US2020042937A1PendingUtilityA1
System and method for item category footage recommendation
Est. expiryAug 6, 2038(~12 yrs left)· nominal 20-yr term from priority
G06Q 10/067G06Q 10/0875
64
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Claims
Abstract
A method for computer modeling a retail environment includes: calculating a space elasticity for an item category in a retail store, using a constrained linear regression model; calculating a cross-space elasticity for the item category in the retail store, using a multiple non-linear regression model; generating a category space allocation for the item category in the retail store, using a non-linear multiple-constraint mixed integer optimization model, based on the space elasticity of the item category and the cross-space elasticity of the item category; and generating an electronic planogram for the retail store, based on the category space allocation of the item category.
Claims
exact text as granted — not AI-modifiedWe claim:
1 . A method for computer modeling a retail environment, comprising:
calculating a space elasticity for an item category in a retail store, using a constrained linear regression model; calculating a cross-space elasticity for the item category in the retail store, using a multiple non-linear regression model; generating a category space allocation for the item category in the retail store, using a non-linear multiple-constraint mixed integer optimization model, based on the space elasticity of the item category and the cross-space elasticity of the item category; and generating an electronic planogram for the retail store, based on the category space allocation of the item category.
2 . The method of claim 1 , wherein the constrained linear regress model comprises Log(D)=log(a)+b*log(x), wherein D is a demand of the item category, x is a number of horizontal footage of the item category on one or more shelves in the retail store, a is a scale parameter being=>0, and b is the space elasticity of the item category being 0<=b<=1.
3 . The method of claim 2 , wherein the space elasticity is calculated by fitting the constrained linear regress model on known data between the demand of the item category and the number of horizontal footage of the item category.
4 . The method of claim 1 , wherein the non-linear multiple regression model is derived from the constrained linear regression model by including horizontal footage allocations of other item categories in the retail store.
5 . The method of claim 4 , wherein the non-linear multiple regression model is solved using least absolute shrinkage and selection operator (LASSO) regression analysis method, such that over-fitting of the multiple regression model is avoided, and relatively important item categories which influence a demand of the item category.
6 . The method of claim 1 , wherein the constrained linear regression model further comprises parameters including a linear horizontal footage of the self and a number of the shelves.
7 . The method of claim 1 , wherein the constrained linear regression further comprises parameters including clusters of the retail store.
8 . The method of claim 1 , wherein the non-linear multiple-constraint mixed integer optimization model is solved using genetic algorithm
9 . The method of claim 1 , wherein the non-linear multiple-constraint mixed integer optimization model comprises
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∑
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=
1
n
(
p
i
α
i
(
x
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b
i
∏
j
=
1
≠
i
n
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x
j
L
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g
ij
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,
wherein:
p i is a unit price of an ith item category in the retail store, x i is category space allocations of the ith item category and is a positive number, L is a linear footage per shelf, S is a number of shelves in the retail store, a i , b i are parameters which are solved using the constrained linear regression model, g ij is a cross-space elasticity of the ith item category on a jth item category solved using the multiple regression model; and
lb i <=x i <=ub i in which lb i and ub i are respectively lower and upper bounds of category space allocation of the ith item category.
10 . The method of claim 9 , wherein the lower and upper bounds are derived from business constraints of 1.5 case pack and 3.5 days of supply (DOS), and 1 DOS safety stock, to eliminate over-stock and out-of-stock situations:
l
b
i
=
⌈
max
(
1.5
CP
·
1
DOS
units
)
vf
i
df
i
⌉
,
ub
i
=
⌈
max
(
1.5
CP
·
4.5
DOS
units
)
vf
i
df
i
⌉
+
1
,
in which 1 DOS units=median of (historical) daily sales vf i and df i are respectively vertical and depth footage of the ith item category for the shelf.
11 . A system for computer modeling a retail environment, comprising:
a processor configured to:
calculate a space elasticity for an item category in a retail store, using a constrained linear regression model;
calculate a cross-space elasticity for the item category in the retail store, using a multiple non-linear regression model;
generate a category space allocation for the item category in the retail store, using a non-linear multiple-constraint mixed integer optimization model, based on the space elasticity of the item category and the cross-space elasticity of the item category; and
generate an electronic planogram for the retail store, based on the category space allocation of the item category.
12 . The system of claim 11 , wherein the constrained linear regress model comprises Log(D)=log(a)+b*log(x), wherein D is a demand of the item category, x is a number of horizontal footage of the item category on one or more shelves in the retail store, a is a scale parameter being=>0, and b is the space elasticity of the item category being 0<=b<=1;
13 . The system of claim 12 , wherein the space elasticity is calculated by fitting the constrained linear regress model on known data between the demand of the item category and the number of horizontal footage of the item category.
14 . The system of claim 11 , wherein the non-linear multiple regression model is derived from the constrained linear regression model by including horizontal footage allocations of other item categories in the retail store.
15 . The system of claim 14 , wherein the non-linear multiple regression model is solved using least absolute shrinkage and selection operator (LASSO) regression analysis method, such that over-fitting of the multiple regression model is avoided, and relatively important item categories which influence a demand of the item category.
16 . The system of claim 11 , wherein the constrained linear regression model further comprises parameters including a linear horizontal footage of the self and a number of the shelves.
17 . The system of claim 11 , wherein the constrained linear regression further comprises parameters including clusters of the retail store.
18 . The system of claim 11 , wherein the non-linear multiple-constraint mixed integer optimization model is solved using genetic algorithm
19 . The system of claim 11 , wherein the non-linear multiple-constraint mixed integer optimization model comprises
R
=
∑
i
=
1
n
(
p
i
α
i
(
x
i
L
*
S
)
b
i
∏
j
=
1
≠
i
n
(
x
j
L
*
S
)
g
ij
)
,
wherein:
p i is a unit price of an ith item category in the retail store, x i is category space allocation of the ith item category and is a positive number, L is a linear footage per shelf, S is a number of shelves in the retail store, a i , b i are parameters which are solved using the constrained linear regression model, g ij is a cross-space elasticity of the ith item category on a jth item category solved using the multiple regression model; and
lb i <=x i <=ub i in which lb i and ub i are respectively lower and upper bounds of category space allocation of the ith item category.
20 . The system of claim 19 , wherein the lower and upper bounds are derived from the business constraints of 1.5 case pack and 3.5 days of supply (DOS), and 1 DOS safety stock, to eliminate over-stock and out-of-stock situations:
l
b
i
=
⌈
max
(
1.5
CP
·
1
DOS
units
)
vf
i
df
i
⌉
,
ub
i
=
⌈
max
(
1.5
CP
·
4.5
DOS
units
)
vf
i
df
i
⌉
+
1
,
in which 1 DOS units=median of (historical) daily sales, vf i and df i are respectively vertical and depth footage of the ith item category for the shelf.Join the waitlist — get patent alerts
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