Abstract: 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.

Abstract: An iterative, tiered system for identifying which assets need to be remodeled. This tiered system uses a Bayesian Structural Time Series, followed by an ensemble classification and cost estimation. The results are then input into an optimization model, where the best possible set of stores is selected according to the constraints. Remodeling of the store then commences.

Abstract: An iterative, tiered system for identifying which assets need to be remodeled. This tiered system uses a Bayesian Structural Time Series, followed by an ensemble classification and cost estimation. The results are then input into an optimization model, where the best possible set of stores is selected according to the constraints. Remodeling of the store then commences.