REFERENCE TO OTHER APPLICATIONS

This application is a continuation application of and claims priority to U.S. Patent now U.S. Pat. No. 5,832,456.

The following applications of common assignee are related to the present application.

"System and Method for the Advanced Prediction of Weather Impact on Managerial Planning Applications," Ser. No. 08/002,847, filed Jan. 15, 1993, now U.S. Pat. No. 5,521,813, incorporated herein by reference in its entirety.

"A User Interface For Graphically Displaying the Impact of Weather on Managerial Planning," Ser. No. 08/504,952, filed Jul. 20, 1995, now U.S. Pat. No. 5,796,932, incorporated herein by reference in its entirety.

"System and Method for Determining the Impact of Weather and Other Factors on Managerial Planning Applications," Ser. No. 08/205,494, filed Mar. 4, 1994, now U.S. Pat. No. 5,491,629, incorporated herein by reference in its entirety.

Claims

What is claimed is:

1. A computer implemented method of forecasting performance of a weather-impacted endeavor, wherein said endeavor is distributed among a plurality of units located in a plurality of geographical locations, comprising the steps of: (1) storing a history database, a weather pattern database that includes a weather pattern classification of a weather parameter, a weather history database, and a weather forecast database; (2) determining the extent to which past performance of the plurality of units at the plurality of locations was affected by weather using said history database, said weather pattern database, and said weather history database; (3) estimating expected future performance of the plurality of units at the plurality of locations for a plurality of future time periods using said weather forecast database and results produced by operation of step (2); and (4) enabling a user to view and manipulate results produced by operation of steps (2) and (3) whereby said results are used by said user to forecast future performance of the plurality of units at the plurality of locations for said time periods.

2. The method of claim 1, wherein step (2) comprises the steps of: (a) selecting a first prior year and a second prior year, said second prior year being before said first prior year; (b) selecting a location and an unit; (c) selecting a weather pattern; and (d) determining the extent to which said selected weather pattern affected performance of said selected unit in said selected location during said first prior year and said second prior year.

3. The method of claim 1, wherein step (4) comprises the step of: (a) displaying to said user predicted future performance of selected units at selected locations in view of predicted weather at said selected locations.

4. The method of claim 1, wherein step (4) comprises the step of: (a) identifying and displaying any future periods where weather patterns selected by said user are predicted to occur.

5. The method of claim 4, wherein step (a) comprises the steps of: (i) enabling a user to enter weather search criteria; (ii) enabling the user to enter one or more locations; (iii) searching through said weather history database or said weather forecast database to locate time periods where said weather search criteria are satisfied; and (iv) displaying results of said weather search means to said user.

6. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to provide business performance forecasting, said control logic comprising: first computer readable program code means for causing the computer to access a business history database, a weather pattern database that includes a weather pattern classification of a weather parameter, a weather history database and a weather forecast database; second computer readable program code means for causing the computer to determine the extent to which past business performance of a plurality of business units at a plurality of locations was affected by weather using said business history database, said weather pattern database, and said weather history database; third computer readable program code means for causing the computer to estimate expected future business performance of said business units at said locations for a plurality of future time periods using said weather forecast database and results produced by operation of said second computer readable program code means; and fourth computer readable program code means for causing the computer to enable a user to view and manipulate results produced by operation of said second and said third computer readable program code means to thereby forecast future business performance of said business units at said locations for said time periods.

7. The computer program product of claim 6, wherein said second computer readable program code means comprises: fifth computer readable program code means for causing the computer to receive a first prior year input and a second prior year input from said user, said second prior year being before said first prior year; sixth computer readable program code means for causing the computer to receive a location input and a business unit input from said user; seventh computer readable program code means for causing the computer to receive a weather pattern input from said user; and eighth computer readable program code means for causing the computer to determine the extent to which said weather pattern affected business performance of said business unit in said location during said first prior year and said second prior year.

8. The computer program product of claim 6, wherein said third computer readable program code means comprises: fifth computer readable program code means for causing the computer to receive a business unit input from said user; sixth computer readable program code means for causing the computer to receive a location input from said user; seventh computer readable program code means for causing the computer to receive a future period in a future time span input from said user; and eighth computer readable program code means for causing the computer to estimate business performance of said business unit in said location during said future period in accordance with weather predicted to occur in said location during said future period.

9. The computer program product of claim 8, wherein said fourth computer readable program code means comprises: fifth computer readable program code means for causing the computer to enable an operator to specify parameters for a customized view; and sixth computer readable program code means for causing the computer to filter said business history database, said weather history database, and said weather forecast database in accordance with said specified parameters to create said customized view.

10. The computer program product of claim 6 wherein said fourth computer readable program code means comprises: fifth computer readable program code means for causing the computer to display to said user the manner in which past weather impacted business performance of selected business units in selected locations.

11. The computer program product of claim 6, wherein said fourth computer readable program code means comprises: fifth computer readable program code means for causing the computer to display to said user predicted future business performance of selected business units at selected locations in view of predicted weather at said selected locations.

12. The computer program product of claim 6, wherein said fourth computer readable program code means comprises: fifth computer readable program code means for causing the computer to identify and display any future periods where user-specified weather patterns are predicted to occur.

13. The computer program product of claim 6, further comprising: fifth computer readable program code means for causing the computer to identify and display any sequence of time periods that match user-specified sequences of weather patterns.

14. The computer program product of claim 13, wherein said fifth computer readable program code means comprises: sixth computer readable program code means for causing the computer to enable said user to enter weather search criteria; seventh computer readable program code means for causing the computer to enable said user to enter one or more locations; eighth computer readable program code means for causing the computer to search through said weather history database or said weather forecast database to locate time periods where said weather search criteria are satisfied; and ninth computer readable program code means for causing the computer to display results of said eighth computer readable program code means to said user.

15. A system for forecasting business performance, comprising: a storage device storing business history data, weather pattern data including a weather pattern classification of a weather parameter, weather history data, and weather forecast data; an analyzer to determine the extent to which past business performance of a business unit at a plurality of locations was affected by weather using at least said business history data, said weather pattern data, and said weather history data; a configurator, coupled to said analyzer, to estimate expected future business performance of said business unit at said locations for a plurality of future time periods using at least said weather forecast data and results produced by said analyzer; and a user interface, coupled to said analyzer and said configurator, to enable a user to at least view results produced by said configurator to thereby forecast future business performance of said business unit at said locations.

16. The system of claim 15, wherein said business unit, whose past performance is represented by at least some of said business history data, is a utilities industry product.

17. The system of claim 16, wherein said utilities industry product is electricity.

18. The system of claim 15 wherein said business unit, whose past performance is represented by at least some of said business history data, is an agricultural product.

19. The system of claim 18, wherein said agricultural product is timber.

20. The system of claim 15, wherein said business unit, whose past performance is represented by at least some of said business history data, is an insurance-related product.

21. A computer implemented method of forecasting business performance, comprising the steps of: (1) accessing business history data, weather pattern data including a weather pattern classification of a weather parameter, weather history data, and weather forecast data; (2) determining the extent to which past business performance of a business unit at a plurality of locations was affected by weather using at least said business history data, said weather pattern data, and said weather history data; (3) estimating expected future business performance of said business unit at said locations for a plurality of future time periods using at least said weather forecast data and results produced by step (2); and (4) enabling a user to at least view results produced by step (3) to thereby forecast future business performance of said business unit at said locations for said time periods.

22. The method of claim 21, wherein said business unit, whose past performance is represented by at least some of said business history data, is a utilities industry product.

23. The method of claim 22, wherein said utilities industry product is electricity.

24. The method of claim 21, wherein said business unit, whose past performance is represented by at least some of said business history data, is an agricultural product.

25. The method of claim 24, wherein said agricultural product is timber.

26. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to forecast business performance, said control logic comprising: first computer readable program code means for causing the computer to access business history data, weather pattern data including a weather pattern classification of a weather parameter, weather history data, and weather forecast data; second computer readable program code means for causing the computer to determine the extent to which past business performance of a business unit at a plurality of locations was affected by weather using at least said business history data, weather pattern data, and said weather history data; third computer readable program code means for causing the computer to estimate expected future business performance of said business unit at said locations for a plurality of future time periods using at least said weather forecast data and results produced by said second computer readable program code means; and fourth computer readable program code means for causing the computer to enable a user to at least view results produced by said third computer readable program code means to thereby forecast future business performance of said business unit at said locations for said time periods.

27. The computer program product of claim 26, wherein said business unit, whose past performance is represented by at least some of said business history data, is a utilities industry product.

28. The computer program product of claim 27, wherein said utilities industry product is electricity.

29. The computer program product of claim 26, wherein said business unit, whose past performance is represented by at least some of said business history data, is an agricultural product.

30. The computer program product of claim 29, wherein said agricultural product is timber locations was affected by weather using at least said business history data, weather pattern data and said weather history data; third computer readable program code means for causing the computer to estimate expected future business performance of said business unit at said locations for a plurality of future time periods using at least said weather forecast data and results produced by said second computer readable program code means; and fourth computer readable program code means for causing the computer to enable a user to at least view results produced by said third computer readable program code means to thereby forecast future business performance of said business unit at said locations for said time periods.

31. The method of claim 1, wherein said determining of step (2) is performed by an analyzer.

32. The method of claim 1, wherein said estimating of step (3) is performed by a configurator.

33. The method of claim 4, wherein said determining of step (2) is performed by an analyzer.

34. The method of claim 4, wherein said estimating of step (3) is performed by a configurator.

Description

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to business performance forecasting, and more particularly to weather adapted, business performance forecasting.

2. Related Art

A. Historical Perspective of Retailing

The retail industry has historically been influenced by the shape of the times. For example, the retail industry is impacted by war and peace, lifestyle changes, demographic shifts, attitude progressions, economic expansion and contraction, tax policies, and currency fluctuations.

The period from 1965 to 1975 was marked by growth and segmentation in the retail industry. New types of stores such as department stores, specialty stores, and discount stores appeared, increasing competition in the retail industry. One result of this growth was a decrease in gross margin (sales price--cost of goods sold). Another result was a shifting of supply sources. Originally, merchandise was supplied exclusively by vendors. However, segmentation and growth resulted in specialty chains and discounters manufacturing merchandise in-house (commonly known as vertical integration).

The period from 1975 to 1980 was marked by disillusionment and complexity in the retail industry. Inflation and women entering the work force in significant numbers resulted in a more sophisticated consumer. Many retailers began to rethink the basics of merchandising in terms of merchandise assortments, store presentations, customer service, and store locations. Other less sophisticated retailers continued on an undisciplined and unstructured policy of store growth.

The period from 1980 to 1990 was marked by recovery and opportunity in the retail industry. An economic boom stimulated consumer confidence and demand. This, coupled with the expansion of the previous period, paved the way for the retail industry to overborrow and overbuild. With their increased size, retailers became increasingly unable to manage and analyze the information flowing into their organizations.

B. Retailing Problems and Opportunities of Today

The problems and opportunities facing the retailer fall into two categories of factors: (1) external factors; and (2) internal (or industry) factors. External factors impacting the retail industry include, for example, adverse or favorable weather, rising labor costs, increasing property costs, increased competition, economics, increasing cost of capital, increasing consumer awareness, increasing distribution costs, changing demographics and zero population growth, decreasing labor pool, and flat to diminishing per capita income.

Internal (or industry) factors affecting the retail industry include,- for example, large number of stores (decentralization), homogeneity among retailers, continuous price promotion (equates to decreased gross margin), decreasing customer loyalty, minimal customer service, physical growth limitations, and large quantities of specific retailer store information.

Growth and profitability can only be achieved by maximizing the productivity and profitability of the primary assets of the retail business: merchandise (inventory), people, and retail space. The above external and industry factors have added to a retailer's burdens of maintaining the productivity of these assets.

Of the three primary assets, merchandise productivity is particularly important due to the limiting effect of external and internal factors on people and space productivity (e.g., physical growth limitations and high labor costs). Merchandise productivity can be best achieved by maintaining effective mix of product in a store by product characteristic (merchandise assortments).

To achieve more effective merchandise assortments, a retailer must have a merchandise plan that provides the retailer with the ability to (1) define, source, acquire, and achieve specific target merchandise assortments for each individual store location; (2) achieve an efficient, non-disruptive flow from supply source to store; (3) maintain store assortments which achieve anticipated financial objectives; and (4) communicate effectively across all areas of the business to facilitate coordinated action and reaction.

Such an effective merchandise plan must consider all possible external and industry factors. To obtain this knowledge, a retailer must have responsive and easy access to the data associated with these factors, referred to as external and industry data, respectively. To assimilate and analyze this data, which comes from many sources and in many formats, retailers began utilizing management information systems (MIS). The primary function of the MIS department in the retail industry has been the electronic collection, storage, retrieval, and manipulation of store information. Mainframe-based systems were primarily utilized due to the large amount of store information generated. Store information includes any recordable event, such as purchasing, receiving, allocation, distribution, customer returns, merchandise transfers, merchandise markdowns, promotional markdowns, inventory, store traffic, and labor data. In contrast to the extensive collection and storage of internal data, these systems, did not typically process external data. Rather, this non-industry data was simply gathered and provided to the retailer for personal interpretation.

Since understanding of local and region level dynamics is a requisite for increased retailing productivity, retailers would essentially feed store information at the store level into massive mainframe databases for subsequent analysis to identify basic trends. However, the use of mainframes typically requires the expense of a large MIS department to process data requests. There is also an inherent delay from the time of a data request to the time of the actual execution. This structure prevented MIS systems from becoming cost effective for use by executives in making daily decisions, who are typically not computer specialists and thus rely on data requests to MIS specialists.

FIG. 37 illustrates a block diagram of a conventional MIS system architecture used in the retail industry. Referring to FIG. 37, an MIS architecture 3701 captures store information (one form of internal data) and electronically flows this information (data) throughout the organization for managerial planning and control purposes.

At point of sale 3704, scanners 3708 and electronic registers 3710 record transactions to create POS data 3706. These transactions include data related to customer purchases, customer returns, merchandise transfers, merchandise markdowns, promotional markdowns, etc. POS data 3706 is one form of store information 3716. Store information 3716 also includes other store data 3712. Other store data 3712 includes data related to receiving, allocation, distribution, inventory, store traffic, labor, etc. Other store data 3712 is generally generated by other in-store systems.

Store information 3716 is polled (electronically transferred) from point of sale 3704 by headquarters, typically by modem or leased-line means 3717. POS 3704 represents one typical location (retail store). However, MIS architecture 3701 can support multiple POS locations 3704.

A data storage and retrieval facility 3720 receives store information 3716 using computer hardware 3722 and software 3724. Data storage and retrieval facility 3720 stores store information 3716. Store information 3716 is retrieved into data analyzer 3727. Data analyzer 3727 shapes and analyzes store information 3716 under the command of a user to produce data, in the form of reports, for use in the preparation of a managerial plan 3730.

In the 1970's and 1980's, retrieval of store information 3716 into data analyzer 3727 and the subsequent report generation were manually or electronically generated through a custom request to MIS department personnel. More recently, in response to the need for a rapid executive interface to data for managerial plan preparation, a large industry developed in Executive Information Systems (EIS). Referring to FIG. 37, an EIS 3729, which typically operates on a personal computer workstation platform, interfaces with the MIS mainframe or mid-range database in data storage and retrieval facility 3720. An EIS system is a computer-based system by which information and analysis can be accessed, created, packaged and/or delivered for use on demand by users who are non-technical in background. Also, EIS systems perform specific managerial applications without extensive interaction with the user, which reduces or eliminates the need for computer software training and documentation.

In contrast to store information 3716, external information 3736 consists of manual reports covering such topics as economic forecasts, demographic changes, and competitive analysis. In conventional systems, external information 3716 is separately made available to the user for consideration in developing managerial plan 3730.

Technical improvements in speed and storage capability of personal computers (PCS) have allowed this trend towards EIS systems to take place, while most firms still maintain a mainframe or minicomputer architecture for basic POS data storage and processing. The advent of powerful mini computers, local area networks (LANs), and PC systems has resulted in many of the traditional mainframe retailing applications migrating to these new platforms.

C. The Nature of Weather Anomalies

Weather anomalies are more of a regional and local event rather than a national phenomenon in countries as geographically large as the United States. This is not to say that very anomalous weather cannot affect an entire country or continent, creating, for example, abnormally hot or cold seasons. However, these events are less frequent than regional or local aberrations. Significant precipitation and temperature deviations from normal events occur continually at time intervals in specific regions and locations throughout the United States.

Because actual daily occurrences fluctuate around the long term "normal" or "average" trend line (in meteorology, normal is typically based on a 30 year average), past historical averages can be a very poor predictor of future weather on a given day and time at any specific location. Implicitly, weather effects are already embedded in an MIS POS database, so the retailer is consciously or unconsciously using some type of historical weather as a factor in any planning approach that uses trendline forecasts based on historical POS data for a given location and time period.

D. Weather Relative to National Planning Applications

At a national level, weather is only one of several important variables driving consumer demand for a retailer's products. Several other factors are, for example, price, competition, quality, advertising exposure, and the structure of the retailer's operations (number of stores, square footage, locations, etc). Relative to the national and regional implementation of planning, the impact of all of these variables dominates trendline projections.

As described above, POS databases track sales trends of specific categories at specific locations which are then aggregated and manipulated into regional and national executive information reports. Since the impact of local weather anomalies can be diluted when aggregated to the national levels (sharp local sales fluctuations due to weather tend to average out when aggregated into national numbers), the impact of weather has not received much scrutiny relative to national planning and forecasting.

E. Weather Relative to Regional and Local Planning Applications

The impact of weather on a regional and local level is direct and dramatic. At the store level, weather is often a key driver of sales of specific product categories. Weather also influences store traffic which, in turn, often impacts sales of all goods. Weather can influence the timing and intensity of markdowns, and can create stockout situations which replenishment cycles can not address due to the inherent time lag of many replenishment approaches.

The combination of lost sales due to stockouts and markdowns required to move slow inventory are enormous hidden costs, both in terms of lost income and opportunity costs. Aggregate these costs on a national level, and weather is one of the last major areas of retailing where costs can be carved out (eliminate overstocks) and stores can improve productivity (less markdown allows for more margin within the same square footage).

In short, weather can create windows of opportunity or potential pitfalls that are completely independent events relative to economics, demographics, consumer income, and competitive issues (price, quality). The cash and opportunity costs in the aggregate are enormous.

F. Conventional Approaches Addressing Weather Impact

Though the majority of retailers acknowledge the effects of weather, many do not consider weather as a problem per se, considering it as a completely uncontrollable part of the external environment.

However, the underlying problem is essentially one of prediction of the future; i.e., developing a predictive model. All retailers must forecast (informally or formally) how much inventory to buy and distribute based on expected demand and appropriate inventory buffers. Hence, many conventional predictive modeling processes have been developed, none of which adequately address the impact of weather.

One conventional solution is to purposely not consider the impact of weather on retail sales. In such instances, the retailer will maintain high inventory levels and rapidly replenish the inventory as it is sold. This approach creates large working capital needs to support such a large inventory.

Another conventional solution is for the retailer to qualitatively use weather information to anticipate future demands. This procedure, if used by decision makers, is very subjective and does not evaluate weather in a predictive sense. Nor does it quantify the effect of past and future weather on consumer demands.

Another conventional approach is the utilization of climatology. Climatology is the study of the climates found on the earth. Climatology synthesizes weather elements (temperature, precipitation, wind, etc.) over a long period of time (years), resulting in characteristic weather patterns for a given area for a given time frame (weekly, monthly, seasonably, etc.). This approach does not utilize forecasted weather as a parameter, which can vary considerably from any given time period from year to year for a given area. Climatology yields only the average weather condition, and is not indicative of the weather for any specific future time frame.

Manufacturers and retailers have been known to rely on broad projections developed by the National Weather Service (the governmental entity in the USA charged with disseminating weather data to the public) and other private forecasting firms. With reference to long range projections, these may be vague, broad, and lack regional or local specificity. It is of limited use since they are issued to cover anticipated weather averaged for 30, 60, or 90 day periods covering large geographic areas. This information cannot be quantified or easily integrated into an MIS-based planning system which is geared toward a daily or weekly time increment for specific location and time.

In summary, the above conventional solutions to weather planning problems in retail all suffer from one or several deficiencies which severely limit their commercial value, by not providing: (1) regional and/or local specificity in measuring past weather impact and projecting future weather impact, (2) the daily, weekly, and monthly increment of planning and forecasting required in the retail industry, (3) ample forecast leadtime required by such planning applications as buying, advertising, promotion, distribution, financial budgeting, labor scheduling, and store traffic analysis, (4) the quantification of weather impact required for precise planning applications such as unit buying and unit distribution, financial budget forecasting, and labor scheduling, (5) reliability beyond a 3 to 5 day leadtime, (6) a predictive weather impact model, which links quantitative weather impact measurement through historical correlation, with quantitative forecasts, (7) the ability to remove historical weather effects from past retail sales for use as a baseline in sales forecasting, (8) an entirely electronic, computerized, EIS implementation for ease of data retrieval/analysis with specific functions that solve specific managerial planning applications, and (9) a graphical user interface representing a predictive model in graphs, formats, and charts immediately useful to the specific managerial applications.

G. Scope of the Problem

The above discussion focused on the retail industry, i.e., the impact of weather on the retail industry. Naturally, the effects of weather are not confined to the retail industry. Instead, weather impacts all aspects of human endeavor. Accordingly, the discussion above applies equally well to many other applications, including but not limited to retail products and services, manufacturing/production (i.e., construction, utilities, movie production companies, advertising agencies, forestry, mining), transportation, the entertainment industry, the restaurant industry, etc.

SUMMARY OF THE INVENTION

The present invention is directed to a system and method for forecasting future retail performance. The system includes a storage device that stores a sales history database, a weather history database, and a weather forecast database. An analyzer determines the extent to which past retail performance of a plurality of products at a plurality of locations was affected by weather using the sales history database and the weather history database. A configurator, coupled to the analyzer, estimates expected future retail performance of the products at the stores for a plurality of future time periods using the weather forecast database and results produced by the analyzer. A graphical user interface, coupled to the analyzer and the configurator, enables users to view and manipulate results produced by the analyzer and the configurator to thereby forecast future retail performance of the products at the locations.

Further features and advantages of the invention, as well as the structure and operation of various embodiments of the invention, are described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference number.

BRIEF DESCRIPTION OF THE FIGURES

The present invention will be described with reference to the accompanying drawings, wherein:

FIG. 1 is a block diagram of a weather adapted, retail performance forecasting system according to a preferred embodiment of the present invention;

FIG. 2 is a block diagram of a computer system preferably used to implement the present invention;

FIG. 3 is a dataflow diagram of an analyzer and a configurator of the forecasting system;

FIG. 4 is a dataflow diagram of a graphical user interface of the forecasting system;

FIG. 5 is a dataflow diagram of administration setup of the forecasting system;

FIGS. 6, 13A, 13B, 14, 15, 16, 17, 18, 19, 20, and 21 are flowcharts depicting the preferred operation and control flow of the present invention;

FIGS. 7, 8, 9, 10, 11, 12, 13C, and 22 depict preferred databases used by the present invention; FIGS. 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 36A, 36B, 38, 39, 40, 41, and 42 are windows or screen shots generated by the graphical user interface of the present invention; and

FIG. 37 is a block diagram of a conventional MIS system architecture used in the retail industry.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Overview of the Invention

The present invention is directed to a system and method for retail performance forecasting. As used herein, the term "retail performance" refers to all statistical metrics related to retail sales performance, such as gross revenue, net revenue, unit sales, customer traffic, etc. For convenience, the present invention is described herein in the context of a retail environment. However, it should be understood that the invention is adapted and envisioned for use at any commercial level, such as manufacturing, distribution, value added reselling, etc., in addition to retail. Moreover, the present invention is well suited and adapted for use with any endeavor and/or industry and/or market that is potentially or actually impacted by weather. This includes, but is not limited to, retail products and services, manufacturing/production (i.e., construction, utilities, movie production companies, advertising agencies, forestry, mining), transportation, the entertainment industry, the restaurant industry, etc.

The present invention is "weather adapted." In other words, the present invention when forecasting retail performance takes the affect of weather into consideration. For example, suppose the invention is used to forecast the opportunity of the snow sled market for next January. In performing this forecasting function, the present invention will take into consideration the weather predictions for next January (whether snow will be below seasonal, seasonal, or above seasonal, for example). Because it takes weather into consideration, the present invention is generally more accurate than similar systems and methods that do not take the affect of weather into consideration.

FIG. 1 is a block diagram of a weather adapted, retail performance forecasting system 102 (also called system herein) according to a preferred embodiment of the present invention. The system 102 includes a Administrator Setup 104, an analyzer 106, a decision support engine 108 (also called configurator herein), and a graphical user interface (GUI) 110.

FIG. 21 presents a flowchart 2102 depicting the high-level operation of these components. In flowchart 2102, steps 2118 are performed by the Administrator Setup 104 in accordance with commands from a system administrator 502 (FIG. 5). Steps 2120 are performed by the analyzer 106, configurator 108, and GUI 110 in accordance with commands from an user 402 (FIG. 4). The system administrator 502 and user 402 may or may not be the same person. Flowchart 2102 shall now be described, with reference to FIG. 5 (a data flow diagram of the Administrator Setup 104), FIG. 3 (a data flow diagram of the analyzer 106 and configurator 108), and FIG. 4 (a data flow diagram of the GUI 110). Flowchart 2102 begins with step 2104, where control immediately passes to step 2106.

In step 2106, the Administrator Setup 104 and the GUI 110 enable the system administrator 502 to define a customized view per a party's specifications. Step 2106 shall now be described in detail with reference to FIG. 5.

The present invention makes use of weather and sales data 304, 505. The weather data includes seasonal weather data (for example, the average temperature in June), historical weather data (for example, the temperature last June), and forecast weather data (for example, a prediction as. to what the temperature will be next June). The seasonal weather data , for example, seasonal temperature, represents a narrow range of temperatures, which is implementation dependent, for a specific location and period. It is based upon the 40% of occurrences centered around the mean temperature during a 30 year period. The historical weather data preferably represents a library of historical weather data covering two to five years, although other time spans are alternatively possible and envisioned by the present invention. The forecast weather data represents weather predictions for preferably fifteen months although other time spans are alternatively possible and envisioned by the present invention. Databases of seasonal weather data and historical weather data are available from many publicly and/or commercially available publications and services. The forecast weather data is commercially available from Strategic Weather Services of Wayne, Pennsylvania. Other forms of forecast weather data are available from other commercial sources, but these other forms cannot be used directly with the present invention. Instead, they must be modified so as to be consistent with the form and substance discussed herein.

The Weather Database 505 comprises Weather History 306, Weather Patterns 308 and Weather Forecasts 312. The Sales Database 304 represents a party's historical sales data. The party is an entity (such as a chain of retail stores) who wishes to use the forecasting system 102 to predict future retail performance. (The user 402 mentioned above is preferably an employee of the party.) The sales history database 304 preferably includes historical sales data pertaining to all products offered for sale at all of the party's commercial outlets. Typically, the sales data includes the past year's historical sales data, but may include other time spans.

As one will appreciated, Weather Database 505 and Sales history database 304 are potentially very large. Also, much of the information contained in the weather database 505 and sales database 304 may not be pertinent to needs of the party. For example, the party may be only interested in the past two years' historical weather data. Also, the party may only be interested in analyzing the performance of a subset of its commercial outlets, and/or a subset of the products that it offers. Accordingly, the present invention allows the party to filter the data from both the weather database 505 and the sales history database 304, and supply only the data desired to the Analyzer 106 from both databases by the System Administrator 502. The data filtering process is performed by the Administrator Setup 104.

Accordingly, in step 2106 the system administrator 502 interacts with the Administrator Setup 104 via the GUI 110 to customize a view (i.e., to establish the parameters of the impending view). The system administrator 502 customizes the view in accordance with specifications previously provided to the party. For example, the party may have specified to include only Stores A, B, and E in the view, and/or to include only performance data relating to shoes and shirts in the view, and/or to include only forecast weather data for January through May of next year. Step 2106 is described in greater detail below.

In step 2108, the Administrator Setup 104 creates a view in accordance with the party's specifications. In essence, the Administrator Setup 104 in step 2108 extracts from the weather database 505 and sales history database 304 the weather and sales information needed to satisfy the party's specifications as defined in step 2106 (for example, if the party specified shoes in step 2106, then retail performance data relating to shoes is extracted from the sales database 506 in step 2108). The Administrator Setup 104 stores the extracted data in an analyzer input database 302 and a weather forecast database 312.

The analyzer input database 302 is used in the Analyzer 106, the weather forecast database 312 is used in conjunction with the analyzer output database 310 in the configurator 108. The analyzer output database 310 and the weather forecast database 312 are utilized by the party to forecast future retail performance.

In step 2112, the analyzer 106 at each client site receives the analyzer input database 302 (see FIG. 3). The analyzer input database 302 includes a sales history database 304, a weather history database 306, and a weather patterns database 308. These databases 304, 306, and 308 are described below. The analyzer 106 analyzes the analyzer input database 302, and produces an analyzer output database 310. Generally speaking, the analyzer output database 310 includes data that indicates the manner in which the retail performance of each product at each store was affected by weather. The operation of the analyzer 106 is discussed in greater detail below.

Also in step 2112, the configurator 108 analyzes the analyzer output database 310 and the weather forecast database 312 and produces a configurator output database 314. Generally speaking, the configurator output database 314 includes data that indicates the expected future retail performance of each product at each store. In generating the configurator output database 314, the configurator 108 takes into consideration the affects of predicted future weather. The operation of the configurator 108 is discussed in greater detail below.

In step 2114, the GUI 110 enables users 402 at each client site (who are typically employed by the party) to extract and analyze in meaningful ways information from the analyzer output database 310, the weather forecast database 312, and the configurator output database 314. The operation of the GUI 110 is discussed in greater detail below.

Flowchart 2102 is complete after step 2114 has been performed, as indicated by step 2116.

Preferred Implementation of the Present Invention

In one embodiment, the invention is directed to a computer system operating as discussed herein. Specifically, the forecasting system 102 could be implemented using a computer system 202 as shown in FIG. 2. Typically, a computer system 202 implementing the forecasting system 102 of the present invention would be located at the view site and at each of the client sites.

The computer system 202 includes one or more processors, such as processor 204. The processor 204 is connected to a communication bus 206.

The computer system 202 also includes a main memory 208, preferably random access memory (RAM). Control logic 210 (i.e., software) and data 212 (such as the analyzer input database 302, the analyzer output database 310, the weather forecast database 312, and the configurator output database 314) are stored in the main memory 208, and may also be stored in secondary storage 214.

The computer system 202 also includes secondary storage 214. The secondary storage 214 includes, for example, a hard disk drive 216 and/or a removable storage drive 218, representing a floppy disk drive, a magnetic tape drive, a compact disk drive, etc. The removable storage drive 218 reads from and/or writes to a removable storage unit 220 in a well known manner.

Removable storage unit 220, also called a program storage device or a computer program product, represents a floppy disk, magnetic tape, compact disk, etc. As will be appreciated, the removable storage unit 220 includes a computer usable storage medium having stored therein computer software and/or data.

Computer programs (also called computer control logic) are stored in main memory 208 and/or the secondary storage 220. Such computer programs, when executed, enable the computer system 202 to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable the processor 204 to perform the features of the present invention. Accordingly, such computer programs represent controllers of the computer system 202.

In another embodiment, the invention is directed to a computer program product comprising a computer readable medium having control logic (computer software) stored therein. The control logic, when executed by the processor 204, causes the processor 204 to perform the functions of the invention as described herein.

In another embodiment, the invention is implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

The computer system 202 also includes input devices 222, such as a keyboard and/or a mouse, and display devices 224, such as a computer monitor.

Analyzer Input Database

The analyzer input database 302 includes a sales history database 304, a weather history database 306, and a weather patterns database 308. These are described below.

Sales History Database

An example sales history database 304 (alternatively called the product history database) is shown in FIG. 7. The sales history database 304 includes, for each year in the view, one or more records (or rows) for each product sold in each store. For any given product/store combination, there is a record for each of several data types. The data types represent performance metrics that are being tracked, such as gross revenue, net revenue, number of items sold, etc.

It should be understood that the invention accommodates and supports any business and/or product related data. In addition to that mentioned above, such product data (that is stored in the sales history database 304) includes retail POS data, shipment data (manufacturing plant, wholesales, etc.), inventory data, store traffic data, economic data, demographic data, order data, etc.

Each record also includes historical performance information for the product/store combination on a per period basis.

For example, the records shown in the example sales history database 304 in FIG. 7 pertain to HATS (i.e., the product) sold in Store001 (i.e., the store or location). Records 702 and 706 pertain to historical information for 1994, and records 704 and 708 pertain to historical information for 1995. The data type for record 702 is "Net Dollars," which represents net sales revenue. Record 702 includes net sales revenue information for HATS sold in Store001 in 1994. This net sales revenue information is provided on a per period basis. A period may be any increment of time, such as a day, a month, a quarter, etc. For convenience purposes, only six periods P1-P6 are shown in FIG. 7. There may be more or less periods, depending on the actual implementation.

The information contained in the sales history database 304 depends on the party's specifications that were provided to the Administrator Setup 104 during the view process. For example, the party may have indicated that it only wanted data on hats and shoes. In this case, no data would be contained in the sales history database 304 about any other product but hats and shoes (for clarity, the sales history database 304 in the example of FIG. 7 only has entries on hats). Alternatively, the party may have indicated that it was only interested in the net sales revenue and the number of items sold (as shown in FIG. 7). Alternatively, the party may have indicated that it wanted to analyze data on a bi-weekly basis (in which case the periods would each correspond to a two week period).

Weather History Database

An example weather history database 306 is shown in FIG. 8. The weather history database 306 includes, for each year in the view, one or more records for each metropolitan area (MA). (The term MA closely resembles the well known name Metropolitan Statistical Area (MSA). However MA encompasses a larger surrounding geographical area/region than the strict MSA definition.) (However, since MA and MSA are similar, they are used interchangeably herein.) The Weather History database contains but is not limited to data on 309 metropolitan areas. These records contain information specifying the weather that occurred in the subject MA in the time span represented in the view. Specifically, for each MA, there is a record for each of several weather data types.

The are three classes of weather data types as it relates to the weather history database: seasonal, actual, and category (also called weather pattern). A seasonal data type is the seasonal (or average) value of a weather parameter. Accordingly, the data type "temp.sea" is the average temperature. The data type "snow.sea" is the average snowfall. The data type "prec.sea" is the average precipitation.

An actual data type is the actual value of a weather parameter. Accordingly, the data type "temp" is the actual temperature. The data type "snow" is the actual snowfall. The data type "prec" is the actual precipitation.

A category data type reflects a weather parameter's actual versus seasonal values. Accordingly, the data type "temp.cat" reflects actual temperature versus seasonal temperature. The data type "prec.cat" reflects actual precipitation versus seasonal precipitation. If a category data type is equal to 1, then the actual value was greater than the seasonal value. If a category data type is equal to 0, then the actual value was equal to (or substantially corresponded to) the seasonal value. If a category data type is equal to -1, then the actual value was less than the seasonal value. These relationships are summarized in the weather pattern legend 1102 presented in FIG. 11. Of course, values other than 1, 0, and -1 could be alternatively used to indicate these relationships.

The historical weather information in the weather history database 306 is provided on a per period basis. As indicated above, the period may be any increment of time, such as daily, weekly, bi-weekly, monthly, bi-monthly, quarterly, etc. Preferably, the increment of time represented by a period is the same in all of the databases.

The Administrator Setup 104 process determines the information that is stored in the analyzer input database 302. For example, the length of the period is specified during the Administrator Setup 104 process. Also, the years and the locations (i.e., MAs) to be represented in the weather history database 306 are specified in the Administrator Setup process. As noted abbve, the Administrator Setup process is customized by the system administrator 502 in accordance with the party's specifications. Additional information on the Administrator Setup 104 can be found below.

Note that the weather history database 306 is on a per MA basis, whereas the sales history database 304 is on a per store basis. Typically, a plurality of stores are located in each MA. The forecasting system 102 maintains a store/MA table 1002 (FIG. 10) that provides a mapping between stores and MAs. The information contained in the store/MA table 1002 is preferably provided by the party (i.e., the entity that owns and/or manages the stores).

Weather Patterns Database

The present invention makes use of a number of different weather patterns to characterize the weather that occurred during any given past period, or that is predicted to occur during any given future period. Preferred weather patterns are presented in FIG. 9. As indicated in FIG. 9, exemplary weather patterns employed by the present invention include temperature/precipitation, temperature/snow, sustained weather, temperature/precipitation lag 1 period, and temperature/snow lag 1 period. The present invention is not limited to these weather patterns, for example patterns also include temperature/precipitation/snow combinations.

The temperature/precipitation and temperature/snow weather patterns are self-explanatory.

The sustained weather pattern represents contiguous weeks (or other periods) of similar weather, for example having "temperature sustained two weeks" as a pattern.

The "temperature seasonal /precipitation seasonal lag 1 period" pattern represents the occurrence of "temperature seasonal /precipitation seasonal" in the previous week.

The temperature seasonal/snow seasonal lag 1 period is similar to the above.

As indicated by the above list, each weather pattern includes one or more weather parameters. For example, the tempera