Product inventory category management and variety optimization method and system

• US 6,366,890 B1
• Filed: 02/27/1998
• Issued: 04/02/2002
• Est. Priority Date: 02/27/1998
• Status: Expired due to Fees

• Current Assignee: Gerald L. Usrey
• Original Assignee: Gerald L. Usrey
• Inventors: Usrey, Gerald L.
• Primary Examiner(s): Millin, Vincent
• Assistant Examiner(s): Nguyen, Cuong H.
• Application Number:
  US09/032,040
• Time in Patent Office: 1,495 Days
• Field of Search: G06/F 1.520, G06/F 1.521, G06/F 1.760, G06/F12.300, 705/28, 705/29, 705/412, 705/23, 705/7, 705/10, 705/22
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 10/0875 Itemisation or classification of parts, supplies or services, e.g. bill of materials
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

In the field of retail product sales, a retail store would like to identify the most popular products in the market to make sure the product assortment of the store carries those popular products. By focusing on the most popular varieties of products and decreasing the exposure of the least popular varieties of products, a retail store can optimize its assortment selection and increase its profits and sales. The present invention supports the optimization of a retail product selection in a computerized system for categorizing products in inventory, determining market demand for products in the market, and optimizing the variety of products in inventory based upon retail sales data for a geographic region and a chain of stores. With the present method and system, a significant amount of retail sales data is manipulated into an easy to understand format for product category management and variety optimization.

Claims

1. A method for optimizing a retail product assortment carried by a store comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market;

categorizing the retail sales data into at least one product category segment for analysis;

determining market coverage of at least one product in the retail product assortment carried by the store;

classifying an average throughput of at least one of the products in the retail product assortment carried by the store; and

,

analyzing at least one variation in the retail product assortment carried by the store.

2. The optimizing method according to claim 1 wherein said retail sales data includes data for a chain of stores.

3. The optimizing method according to claim 1 wherein said retail sales data includes data for a geographic region.

4. The optimizing method according to claim 1 wherein said retail sales data includes data for a chain of stores and a geographic region.

5. The optimizing method according to claim 1 further comprising the step of:

excluding at least one product component during the categorizing step.

6. The optimizing method according to claim 1 further comprising the step of:

including at least one product component during the categorizing step.

7. The optimizing method according to claim 1 further comprising the step of:

combining a plurality of product components during the categorizing step.

8. The optimizing method of claims 5, 6 or 7 wherein a product component includes one of the following:

a product type,

a product container size, or

a product container type.

9. The optimizing method according to claim 1 further comprising the step of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

10. The optimizing method according to claim 1 further comprising the step of:

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

11. The optimizing method according to claim 1 further comprising the steps of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market; and

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

12. The optimizing method according to claim 1 wherein:

a plurality of unique classification values are used to classify the average throughput of the products in the retail product assortment carried by the store.

13. The optimizing method according to claim 12 wherein:

the plurality of unique classification values may be modified by the number of classification values.

14. The optimizing method according to claim 12 wherein:

the plurality of unique classification values may be modified by the range of throughput of the classification.

15. The optimizing method according to claim 1 further comprising the step of:

adding an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

16. The optimizing method according to claim 1 further comprising the step of:

deleting an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

17. A computer-implemented method for optimizing a retail product assortment carried by a store comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market,

categorizing the retail sales data in to at least one product category segment for analysis, and

using the categorized data to assist in optimizing a retail product assortment.

18. The optimizing method according to claim 17 wherein said retail sales data includes data for a chain of stores.

19. The optimizing method according to claim 17 wherein said retail sales data includes data for a geographic region.

20. The optimizing method according to claim 17 wherein said retail sales data includes data for a chain of stores and a geographic region.

21. The optimizing method according to claim 17 further comprising the steps of:

excluding at least one product component during the categorizing step.

22. The optimizing method according to claim 17 further comprising the steps of:

including at least one product component during the categorizing step.

23. The optimizing method according to claim 17 further comprising the steps of:

combining a plurality of product components during the categorizing step.

24. The optimizing method of claims 21, 22, or 23 wherein a product component includes one of the following:

a product type,

a product container size, or

a product container type.

25. A method for optimizing a retail product assortment carried by a store comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market;

determining market coverage of the products in the retail product assortment carried by the store; and

,

analyzing at least one variation in the retail product assortment carried by the store.

26. The optimizing method according to claim 25 wherein said retail sales data includes data for a chain of stores.

27. The optimizing method according to claim 25 wherein said retail sales data includes data for a geographic region.

28. The optimizing method according to claim 25 wherein said retail sales data includes data for a chain of stores and a geographic region.

29. The optimizing method according to claim 25 further comprising the step of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

30. The optimizing method according to claim 25 further comprising the step of:

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

31. The optimizing method according to claim 25 further comprising the steps of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market; and

,

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

32. The optimizing method according to claim 25 further comprising the step of:

adding an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

33. The optimizing method according to claim 25 further comprising the step of:

deleting an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

34. A method for optimizing a retail product assortment carried by a store comprising the steps of:

classifying an average throughput of at least one product in the retail product assortment carried by the store according to retail sales data related to the products in the retail product assortment; and

,

identifying a variation in the retail product assortment based upon the throughput classification assigned to a product in the market.

35. The optimizing method according to claim 34 wherein said retail sales data includes data for a chain of stores.

36. The optimizing method according to claim 34 wherein said retail sales data includes data for a geographic region.

37. The optimizing method according to claim 34 wherein said retail sales data includes data for a chain of stores and a geographic region.

38. The optimizing method according to claim 34 wherein:

a plurality of unique classification values are used to classify the average throughput of the products in the retail product assortment carried by the store.

39. The optimizing method according to claim 34 wherein:

the plurality of unique classification values may be modified by the number of classification values.

40. The optimizing method according to claim 34 wherein:

the plurality of unique classification values may be modified by the range of throughput of the classification.

41. The optimizing method according to claim 34 further comprising the step of:

adding an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

42. The optimizing method according to claim 34 further comprising the step of:

deleting an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

43. A method for categorizing segments of a product market, comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market; and

,

performing one of the following steps;

excluding a product component from the product category;

including a product component from the product category;

or,

combining product components into the product category.

44. The categorizing method according to claim 43 wherein said retail sales data includes data for a chain of stores.

45. The categorizing method according to claim 43 wherein said retail sales data includes data for a geographic region.

46. The categorizing method according to claim 43 wherein said retail sales data includes data for a chain of stores and a geographic region.

47. The categorizing method of claim 43 wherein the product component includes one of the following:

a product type,

a product container size, or

a product container type.

48. A method for determining the market coverage of a retail product assortment carried by a store, comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market; and

,

determining market coverage of at least one product in the retail product assortment carried by the store.

49. The determination method according to claim 48 wherein said retail sales data includes data for a chain of stores.

50. The determination method according to claim 48 wherein said retail sales data includes data for a geographic region.

51. The determination method according to claim 48 wherein said retail sales data includes data for a chain of stores and a geographic region.

52. The determination method according to claim 48 further comprising the step of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

53. The determination method according to claim 48 further comprising the step of:

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

54. The determination method according to claim 48 further comprising the steps of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market; and

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

55. A method for determining a throughput classification of a product, comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market; and

,

classifying an average throughput of the products in the retail product assortment carried by the store based upon the plurality of retail sales data.

56. The determination method according to claim 55 wherein said retail sales data includes data for a chain of stores.

57. The determination method according to claim 55 wherein said retail sales data includes data for a geographic region.

58. The determination method according to claim 55 wherein said retail sales data includes data for a chain of stores and a geographic region.

59. The determination method according to claim 55 wherein:

a plurality of unique classification values are used to classify the average throughput of the products in the retail product assortment carried by the store.

60. The determination method according to claim 59 wherein:

the plurality of unique classification values may be modified by the number of classification values.

61. The determination method according to claim 59 wherein:

the plurality of unique classification values may be modified by the range of throughput of the classification.

62. A method for optimizing an assortment of retail products, comprising the steps of:

providing a plurality of retail sales data for a plurality of retail products in a market;

classifying an average throughput of at least one product in the retail product assortment carried by the store;

analyzing at least one variation in the retail product assortment carried by the store.

63. The optimizing method according to claim 62 wherein said retail sales data includes data for a chain of stores.

64. The optimizing method according to claim 62 wherein said retail sales data includes data for a geographic region.

65. The optimizing method according to claim 62 wherein said retail sales data includes data for a chain of stores and a geographic region.

66. The optimizing method according to claim 62 further comprising the steps of:

determining market coverage of at least one product in the retail product assortment carried by the store.

67. The optimizing method according to claim 66 further comprising the step of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

68. The optimizing method according to claim 66 further comprising the step of:

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

69. The optimizing method according to claim 66 further comprising the steps of:

calculating the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market; and

calculating the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

70. The optimizing method according to claim 62 wherein:

a plurality of unique classification values are used to classify the average throughput of the products in the retail product assortment carried by the store.

71. The optimizing method according to claim 70 wherein:

the plurality of unique classification values may be modified by the number of classification values.

72. The optimizing method according to claim 70 wherein:

the plurality of unique classification values may be modified by the range of throughput of the classification.

73. The optimizing method according to claim 62 further comprising the step of:

adding an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

74. The optimizing method according to claim 62 further comprising the step of:

deleting an item to the retail product assortment carried by the store to analyze at least one variation in the retail product assortment carried by the store.

75. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market;

a product segment categorizer which manipulates the retail sales data in the database into at least one product category segment for analysis;

a market coverage analyzer which determines a market coverage of at least one retail product carried by the store;

an average throughput classifier which classifies an average throughput of at least one product in the retail product assortment carried by the store; and

,

an alternative assortment analyzer which analyzes at least one variation in the retail product assortment carried by the store.

76. The system according to claim 75 wherein said database includes data for a chain of stores.

77. The system according to claim 75 wherein said database includes data for a geographic region.

78. The system according to claim 75 wherein said database includes data for a chain of stores and a geographic region.

79. The system according to claim 75 wherein the categorizer excludes at least one product component during the categorization of data in the database.

80. The system according to claim 75 wherein the categorizer includes at least one product component during the categorization of data in the database.

81. The system according to claim 75 wherein the categorizer combines a plurality of product components during the categorization of data in the database.

82. The system according to claims 79, 80, or 81 wherein the product component includes one of the following:

a product type,

a product container size, or

a product container type.

83. The system according to claim 75 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

84. The system according to claim 75 wherein the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

85. The system according to claim 75 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market, and

the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

86. The system according to claim 75 wherein the average throughput classifier uses a plurality of unique classification values to classify the average throughput of at least one product in the retail product assortment carried by the store.

87. The system according to claim 86 wherein the plurality of unique classification values may be modified by the number of classification values.

88. The system according to claim 86 wherein the plurality of unique classification values may be modified by the range of throughput of the classification.

89. The system according to claim 75 wherein said an alternative assortment analyzer calculates impact of adding an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

90. The system according to claim 75 wherein said an alternative assortment analyzer calculates impact of deleting an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

91. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market;

a product segment categorizer which manipulates the retail sales data in the database into at least one product category segment for analysis.

92. The system according to claim 91 wherein said database includes data for a chain of stores.

93. The system according to claim 91 wherein said database includes data for a geographic region.

94. The system according to claim 91 wherein said database includes data for a chain of stores and a geographic region.

95. The system according to claim 91 wherein the categorizer excludes at least one product component during the categorization of data in the database.

96. The system according to claim 91 wherein the categorizer includes at least one product component during the categorization of data in the database.

97. The system according to claim 91 wherein the categorizer combines a plurality of product components during categorization of data in the database.

98. The system according to claims 95, 96, or 97 wherein the product component includes one of the following:

a product type,

a product container size, or

a product container type.

99. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market;

a market coverage analyzer which determines the market coverage of the retail products carried by the store; and

,

an alternative assortment analyzer which analyzes at least one variation in the retail product assortment carried by the store.

100. The system according to claim 99 wherein said database includes data for a chain of stores.

101. The system according to claim 99 wherein said database includes data for a geographic region.

102. The system according to claim 99 wherein said database includes data for a chain of stores and a geographic region.

103. The system according to claim 99 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

104. The system according to claim 99 wherein the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

105. The system according to claim 99 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market, and

the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

106. The system according to claim 99 wherein said an alternative assortment analyzer calculates impact of deleting an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

107. The system according to claim 99 wherein said an alternative assortment analyzer calculates impact of adding an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

108. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market;

an average throughput classifier which classifies the average throughput of at least one product in the retail product assortment carried by the store; and

,

an alternative assortment analyzer which analyzes at least one variation in the retail product assortment carried by the store.

109. The system according to claim 108 wherein said database includes data for a chain of stores.

110. The system according to claim 108 wherein said database includes data for a geographic region.

111. The system according to claim 108 wherein said database includes data for a chain of stores and a geographic region.

112. The system according to claim 108 wherein the average throughput classifier uses a plurality of unique classification values to classify the average throughput of the products in the retail product assortment carried by the store.

113. The system according to claim 112 wherein the plurality of unique classification values may be modified by the number of classification values.

114. The system according to claim 112 wherein the plurality of unique classification values may be modified by the range of throughput of the classification.

115. The system according to claim 108 wherein said an alternative assortment analyzer calculates impact of adding an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

116. The system according to claim 108 wherein said an alternative assortment analyzer calculates impact of deleting an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

117. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market; and

a product segment categorizer which manipulates the retail sales data in the database into at least one product category segment for analysis by either adding, deleting or combining product components.

118. The system according to claim 117 wherein said database includes data for a chain of stores.

119. The system according to claim 117 wherein said database includes data for a geographic region.

120. The system according to claim 117 wherein said database includes data for a chain of stores and a geographic region.

121. The system according to claim 117 wherein the product component includes one of the following:

a product type,

a product container size, or

a product container type.

122. The system according to claim 117 wherein the categorizer provides graphical or tabular display.

123. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market; and

,

a market coverage analyzer which determines a market coverage of at least one retail product carried by the store.

124. The system according to claim 123 wherein said database includes data for a chain of stores.

125. The system according to claim 123 wherein said database includes data for a geographic region.

126. The system according to claim 123 wherein said database includes data for a chain of stores and a geographic region.

127. The system according to claim 123 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

128. The system according to claim 123 wherein the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

129. The system according to claim 125 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market, and

the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

130. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market; and

an average throughput classifier which classifies an average throughput of a plurality of products in the retail product assortment carried by the store.

131. The system according to claim 130 wherein said database includes data for a chain of stores.

132. The system according to claim 130 wherein said database includes data for a geographic region.

133. The system according to claim 130 wherein said database includes data for a chain of stores and a geographic region.

134. The system according to claim 130 wherein the average throughput classifier uses a plurality of unique classification values to classify the average throughput of the products in the retail product assortment carried by the store.

135. The system according to claim 134 wherein the plurality of unique classification values may be modified by the number of classification values.

136. The system according to claim 134 wherein the plurality of unique classification values may be modified by the range of throughput of the classification.

137. A computer-based system stored in a software storage medium for optimizing a retail product assortment carried by a store comprising:

a database having a plurality of retail sales data for a market;

an average throughput classifier which classifies an average throughput of a plurality of products in the retail product assortment carried by the store; and

,

an alternative assortment analyzer which analyzes at least one variation in the retail product assortment carried by the store.

138. The system according to claim 137 wherein said database includes data for a chain of stores.

139. The system according to claim 137 wherein said database includes data for a geographic region.

140. The system according to claim 137 wherein said database includes data for a chain of stores and a geographic region.

141. The system according to claim 137 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market.

142. The system according to claim 137 wherein the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

143. The system according to claim 137 wherein the market coverage analyzer calculates the percent product coverage to determine the market coverage of the products in the retail product assortment carried by the store wherein percent product coverage is defined as (a/b)*100, where a equals the number of items in a product segment carried by the store and b equals the total items in the product segment in the market, and

the market coverage analyzer calculates the market demand to determine market coverage of the products in the retail product assortment carried by the store wherein market demand is defined as (c/d)*100, where c equals the sales revenue generated by the items in a product segment carried by the store and d equals the total sales revenue generated by the items in the product segment in the market.

144. The system according to claim 137 wherein the average throughput classifier uses a plurality of unique classification values to classify the average throughput of the products in the retail product assortment carried by the store.

145. The system according to claim 144 wherein the plurality of unique classification values may be modified by the number of classification values.

146. The system according to claim 144 wherein the plurality of unique classification values may be modified by the range of throughput of the classification.

147. The system according to claim 137 wherein said an alternative assortment analyzer calculates impact of adding an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.

148. The system according to claim 137 wherein said an alternative assortment analyzer calculates impact of deleting an item to the retail product assortment carried by the store in order to analyze at least one variation in the retail product assortment carried by the store.