LIFECYCLE MANAGEMENT ENGINE WITH AUTOMATED INTELLIGENCE
A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: estimating, using a machine learning model, a respective budget for expenditures based on respective parameters of each of one or more project initiatives associated with physical stores; determining, using a mixed integer linear programming formulation, a time window to execute the each of the one or more project initiatives; generating one or more respective recommendations for the each of the one or more project initiatives for a predetermined time period; and sending instructions to display the one or more respective recommendations on a graphical user interface, wherein the graphical user interface displays a respective status of each of the one or more respective recommendations. Other embodiments are disclosed.
1 . A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform:
estimating, using a machine learning model, a respective budget for expenditures based on respective parameters of each of one or more project initiatives associated with physical stores;
determining, using a mixed integer linear programming formulation, a time window to execute the each of the one or more project initiatives;
generating one or more respective recommendations for the each of the one or more project initiatives for a predetermined time period; and
sending instructions to display the one or more respective recommendations on a graphical user interface, wherein the graphical user interface displays a respective status of each of the one or more respective recommendations.
2 . The system of claim 1 , wherein the computing instructions are further configured to perform:
generating training data for the machine learning model, wherein the training data comprises historical expenditures associated with the respective parameters of the each of the one or more project initiatives within a historical time period;
determining, using the machine learning model, a cost estimate for a project initiative of the each of the one or more project initiatives based on the training data; and
iteratively updating the training data with the cost estimates for the each of the one or more project initiatives.
3 . The system of claim 1 , wherein estimating the respective budget for the expenditures comprises:
estimating a respective capital expenditure for the each of the one or more project initiatives; and
based on the respective capital expenditure, deriving respective tier expenditures for the each of the one or more project initiatives.
4 . The system of claim 3 , wherein the machine learning model comprises an ensemble of algorithms comprising two or more of: linear regression, linear mixed model, median based model, least absolute shrinkage and selection operator (LASSO), or k-nearest neighbor (KNN).
5 . The system of claim 1 , wherein the mixed integer linear programming formulation uses one or more constraints comprising one or more of:
a maximum number of total project initiatives;
a maximum number of project resources for the each of the one or more project initiatives;
a maximum number of project initiatives within each geographic region; or
a maximum number of project initiatives to begin each week.
6 . The system of claim 1 , wherein each of the one or more project initiatives is a remodel or a special project.
7 . The system of claim 1 , wherein the computing instructions are further configured to perform:
upon execution of a project initiative of the one or more project initiatives for a first physical store of the physical stores, calculating an impact metric of the project initiative on the first physical store compared to another impact metric on a sister physical store, using k-nearest-neighbors, and
transmitting feedback of the impact metric of the project initiative on the first physical store to a site selection model to be used as training data.
8 . The system of claim 7 , wherein calculating the impact metric of the project initiative comprises:
tracking performance metrics of the each of one or more project initiatives; and
determining a benchmark metric using control metrics comprising sister physical stores.
9 . The system of claim 7 , wherein the site selection model uses:
inputs comprising a disruption metric, a lift metric, or the respective budget for the expenditures;
constraints comprising a maximum of number of candidate physical stores within a geographical area; and
an objective function of scaled measures based on two decision drivers, wherein the two decision drivers comprise (i) a measure of profit and (ii) a measure of need of the project initiative.
10 . The system of claim 9 , wherein:
the disruption metric is based on data obtained during execution of the each of the one or more project initiatives; and
the lift metric is based on data obtained after execution of the each of the one or more project initiatives.
11 . A method being implemented via execution of computing instructions configured to run on one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
estimating, using a machine learning model, a respective budget for expenditures based on respective parameters of each of one or more project initiatives associated with physical stores;
determining, using a mixed integer linear programming formulation, a time window to execute the each of the one or more project initiatives;
generating one or more respective recommendations for the each of the one or more project initiatives for a predetermined time period; and
sending instructions to display the one or more respective recommendations on a graphical user interface, wherein the graphical user interface displays a respective status of each of the one or more respective recommendations.
12 . The method of claim 11 , further comprising:
generating training data for the machine learning model, wherein the training data comprises historical expenditures associated with the respective parameters of the each of the one or more project initiatives within a historical time period;
determining, using the machine learning model, a cost estimate for a project initiative of the each of the one or more project initiatives based on the training data; and
iteratively updating the training data with the cost estimates for the each of the one or more project initiatives.
13 . The method of claim 11 , wherein estimating the respective budget for the expenditures comprises:
estimating a respective capital expenditure for the each of the one or more project initiatives; and
based on the respective capital expenditure, deriving respective tier expenditures for the each of the one or more project initiatives.
14 . The method of claim 11 , wherein the machine learning model comprises an ensemble of algorithms comprising two or more of: linear regression, linear mixed model, median based model, least absolute shrinkage and selection operator (LASSO), or k-nearest neighbor (KNN).
15 . The method of claim 11 , wherein the mixed integer linear programming formulation uses one or more constraints comprising one or more of:
a maximum number of total project initiatives;
a maximum number of project resources for the each of the one or more project initiatives;
a maximum number of project initiatives within each geographic region; or
a maximum number of project initiatives to begin each week.
16 . The method of claim 11 , wherein each of the one or more project initiatives is a remodel or a special project.
17 . The method of claim 11 , further comprising:
upon execution of a project initiative of the one or more project initiatives for a first physical store of the physical stores, calculating an impact metric of the project initiative on the first physical store compared to another impact metric on a sister physical store, using k-nearest-neighbors, and
transmitting feedback of the impact metric of the project initiative on the first physical store to a site selection model to be used as training data.
18 . The method of claim 17 , wherein calculating the impact metric of the project initiative comprises:
tracking performance metrics of the each of one or more project initiatives; and
determining a benchmark metric using control metrics comprising sister physical stores.
19 . The method of claim 17 , wherein the site selection model uses:
inputs comprising a disruption metric, a lift metric, or the respective budget for the expenditures;
constraints comprising a maximum of number of candidate physical stores within a geographical area; and
an objective function of scaled measures based on two decision drivers, wherein the two decision drivers comprise (i) a measure of profit and (ii) a measure of need of the project initiative.
20 . The method of claim 19 , wherein:
the disruption metric is based on data obtained during execution of the each of the one or more project initiatives; and the lift metric is based on data obtained after execution of the each of the one or more project initiatives.