DYNAMIC BUSINESS ENHANCEMENT SYSTEM

• US 20100138264A1
• Filed: 06/09/2005
• Published: 06/03/2010
• Est. Priority Date: 06/09/2004
• Status: Abandoned Application

• Current Assignee: Total Communications Limited
• Original Assignee: Total Communications Limited
• Inventors: Li, Richard, Faris, Andrew Bruce
• Application Number:
  US11/570,293
• Publication Number:
  US 20100138264A1
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A computer enabled business system is disclosed which provides a business with the ability to be aware on a moment-to-moment basis of their historic, current and future operational states. The business system uses a dynamic data engine for the purposes of creating and displaying historic transactions, current stock levels and forecasted demand data in real-time. As the data is created and cast forward, the data retains attributes of the original transaction data. These attributes are configured and modified dynamically resulting in precise and managed demand forecast, budget and purchasing information. Any change in the raw data as a result of a business transaction is immediately reflected in the demand forecast; hence, the data is in a perpetual dynamic state.

Claims

1. A dynamic data engine used to forecast future demand of product units in real-time comprising:

means for storing unit sales data describing sales of said product units over multiple seasons,

means for receiving unit sales data and continuously updating said stored unit sales data in real-time,

means for appending a forecast pattern code to a new product units product code which is substantially equivalent to a previous season'"'"'s product code where said new product unit is substantially equivalent to the product unit represented by said previous season'"'"'s product code,

an algorithm for calculating forecast unit sales data for future unit sales based on the historical record of unit sales in said means for storing unit sales, wherein each product for each season is uniquely represented by a multi-field product code which provides an historical reference when undertaking analysis or forecasting for future unit sales, at least one field of said multi-field product code being hierarchical, and

means for displaying sales forecasts in a plurality of views.

2. A dynamic data engine according to claim 1 wherein each view is produced in response to input query data in one or more fields of said multi-field product code.

3. A dynamic data engine according to claim 1 wherein said multi-field product code comprises a plurality of fields grouped to form a continuous alphanumeric string.

4. A dynamic data engine according to claim 3 wherein said fields contain information on a product including department, category, sub-category, gender, season and model which make up a “

General Code”

product sub-set.

5. A dynamic data engine according to claim 4 wherein said fields contain additional information on said product including a colour/option, a size and at least a manufacturer product code which make up a “

Variant”

product sub-set.

6. A dynamic data engine according to claim 5 wherein said forecast pattern code comprises said forecast unit sales data for at least one previous season'"'"'s product code.

7. A dynamic data engine according to claim 4 wherein a new season product code is created using an existing product code but incrementing said season field.

8. A dynamic data engine according to claim 1 wherein a product lineage is generated for a new product unit using a record of actual unit sales data for at least one previous product code representing a product unit which is substantially equivalent to said new product unit.

9. A dynamic data engine according to claim 8 wherein said lineage is generated by an allocation engine for a new product code for said new product item.

10. A dynamic data engine according to claim 9 wherein said allocation engine uses said lineage and a record of current forecast data for said product code to generate a lineage for said new product code.

11. A dynamic data engine according to claim 10 wherein said allocation engine generates a product lineage for at least one product code for a customer.

12. A dynamic data engine according to claim 10 wherein said product lineage is updated automatically and in real-time on receiving actual sales data.

13. A dynamic data engine according to claim 11 wherein said customer is allocated a customer identifying code.

14. A dynamic data engine according to claim 13 wherein said customer identifying code is substantially similar to said forecast pattern code enabling said dynamic data engine to generate a baseline forecast and a budget data for sales for said customer.

15. A dynamic data engine according to claim 1 wherein said means for receiving unit sales data is achieved via an electronic interface to at least one customer database system.

16. A dynamic data engine according to claim 15 wherein said electronic interface acts via the Internet.

17. A dynamic data engine according to claim 15 wherein said electronic interface is acts a Wide Area Network connection.

18. A dynamic data engine according to claim 15 wherein said electronic interface is acts a Local Area Network connection.

19. A dynamic data engine according to claim 15 wherein said electronic interface is acts a Wireless Network connection.

20. A dynamic data engine according to claim 1 wherein said algorithm for calculating forecast unit sales data future unit sales is configured to apply a forecast factor, a historic forecast pattern and an averaged unit selling price.

21. A dynamic data engine according to claim 20 wherein said algorithms is configured to provide data for use in a budgeting and a purchasing process model.

22. A dynamic data engine according to claim 1 wherein said forecast unit sales data for a future version or model of a product for a same time next year is generated from a plurality of transactions in said dynamic data engine as said plurality of transactions occur thereby providing an immediate and an historic, a current and a future view of a business performance and movements of stock items.

23. A dynamic data engine according to claim 22 wherein said forecast data is at a variant level and displayed on said display means at a company, department or customer level.

24. A dynamic data engine according to claim 22 wherein said forecast unit sales data is editable by a user such that said forecast unit sales data becomes a budget of future unit sales at a predetermined date.

25. A dynamic data engine according to claim 24 wherein said editable forecast unit sales data at a general code level is processed by a dynamically generated allocation engine used to allocate a plurality of edited quantities across a plurality of products based on a dynamically generated percentage split calculated from a current variable percentage.

26. A dynamic data engine according to claim 22 wherein using said forecast data from a previous season product or a plurality of previous season'"'"'s product historic group data has a plurality of calculations performed on said forecast data to create a percentage split which can be used to configure said allocation engine for a forecasting event.

27. A dynamic data engine according to claim 22 wherein said forecast data is capable of being used to facilitate a purchasing process.

28. A dynamic data engine according claim 22 wherein said forecast data is capable of being adjusted by a forecast factor and used to provide a minimum order quantity.

29. A dynamic data engine according to claim 22 wherein said forecast data is capable of being specific to a sales entity.

30. A dynamic data engine according to claim 22 wherein said forecast data is capable of being specific to a sales entity'"'"'s customers.

31. A dynamic data engine according to claim 22 wherein said forecast data is capable of being specified for use as a proposal for said sale'"'"'s entity'"'"'s customer.

32. A dynamic data engine according to claim 26 wherein said historic group data is capable of being filtered to select a user specified range for said percentage split calculation.

33. A demand driven supply chain management system including the dynamic data engine of claim 1 to forecast stock demand for a given customer, and further comprising:

a means for modulating in real-time a flow of goods through said supply chain to meet a stock demand, and

a means for determining a plurality of product stock levels across said supply chain.

34. A demand driven supply chain management system according to claim 33 wherein said means for modulating in real-time a flow of goods comprises means for re-calculating a forecast of sales of said goods based on an actual sales value deviation.

35. A demand driven supply chain management system according to claim 33 wherein said modulated flow of goods provides a means of optimising said stock levels across said supply chain.

36. A demand driven supply chain management system according to claim 33 wherein said forecast stock demand is based on a demand history for said customer.

37. A demand driven supply chain management system according to claim 33 wherein said forecast stock demand can be modified by a user as a result of said user receiving a plurality of real-time point-of-sale transaction data.

38. A demand driven supply chain management system according to claim 33 wherein said means for determining a plurality of product stock levels across said supply chain monitors a customer'"'"'s stock levels in a store.

39. A demand driven supply chain management system according to claim 38 wherein monitoring of said customer'"'"'s stock levels is achieved by receiving in real-time said stock levels from a customer stock receipting system and a customer point-of-sale transaction system.

40. A demand driven supply chain management system according to claim 38 wherein said dynamic data engine is capable of recommending a maximum and a minimum stock level for said customer within said supply chain by monitoring in real-time said customer stock levels thereby enabling said forecast demand to be met.

41. A method of forecasting product requirements using the dynamic data engine of claim 1 comprising the steps of:

preparing and inputting baseline forecast data for at least one customer,

generating forecasting data collection for each product to be allocated to said at least one customer,

receiving a history of sales data from said customer in real-time via an electronic interface once a sales transaction occurs,

generating a sales data collection from an allocation engine,

modifying customer budget data based on said sales transactions,

generating new forecasting data collection for said customer to replace said baseline forecast data, and

using said new forecast data collection as an operational forecast processing model.

42. A method of forecasting product requirements according to claim 41 wherein said step of generating said forecasting data collection includes gathering and collating a plurality of stock items within a user specified data range within a product database.

43. A method of forecasting product requirements according to claim 42 wherein said step of generating said forecasting data collection is used to create a forecasting general code processing model and a forecasting detail processing model which has a previous forecast pattern, a forecast factor and a pricing scheme set.

44. A method of forecasting product requirements according to claim 41 wherein said step of receiving a history of sales data in real-time is achieved by receiving said sales transactions from an online point-of-sales transaction system.

45. A method of forecasting product requirements according to claim 41 wherein said step of receiving a history of sales data is obtained from a database containing a plurality of sales data from at least one previous financial year and wherein said sales data is used to calculate a budget for a current financial year.

46. A method of forecasting product requirements according to claim 41 wherein said step of generating said sales data collection is obtained from at least one database which stores a plurality of future confirmed sales data and a plurality of reserved sales data.

47. A method of forecasting product requirements according to claim 41 wherein said step of modifying said customer budget data calculated when said real-time sales transaction data is collated with said historic sales data in order to adjust said customer budget data for a current financial year.

48. A method of forecasting product requirements according to claim 47 wherein said step of modifying said customer budget data enables said historic sales data which is adjusted by said real-time sales transaction data to provide a plurality of forecasting data for a plurality of future months.

49-51. -51. (canceled)