Method and apparatus for monitoring the flow of items through a store or warehouse

• US 7,240,027 B2
• Filed: 04/06/2001
• Issued: 07/03/2007
• Est. Priority Date: 04/07/2000
• Status: Expired due to Term

• Current Assignee: Dunnhumby Limited (Nabors Industries Limited)
• Original Assignee: Standard Analytics, LLC (Nabors Industries Limited), The Procter & Gamble Company
• Inventors: McConnell, Theodore Van Fossen, Vaccaro, Henry Sebastian
• Primary Examiner(s): Kramer; James
• Assistant Examiner(s): Khattar; Rajesh
• Application Number:
  US09/827,811
• Publication Number:
  US 20010049690A1
• Time in Patent Office: 2,279 Days
• Field of Search: 705/28, 705/29, 705/7, 705/8
• US Class Current: 705/28
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 20/20 Point-of-sale [POS] network systems
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G07F 9/002 Vending machines being part of a centrally controlled network of vending machines
• CPC Class Codes: G07F 9/026 for alarm, monitoring and auditing in vending machines or means for indication, e.g. when empty
• CPC Class Codes: G07G 1/0054 with control of supplementary check-parameters, e.g. weight or number of articles
• CPC Class Codes: G07G 1/14 Systems including one or more distant stations co-operating with a central processing unit data transmission in general H04L; telemetry systems for selectively calling a substation from a main station H04Q9/00
• CPC Class Codes: G07G 3/00 Alarm indicators, e.g. bells
• CPC Class Codes: G07G 3/003 Anti-theft control theft alarms G08B13/00

Abstract

An item velocity monitoring system is provided which interfaces with a consumer retail store that has several cash registers that are tied into a “point of sale” store controller. The item velocity monitoring system is capable of detecting when sales (or other movement activities) of an item are occurring too quickly, or too slowly. The item velocity monitoring system is first “trained” in a learning mode of operations, during which item patterns and group patterns are evaluated and placed into a pattern database. The system then compares the observed item velocity to its model probability velocity, and if the observed item velocity deviates beyond the statistical model, a “velocity event” is generated, declaring one of the above selling “too quick” or “too slow” conditions. Once a velocity event is detected, an event handling routine displays the event, and can transmit the event information over a network (including the INTERNET) to a remote computer for additional analysis or record keeping. A “Loyalty Out-of-Stock System,” (LOSS) is incorporated in the above item velocity monitoring system which automatically detects when items for sale are out-of-stock (OOS), discovers the reasons for these “stock-outs,” and determines how customers react to these stock-outs. The LOSS operates on store data and models the expected item movement rate for each item under varying time-of-day, day-of-week, price, promotion, season, holiday, and market conditions; detects items that are moving abnormally slowly, thereby identifying items that may be improperly displayed; provides early warning that an item may go out-of-stock (OOS) by detecting items with abnormally high movement; detects and reports on items that are OOS at retail stores; summarizes OOS events for the store and retail chain management, and for suppliers, thereby identifying items that are over-stocked (too few OOS events), under-stocked (too many events), badly re-stocked (too long events); analyzes the OOS events to find patterns that explain why OOS'"'"'s are occurring; and determines the impacts of these OOS events on store customers, thereby measuring losses to the retailer and supplier, and establishing the loyalty of consumers to the item, brand, and chain.

Claims

1. A method of using a computer processor to monitor items being received and disbursed within a predetermined environment, said method comprising:

(a) providing a computer monitoring system having a memory circuit for storage of data, a communications port, and a processing circuit;

(b) providing a plurality of sensing circuits that detect at least one item as it is moved to predetermined locations within said predetermined environment;

(c) receiving, by way of said communications port, identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits, and receiving an arrival time, wherein said arrival time is a time when said at least one item was detected by the one of said plurality of sensing circuits;

(d) determining at a later time, by way of said processing circuit, a waiting time for said at least one item, wherein said waiting time is an amount of time between said arrival time and said later time, wherein said later time is prior to a next time said at least one item is again detected by one of said plurality of sensing circuits;

(e) retrieving, from said memory circuit, a probability distribution over inter-arrival times for said at least one item, wherein an inter-arrival time is an amount of time between an arrival of said at least one item and a next arrival of said at least one item, and wherein said probability distribution is retrieved, by way of said processing circuit, based on said identification characteristic information; and

(f) determining, by way of said processing circuit, based on said retrieved probability distribution, that said waiting time is anomalous if a cumulative probability of all inter-arrival times that are greater than said waiting time is less than a predetermined threshold; and

(g) generating at said later time, by way of said processing circuit, an inter-arrival time event announcement for said at least one item whenever said waiting time is anomalous.

2. The method as recited in claim 1, further comprising updating said probability distribution over inter-arrival times for said at least one item after it passes one of said plurality of sensing circuits.

3. The method as recited in claim 1, further comprising storing said probability distribution in said memory circuit, wherein the step of storing said probability distribution in said memory circuit comprises:

creating or modifying an entry in a database that is stored in said memory circuit such that said entry can later be accessed in substantially real time with respect to the occurrence of the step of determining, by way of said processing circuit, whether said waiting time is anomalous.

4. The method as recited in claim 1, wherein said identification characteristic information comprises:

an SKU identifier of said at least one item, or a bar code from a label affixed to said at least one item.

5. The method as recited in claim 1, wherein the step of determining, by way of said processing circuit, whether said waiting time is anomalous occurs in substantially real time with respect to the occurrence of said step of determining a waiting time for said at least one item.

6. The method as recited in claim 1, wherein the step of receiving identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits occurs when said at least one item is being sold at a point-of-sale register within said predetermined environment.

7. The method as recited in claim 1, wherein the step of generating an inter-arrival time event announcement when said waiting time is anomalous is indicative of one of the following conditions:

(i) said at least one item is substantially hidden while residing in its correct location on a display shelf;

(ii) said at least one item is completely out-of-stock on said display shelf;

(iii) said at least one item has been placed at an incorrect location within said predetermined environment, or (iv) access to said at least one item has been substantially prevented by an obstruction.

8. The method as recited in claim 1, wherein said step of determining, by way of said processing circuit, whether said waiting time is anomalous comprises:

comparing the waiting time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration at least one of the following factors;

varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

9. The method as recited in claim 1, wherein said step of determining whether or not said waiting time is anomalous comprises:

comparing the waiting time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration a usage history of items being disbursed and received.

10. The method as recited in claim 1, wherein, if no probability distribution over inter-arrivals times can be retrieved based on said identification characteristic information, then said at least one item is a new item, and a new item event is generated, and a probability distribution over inter-arrival times is created for said new item.

13. The method as recited in claim 1, wherein said probability distribution comprises a modified Poisson distribution consisting of weighted sums of Poisson distributions.

14. The method as recited in claim 1, further comprising:

detecting an out-of-stock event using a probability of observing zero arrivals of said at least one item during said waiting time for said at least one item.

15. The method as recited in claim 14, wherein said out-of-stock event comprises a time interval during which said at least one item appears to be physically out-of-stock, and upon the occurrence of said out-of-stock event, the computer monitoring system summarizes said out-of-stock events, determines their causes, and measures their effect on revenues, profits, substitute items, and customer behavior.

16. The method as recited in claim 14, wherein said computer monitoring system provides forecasting of inventory or replenishment levels that removes effects of out-of-stock events before generating forecasting reports.

17. The method as recited in claim 1, wherein said probability distribution is determined by training said computer monitoring system by use of one of:

(i) historical transaction data, or (ii) transaction data that is gathered in substantially real time.

18. The method as recited in claim 17, wherein said training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table or Item Table.

19. The method as recited in claim 1, wherein all said times are redefined in terms of measured cumulative activity in said predetermined environment, and wherein said measured cumulative activity is one of (i) quantity of packaged items sold or processed, (ii) sales in monetary units, or (iii) number of different items in each transaction at a point of sale.

20. The method as recited in claim 1, wherein all said times are redefined in terms of measured cumulative activity in one of a retail store or a category of items in a retail store.

21. The method as recited in claim 1, wherein said probability distribution over inter-arrival times is a Poisson distribution with a parameter lambda that is a function of Base Lambda and Adjustment Alpha, which include information as saved data and lookup tables on:

SKU, store, and various effects, including price point, promotion, season, holiday, time-of-day, day-of-week, and market conditions.

22. The method as recited in claim 21, wherein a median is used to estimate said parameter lambda, thereby reducing bias in an estimate of a true parameter lambda arising from a contaminating effect of historical out-of-stock events.

23. The method as recited in claim 1, wherein each inter-arrival time for said at least one item and a quantity of said at least one item are linked together as a renewal-reward process, in which the quantity of said at least one item is a separate random log-normal variable with a mean beta and a beta variance.

24. The method as recited in claim 23, wherein said mean and variance parameters to the renewal-reward process are not constants, but vary during the inter-arrival time as conditions at said predetermined environment change.

25. The method as recited in claim 1, further comprising:

detecting a slow event using a probability of observing more than K arrivals of said at least one item in the time actually observed for K arrivals of said at least one item.

26. The method as recited in claim 1, further comprising:

detecting a fast event using a probability of observing less than J arrivals of said at least one item in the time actually observed for J arrivals of said at least one item.

27. The method as recited in claim 1, wherein each inter-arrival time for said at least one time varies as a function of:

total predetermined environment traffic, item category traffic, time of day, day of week, season, holidays, and market conditions of said predetermined environment.

28. The method as recited in claim 1, wherein said predetermined environment comprises one of:

a retail store, a chain of retail stores, a warehouse, a chain of warehouses, a distribution point, a chain of distribution points, manufacture'"'"'s distribution center or a chain of manufacture'"'"'s distribution centers.

29. The method as recited in claim 17, further comprising:

automatically re-training said computer monitoring system on a periodic basis using substantially real time data throughout a periodic interval.

30. The method as recited in claim 29, wherein said re-training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table.

31. The method as recited in claim 30, wherein said iterative passes comprise:

(i) computing Initial Base Lambdas using total store sales and total category sales;

(ii) computing Intermediate Base Lambdas using item transaction data and said item'"'"'s inter-arrival time using said Initial Base Lambdas;

(iii) computing Initial Adjustment Alphas using an adjusted item inter-arrival time and a plurality of current effects;

(iv) computing Final Base Lambdas using said Initial Adjustment Alphas and using said item transaction data and said item'"'"'s inter-arrival time; and

(v) computing Final Adjustment Alphas using said Final Base Lambdas and a plurality of current effects, and computing a Beta Table.

32. The method as recited in claim 31, wherein said Final Base Lambda Table, Final Adjustment Alpha Table, Store Table, and UPC Table are used to calculate a probability distribution of inter-arrival times for said at least one item, and wherein said inter-arrival time is stated either in units of:

(i) time, or (ii) quantity of sales in monetary units.

54. The method as recited in claim 1, wherein when said waiting time is stated in units of quantity, said quantity is stated in terms of either the number of unique items sold or the total number of items sold.

56. The method as recited in claim 1, wherein information upon which said probability distribution is retrieved is further comprised of interval information, wherein said interval information is comprised of information about one or more conditions occurring during said waiting time.

57. The method as recited in claim 56, wherein said information about one or more conditions occurring during said waiting time is comprised of information about varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

64. The method as recited in claim 1, further comprising:

providing a point-of-sale controller that is in communication with said plurality of sensing circuits and with said communications port.

65. The method as recited in claim 1, wherein said probability distribution is retrieved based on varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

11. An item monitoring system, comprising:

(a) a plurality of sensing circuits that detect at least one item as it is moved to predetermined locations within a predetermined environment;

(b) a computer monitoring system, comprising;

(i) a memory circuit for storage of data, said memory circuit containing a quantity of random access memory (RAM) and a bulk memory storage device;

(ii) a communications port that is connected to at least one of said sensing circuits and to said memory circuit; and

(iii) a processing circuit that is configured to control the flow of data between said memory circuit and said communications port;

(c) said processing circuit also being configured to;

(i) receive, by way of said communications port, identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits, and to receive an arrival time, wherein said arrival time is a time when said at least one item was detected by the one of said plurality of sensing circuits;

(ii) determine at a later time, by way of said processing circuit, a waiting time for said at least one item, wherein said waiting time is an amount of time between said arrival time and said later time, wherein said later time is prior to a next time said at least one item is again detected by one of said plurality of sensing circuits;

(iii) retrieve, from said memory circuit, a probability distribution over inter-arrival times for said at least one item, wherein an inter-arrival time is an amount of time between an arrival of said at least one item and a next arrival of said at least one item, and wherein said probability distribution is retrieved, by way of said processing circuit, based on said identification characteristic information; and

(iv) determine, by way of said processing circuit, based on said retrieved probability distribution, whether said waiting time is anomalous if a cumulative probability of all inter-arrival times that are greater than said waiting time is less than a predetermined threshold; and

(v) generate at said later time, by way of said processing circuit, an inter-arrival time event announcement for said at least one item whenever said waiting time is anomalous.

12. The item monitoring system as recited in claim 11, further comprising:

a point-of-sale controller that is in communication with said plurality of sensing circuits and with said communications port.

33. The item monitoring system as recited in claim 11, said processing circuit also being configured to:

update said probability distribution over inter-arrival times for said at least one item after it passes one of said plurality of sensing circuits.

34. The item monitoring system as recited in claim 11, wherein said processing circuit is further configured to detect an out-of-stock event using a probability of observing zero arrivals of said at least one item during said waiting time for said at least one item.

35. The item monitoring system as recited in claim 34, wherein said out-of-stock event comprises a time interval during which said at least one item appears to be physically out-of-stock, and upon the occurrence of said out-of-stock event, the computer monitoring system summarizes said out-of-stock events, determines their causes, and measures their effect on revenues, profits, substitute items, and customer behavior.

36. The item monitoring system as recited in claim 11, wherein said probability distribution is determined by training said computer monitoring system by use of one of:

(i) historical transaction data, or (ii) transaction data that is gathered in substantially real time.

37. The item monitoring system as recited in claim 36, wherein said training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table.

38. The item monitoring system as recited in claim 11, wherein all said times are redefined in terms of measured cumulative activity in said predetermined environment, and wherein said measured activity is one of (i) quantity of packaged items sold or processed, (ii) sales in monetary units, or (iii) number of different items in each transaction at a point of sale.

39. The item monitoring system as recited in claim 11, wherein each inter-arrival time for said at least one item and a quantity of said at least one item are linked together as a renewal-reward process, in which the quantity of said at least one item is a separate random log-normal variable with a mean beta and a beta variance.

40. The item monitoring system as recited in claim 11, wherein each inter-arrival time for said at least one item varies as a function of:

total predetermined environment traffic, item category traffic, time of day, day of week, season, holidays, and market conditions of said predetermined environment.

41. The item monitoring system as recited in claim 11, wherein said predetermined environment comprises one of:

a retail store, a chain of retails stores, a warehouse, a chain of warehouses, a distribution point, or a chain of distribution points.

42. The item monitoring system as recited in claim 11, wherein said processing circuit is further configured to automatically re-train said computer monitoring system on a periodic basis using substantially real time data throughout a periodic interval.

43. The item monitoring system as recited in claim 42, wherein said re-training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table.

44. The item monitoring system as recited in claim 43, wherein said iterative passes comprise:

(i) computing Initial Base Lambdas using total store sales and total category sales;

(ii) computing Intermediate Base Lambdas using item transaction data and said item'"'"'s inter-arrival time using said Initial Base Lambdas;

(iii) computing Initial Adjustment Alphas using an adjusted item inter-arrival time and a plurality of current effects;

(iv) computing Final Base Lambdas using said Initial Adjustment Alphas and using said item transaction data and said item'"'"'s inter-arrival time; and

(v) computing Final Adjustment Alphas using said Final Base Lambdas and a plurality of current effects, and computing a Beta Table.

45. The item monitoring system as recited in claim 44, wherein said Final Base Lambda Table, Final Adjustment Alpha Table, Store Table, and UPC Table are used to calculate a probability distribution over inter-arrival times for said at least one item, and wherein said inter-arrival time is stated either in units of:

quantity or sales in monetary units.

46. The item monitoring system as recited in claim 11, said processing circuit also being configured to store said probability distribution in said memory circuit, wherein said memory circuit creates or modifies an entry in a database that is stored in said memory circuit such that said entry can later be accessed in substantially real time with respect to when said processing circuit determines whether said waiting time is anomalous.

47. The item monitoring system as recited in claim 11, wherein said identification characteristic information comprises:

an SKU identifier of said at least one item, or a bar code from a label affixed to said at least one item.

48. The item monitoring system as recited in claim 11, wherein said processing circuit determines whether said waiting time is anomalous in substantially real time with respect to when said processing circuit determines a waiting time for said at least one item.

49. The item monitoring system as recited in claim 11, wherein said processing circuit receives identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits occurs when said at least one item is being sold at a point-of-sale register within said predetermined environment.

50. The item monitoring system as recited in claim 11, said processing circuit generating an inter-arrival time event announcement when said waiting time is anomalous is indicative of one of the following conditions:

(i) said at least one item is substantially hidden while residing in its correct location on a display shelf (ii) said at least one item is completely out-of-stock on said display shelf (iii) said at least one item has been placed at an incorrect location within said predetermined environment, or (iv) access to said at least one item has been substantially prevented by an obstruction.

51. The item monitoring system as recited in claim 11, wherein said processing circuit is further configured to determine whether said waiting time is anomalous by comparing said waiting time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration at least one of the following factors:

varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

52. The item monitoring system as recited in claim 11, wherein said processing circuit is further configured to determine whether or not said waiting time is anomalous by comparing said waiting time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration a usage history of items being disbursed and received.

53. The item monitoring system as recited in claim 11, wherein if no probability distribution over inter-arrivals times can be retrieved based on said identification characteristic information, then said at least one item is a new item, and a new item event is generated and a probability distribution over inter-arrival times is created for said new item.

55. The item monitoring system as recited in claim 11, wherein when said waiting time is stated in units of quantity, said quantity is stated in terms of either the number of unique items sold or the total number of items sold.

58. The item monitoring system as recited in claim 11, wherein information upon which said probability distribution is retrieved is comprised of on interval information, wherein said interval information is comprised of information about one or more conditions occurring during said waiting time.

59. The item monitoring system as recited in claim 58, wherein said information about one or more conditions occurring during said waiting time is comprised of information about varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

66. The item monitoring system as recited in claim 11, wherein said processing circuit is further configured to retrieve said probability distribution based on varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

67. The item monitoring system as recited in claim 11, wherein said probability distribution comprises a modified Poisson distribution consisting of weighted sums of Poisson distributions.

68. The item monitoring system as recited in claim 34, wherein said computer monitoring system provides forecasting of inventory or replenishment levels that removes effects of out of stock events before generating forecasting reports.

69. The item monitoring system as recited in claim 11, wherein all said times are redefined in terms of measured cumulative activity in one of a retail store or a category of items in a retail store.

70. The item monitoring system as recited in claim 11, wherein said probability distribution over inter-arrival times is a Poisson distribution with a parameter lambda that is a function of Base Lambda and Adjustment Alpha, which include information as saved data and lookup tables on:

SKU, store, and various effects, including price point, promotion, season, holiday, time-of-day, day-of-week, and market conditions.

71. The item monitoring system as recited in claim 70, wherein a median is used to estimate said parameter lambda, thereby reducing bias in an estimate of a true parameter lambda arising from a contaminating effect of historical out-of-stock events.

72. The item monitoring system as recited in claim 39, wherein said mean and variance parameters to the renewal-reward process are not constants, but vary during the waiting time as conditions at said predetermined environment change.

73. The item monitoring system as recited in claim 11, said processing circuit also being configured to detect a slow event using a probability of observing more than K arrivals of said at least one item in the time actually observed for K arrivals of said at least one item.

74. The item monitoring system as recited in claim 11, said processing circuit also being configured to detect a fast event using a probability of observing less than J arrivals of said at least one item in the time actually observed for J arrivals of said at least one item.

60. A method of using a computer processor to monitor items being received and disbursed within a predetermined environment, said method comprising:

(a) providing a computer monitoring system having a memory circuit for storage of data, a communications port, and a processing circuit;

(b) providing a plurality of sensing circuits that detect at least one item as it is moved to predetermined locations within said predetermined environment;

(c) receiving, by way of said communications port, identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits;

(d) receiving, by way of said communications port, a first arrival time, wherein said first arrival time is a first time when said at least one item was detected by the one of said plurality of sensing circuits;

(e) receiving, by way of said communications port, a second arrival time, wherein said second arrival time is a next time said at least one item is again detected by one of said plurality of sensing circuits, and wherein said second arrival time is later than said first arrival time;

(f) determining at a later time, by way of said processing circuit, an observed inter-arrival time for said at least one item, wherein said observed inter-arrival time is an amount of time between said first arrival time and said second arrival time;

(g) retrieving, from said memory circuit, a probability distribution over inter-arrival times for said at least one item, wherein an inter-arrival time is an amount of time between an arrival of said at least one item and a next arrival of said at least one item, and wherein said probability distribution is retrieved, by way of said processing circuit, based on said identification characteristic information;

(h) determining, by way of said processing circuit, based on said retrieved probability distribution, that said observed inter-arrival time is anomalous if a cumulative probability of all inter-arrival times that are less than said observed inter-arrival time is less than a predetermined threshold; and

(i) generating at said later time, by way of said processing circuit, an inter-arrival time event announcement for said at least one item whenever said observed inter-arrival time is anomalous.

61. The method as recited in claim 60, further comprising:

(j) repeating steps (d)-(g) for one to six, additional, consecutive arrivals of said at least one item;

and wherein the step of determining, by way of said processing circuit, based on said retrieved probability distribution, that said observed inter-arrival time is anomalous is based on said one to six, additional, consecutive arrivals of said at least one item.

62. The method as recited in claim 60, further comprising:

(j) repeating steps (d)-(g) for three, additional, consecutive arrivals of said at least one item;

and wherein the step of determining, by way of said processing circuit, based on said retrieved probability distribution, that said observed inter-arrival time is anomalous is based on said three, additional, consecutive arrivals of said at least one item.

77. The method as recited in claim 60, further comprising updating said probability distribution over inter-arrival times for said at least one item after it passes one of said plurality of sensing circuits.

78. The method as recited in claim 60, further comprising storing said probability distribution in said memory circuit, wherein the step of storing said probability distribution in said memory circuit comprises:

creating or modifying an entry in a database that is stored in said memory circuit such that said entry can later be accessed in substantially real time with respect to the occurrence of the step of determining, by way of said processing circuit, whether said observed inter-arrival time is anomalous.

79. The method as recited in claim 60, wherein said identification characteristic information comprises:

an SKU identifier of said at least one item, or a bar code from a label affixed to said at least one item.

80. The method as recited in claim 60, wherein the step of determining, by way of said processing circuit, whether said observed inter-arrival time is anomalous occurs in substantially real time with respect to the occurrence of said step of determining an observed inter-arrival time for said at least one item.

81. The method as recited in claim 60, wherein the step of receiving identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits occurs in substantially real time with respect to said at least one item is being sold at a point-of-sale register within said predetermined environment.

82. The method as recited in claim 60, wherein said step of determining, by way of said processing circuit, whether said observed inter-arrival time is anomalous comprises:

comparing said observed inter-arrival time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration at least one of the following factors;

varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

83. The method as recited in claim 60, wherein said step of determining whether or not said observed inter-arrival time is anomalous comprises:

comparing said observed inter-arrival time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration a usage history of items being disbursed and received.

84. The method as recited in claim 60, wherein, if no probability distribution over inter-arrivals times can be retrieved based on said identification characteristic information, then said at least one item is a new item, and a new item event is generated, and a probability distribution over inter-arrival times is created for said new item.

85. The method as recited in claim 60, wherein said probability distribution comprises a modified Poisson distribution consisting of weighted sums of Poisson distributions.

86. The method as recited in claim 60, wherein said probability distribution is determined by training said computer monitoring system by use of one of:

(i) historical transaction data, or (ii) transaction data that is gathered in substantially real time.

87. The method as recited in claim 86, wherein said training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table or Item Table.

88. The method as recited in claim 60, wherein all said times are redefined in terms of measured cumulative activity in said predetermined environment, and wherein said measured cumulative activity is one of (i) quantity of packaged items sold or processed, (ii) sales in monetary units, or (iii) number of different items in each transaction at a point of sale.

89. The method as recited in claim 60, wherein all said times are redefined in terms of measured cumulative activity in one of a retail store or a category of items in a retail store.

90. The method as recited in claim 60, wherein said probability distribution over inter-arrival times is a Poisson distribution with a lambda parameter that is a function of Base Lambda and Adjustment Alpha, which include information as saved data and lookup tables on:

SKU, store, and various effects, including price point, promotion, season, holiday, time-of-day, day-of-week, and market conditions.

91. The method as recited in claim 90, wherein a median is used to estimate said parameter lambda, thereby reducing bias in an estimate of a true parameter lambda arising from a contaminating effect of historical out-of-stock events.

92. The method as recited in claim 60, each observed inter-arrival time for said at least one item and a quantity of said at least one item are linked together as a renewal-reward process, in which the quantity of said at least one item is a separate random log-normal variable with a mean beta and a beta variance.

93. The method as recited in claim 92, wherein said mean and variance parameters to the renewal-reward process are not constants, but vary during the observed inter-arrival time as conditions at said predetermined environment change.

94. The method as recited in claim 60, further comprising:

detecting a slow event using a probability of observing more than K arrivals of said at least one item in the time actually observed for K arrivals of said at least one item.

95. The method as recited in claim 60, further comprising:

detecting a fast event using a probability of observing less than J arrivals of said at least one item in the time actually observed for J arrivals of said at least one item.

96. The method as recited in claim 60, wherein each observed inter-arrival time for said at least one time varies as a function of:

total predetermined environment traffic, item category traffic, time of day, day of week, season, holidays, and market conditions of said predetermined environment.

97. The method as recited in claim 60, wherein said predetermined environment comprises one of:

a retail store, a chain of retails stores, a warehouse, a chain of warehouses, a distribution point, a chain of distribution points, manufacture'"'"'s distribution center or a chain of manufacture'"'"'s distribution centers.

98. The method as recited in claim 60, further comprising:

automatically re-training said computer monitoring system on a periodic basis using substantially real time data throughout a periodic interval.

99. The method as recited in claim 98, wherein said re-training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table.

100. The method as recited in claim 99, wherein said iterative passes comprise:

(i) computing Initial Base Lambdas using total store sales and total category sales;

(ii) computing Intermediate Base Lambdas using item transaction data and said item'"'"'s inter-arrival time using said Initial Base Lambdas;

(iii) computing Initial Adjustment Alphas using an adjusted item inter-arrival time and a plurality of current effects;

(iv) computing Final Base Lambdas using said Initial Adjustment Alphas and using said item transaction data and said item'"'"'s inter-arrival time; and

(v) computing Final Adjustment Alphas using said Final Base Lambdas and a plurality of current effects, and computing a Beta Table.

101. The method as recited in claim 100, wherein said Final Base Lambda Table, Final Adjustment Alpha Table, Store Table, and UPC Table are used to calculate a probability distribution of inter-arrival times for said at least one item, and wherein said inter-arrival time is stated either in units of:

(i) time, or (ii) quantity of sales in monetary units.

102. The method as recited in claim 60, wherein when said observed inter-arrival time is stated in units of quantity, said quantity is stated in terms of either the number of unique items sold or the total number of items sold.

103. The method as recited in claim 60, wherein information upon which said probability distribution is retrieved is further comprised of interval information, wherein said interval information is comprised of information about one or more conditions occurring during said observed inter-arrival time.

104. The method as recited in claim 103, wherein said information about one or more conditions occurring during said observed inter-arrival time is comprised of information about varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

105. The method as recited in claim 60, further comprising:

providing a point-of-sale controller that is in communication with said plurality of sensing circuits and with said communications port.

106. The method as recited in claim 60, wherein said probability distribution is retrieved based on varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

63. An item monitoring system, comprising:

(a) a plurality of sensing circuits that detect at least one item as it is moved to predetermined locations within a predetermined environment;

(b) a computer monitoring system, comprising;

(i) a memory circuit for storage of data, said memory circuit containing a quantity of random access memory (RAM) and a bulk memory storage device;

(ii) a communications port that is connected to at least one of said sensing circuits and to said memory circuit; and

(iii) a processing circuit that is configured to control the flow of data between said memory circuit and said communications port;

(c) said processing circuit also being configured to;

(i) receive, by way of said communications port, identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits;

(ii) receive, by way of said communications port, a first arrival time, wherein said first arrival time is a first time when said at least one item was detected by the one of said plurality of sensing circuits;

(iii) receive, by way of said communications port, a second arrival time, wherein said second arrival time is a next time said at least one item is again detected by one of said plurality of sensing circuits, and wherein said second arrival time is later than said first arrival time;

(iv) determine at a later time, by way of said processing circuit, an observed inter-arrival time for said at least one item, wherein said observed inter-arrival time is an amount of time between said first arrival time and said second arrival time;

(v) retrieve, from said memory circuit, a probability distribution over inter-arrival times for said at least one item, wherein an inter-arrival time is an amount of time between an arrival of said at least one item and a next arrival of said at least one item, and wherein said probability distribution is retrieved, by way of said processing circuit, based on said identification characteristic information;

(vi) determine, by way of said processing circuit, based on said retrieved probability distribution, that said observed inter-arrival time is anomalous if a cumulative probability of all inter-arrival times that are less than said observed inter-arrival time is less than a predetermined threshold; and

(vii) generate at said later time, by way of said processing circuit, an inter-arrival time event announcement for said at least one item whenever said observed inter-arrival time is anomalous.

75. The item monitoring system as recited in claim 63, further comprising:

(j) repeating steps (d)-(g) for one to six, additional, consecutive arrivals of said at least one item;

and wherein the step of determining, by way of said processing circuit, based on said retrieved probability distribution, that said observed inter-arrival time is anomalous is based on said one to six, additional, consecutive arrivals of said at least one item.

76. The item monitoring system as recited in claim 63, further comprising:

(j) repeating steps (d)-(g) for three, additional, consecutive arrivals of said at least one item;

and wherein the step of determining, by way of said processing circuit, based on said retrieved probability distribution, that said observed inter-arrival time is anomalous is based on said three, additional, consecutive arrivals of said at least one item.

107. The item monitoring system as recited in claim 63, said processing circuit also being configured to update said probability distribution over inter-arrival times for said at least one item after it passes one of said plurality of sensing circuits.

108. The item monitoring system as recited in claim 63, said processing circuit also being configured to store said probability distribution in said memory circuit, wherein said memory circuit creates or modifies an entry in a database that is stored in said memory circuit such that said entry can later be accessed in substantially real time with respect to when said processing circuit determines whether said observed inter-arrival time is anomalous.

109. The item monitoring system as recited in claim 63, wherein said identification characteristic information comprises:

an SKU identifier of said at least one item, or a bar code from a label affixed to said at least one item.

110. The item monitoring system as recited in claim 63, wherein said processing circuit determines whether said observed inter-arrival time is anomalous in substantially real time with respect to when said processing circuit determines an observed inter-arrival time for said at least one item.

111. The item monitoring system as recited in claim 63, wherein said processing circuit receives identification characteristic information pertaining to said at least one item as it passes one of said plurality of sensing circuits occurs when said at least one item is being sold at a point-of-sale register within said predetermined environment.

112. The item monitoring system as recited in claim 63, said processing circuit generating an inter-arrival time event announcement when said observed inter-arrival time is anomalous is indicative of one of the following conditions:

(i) said at least one item is substantially hidden while residing in its correct location on a display shelf;

(ii) said at least one item is completely out-of-stock on said display shelf;

(iii) said at least one item has been placed at an incorrect location within said predetermined environment, or (iv) access to said at least one item has been substantially prevented by an obstruction.

113. The item monitoring system as recited in claim 63, wherein said processing circuit is further configured to determine whether said observed inter-arrival time is anomalous by comparing said observed inter-arrival time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration at least one of the following factors:

varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

114. The item monitoring system as recited in claim 63, wherein said processing circuit is further configured to determine whether or not said observed inter-arrival time is anomalous by comparing said observed inter-arrival time of said at least one item to said probability distribution over inter-arrival times for said at least one item, while taking into consideration a usage history of items being disbursed and received.

115. The item monitoring system as recited in claim 63, if no probability distribution over inter-arrivals times can be retrieved based on said identification characteristic information, then said at least one item is a new item, and a new item event is generated and a probability distribution over inter-arrival times is created for said new item.

116. The item monitoring system as recited in claim 63, further comprising:

a point-of-sale controller that is in communication with said plurality of sensing circuits and with said communications port.

117. The item monitoring system as recited in claim 63, wherein said probability distribution comprises a modified Poisson distribution consisting of weighted sums of Poisson distributions.

118. The item monitoring system as recited in claim 63, wherein said probability distribution is determined by training said computer monitoring system by use of one of:

(i) historical transaction data, or (ii) transaction data that is gathered in substantially real time.

119. The item monitoring system as recited in claim 118, wherein said training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table.

120. The item monitoring system as recited in claim 63, wherein all said times are redefined in terms of measured cumulative activity in said predetermined environment, and wherein said measured activity is one of (i) quantity of packaged items sold or processed, (ii) sales in monetary units, or (iii) number of different items in each transaction at a point of sale.

121. The item monitoring system as recited in claim 63, wherein all said times are redefined in terms of measured cumulative activity in one of a retail store or a category of items in a retail store.

122. The item monitoring system as recited in claim 63, wherein said probability distribution over inter-arrival times is a Poisson distribution with a parameter lambda that is a function of Base Lambda and Adjustment Alpha, which include information as saved data and lookup tables on:

SKU, store, and various effects, including price point, promotion, season, holiday, time-of-day, day-of-week, and market conditions.

123. The item monitoring system as recited in claim 122, wherein a median is used to estimate said parameter lambda, thereby reducing bias in an estimate of a true parameter lambda arising from a contaminating effect of historical out-of-stock events.

124. The item monitoring system as recited in claim 123, wherein said mean and variance parameters to the renewal-reward process are not constants, but vary during the observed inter-arrival time as conditions at said predetermined environment change.

125. The item monitoring system as recited in claim 63, wherein each observed inter-arrival time for said at least one item and a quantity of said at least one item are linked together as a renewal-reward process, in which the quantity of said at least one item is a separate random log-normal variable with a mean beta and a beta variance.

126. The item monitoring system as recited in claim 63, said processing circuit also being configured to detect a slow event using a probability of observing more than K arrivals of said at least one item in the time actually observed for K arrivals of said at least one item.

127. The item monitoring system as recited in claim 63, said processing circuit also being configured to detect a fast event using a probability of observing less than J arrivals of said at least one item in the time actually observed for J arrivals of said at least one item.

128. The item monitoring system as recited in claim 63, wherein each observed inter-arrival time for said at least one item varies as a function of:

total predetermined environment traffic, item category traffic, time of day, day of week, season, holidays, and market conditions of said predetermined environment.

129. The item monitoring system as recited in claim 63, wherein said predetermined environment comprises one of:

a retail store, a chain of retails stores, a warehouse, a chain of warehouses, a distribution point, or a chain of distribution points.

130. The item monitoring system as recited in claim 63, wherein said processing circuit is further configured to automatically re-train said computer monitoring system on a periodic basis using substantially real time data throughout a periodic interval.

131. The item monitoring system as recited in claim 130, wherein said re-training of the computer monitoring system occurs in a plurality of iterative passes to create:

a Final Base Lambda Table, a Final Adjustment Alpha Table, a Store Table, and a UPC Table.

132. The item monitoring system as recited in claim 131, wherein said iterative passes comprise:

(i) computing Initial Base Lambdas using total store sales and total category sales;

(ii) computing Intermediate Base Lambdas using item transaction data and said item'"'"'s inter-arrival time using said Initial Base Lambdas;

(iii) computing Initial Adjustment Alphas using an adjusted item inter-arrival time and a plurality of current effects;

(iv) computing Final Base Lambdas using said Initial Adjustment Alphas and using said item transaction data and said item'"'"'s inter-arrival time; and

(v) computing Final Adjustment Alphas using said Final Base Lambdas and a plurality of current effects, and computing a Beta Table.

133. The item monitoring system as recited in claim 132, wherein said Final Base Lambda Table, Final Adjustment Alpha Table, Store Table, and UPC Table are used to calculate a probability distribution over inter-arrival times for said at least one item, and wherein said inter-arrival time is stated either in units of:

quantity or sales in monetary units.

134. The item monitoring system as recited in claim 63, wherein when said observed inter-arrival time is stated in units of quantity, said quantity is stated in terms of either the number of unique items sold or the total number of items sold.

135. The item monitoring system as recited in claim 63, wherein information upon which said probability distribution is retrieved is comprised of on interval information, wherein said interval information is comprised of information about one or more conditions occurring during said observed inter-arrival time.

136. The item monitoring system as recited in claim 135, wherein said information about one or more conditions occurring during said observed inter-arrival time is comprised of information about varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.

137. The item monitoring system as recited in claim 63, wherein said processing circuit is further configured to retrieve said probability distribution based on varying price conditions, time of day, day of week, week of year, holidays, promotion activities, or competitive activities.