Abstract: The present invention relates to a computer implemented method of determining optimal business metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The computer implemented method comprising the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, and creating a product mix ranking based, in part, on prior sales of each of the product type. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then use to inform changes to make to the product mix to achieve the desired OPTIMAL BUSINESS METRIC.

Abstract: The present invention relates to a method of determining a maximum days-on-shelf metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The method comprises the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, creating a product mix ranking based, in part, on prior sales of each of the product type. The method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then used to determine the MAXIMUM DAYS-ON-SHELF, which is the number of days before an out of stock condition of a product type SKU occurs.

Abstract: The present invention relates to a computer implemented method of determining the cost of implementing a pricing strategy, the pricing strategy formed by changeable business rules which engender algorithmic constraints, impacting optimized business metrics. The computer implemented method comprising the steps of defining a cohort of at least one of a boundary constrained shelf space and associating with each of the boundary constrained shelf space, in the cohort, a product mix, and a product mix ranking. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable MEMBER OPTIMAL SALES REVENUE AMOUNT performance absent pricing strategy business rules and MEMBER OPPORTUNITY PRICING STRATEGY SALES REVENUE AMOUNT inclusive of pricing strategy business rules.

Abstract: The present invention relates to a computer implemented method of determining optimal business metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The computer implemented method comprising the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, and creating a product mix ranking based, in part, on prior sales of each of the product type. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then use to inform changes to make to the product mix to achieve the desired OPTIMAL BUSINESS METRIC.

Abstract: The present invention relates to a computer implemented method of determining optimal business metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The computer implemented method comprising the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, and creating a product mix ranking based, in part, on prior sales of each of the product type. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then use to inform changes to make to the product mix to achieve the desired OPTIMAL BUSINESS METRIC.

Abstract: The present invention relates to a method of determining a maximum days-on-shelf metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The method comprises the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, creating a product mix ranking based, in part, on prior sales of each of the product type. The method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then used to determine the MAXIMUM DAYS-ON-SHELF, which is the number of days before an out of stock condition of a product type SKU occurs.

Abstract: The present invention relates to a computer implemented method of determining optimal business metrics from a product mix constrained by at least physical shelf space and selectively by at least one business rule. The computer implemented method comprising the steps of defining a boundary constrained shelf space, placing, physically, a product mix within the boundary constrained shelf space, and creating a product mix ranking based, in part, on prior sales of each of the product type. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable business metric performance of the boundary constrained shelf space. In this regard, a group of similar product mix/ranking is optimized to create an ideal product mix/ranking which is then use to inform changes to make to the product mix to achieve the desired OPTIMAL BUSINESS METRIC.

Abstract: The present invention relates to a computer implemented method of determining the cost of implementing a pricing strategy, the pricing strategy formed by changeable business rules which engender algorithmic constraints, impacting optimized business metrics. The computer implemented method comprising the steps of defining a cohort of at least one of a boundary constrained shelf space and associating with each of the boundary constrained shelf space, in the cohort, a product mix, and a product mix ranking. The computer implemented method continues by using a data processing device to develop, through algorithmic autonomous learning, achievable MEMBER OPTIMAL SALES REVENUE AMOUNT performance absent pricing strategy business rules and MEMBER OPPORTUNITY PRICING STRATEGY SALES REVENUE AMOUNT inclusive of pricing strategy business rules.