Strategic macro space recommendation

A computer-implemented method for analyzing a retail space allocation, comprising:

• receiving, by a computer, product data from automated machinery having at least one sensor, wherein the product data corresponds to one or more products detected by the at least one sensor of the automated machinery;
• analyzing, by the computer, a space elasticity for a retail entity comprising a product display area, the product display area comprising the one or more products and one or more product facings arranged in a first display configuration, wherein the analyzing is based, at least in part, on a machine learning model, and wherein the machine learning model comprises one or more logarithmic functions;
• generating, by the computer, a space allocation recommendation based, at least in part, on a summation of ratios of a product category evaluation metric to a historical value for each product category, wherein the space allocation recommendation is subject to at least one constraint;
• generating, by the computer, a placement recommendation based, at least in part, on the generated space allocation recommendation;
• and
• rearranging, by automated robotic shelving machinery, the one or more products and the one or more product facings into a second display configuration, based, at least in part, on the generated space allocation recommendation and the generated placement recommendation.

A non-transitory computer-readable medium embodied with software for analyzing a retail space allocation, the software when executed is configured to:

• receive product data from automated machinery having at least one sensor, wherein the product data corresponds to one or more products detected by the sensor of the automated machinery;
• analyze a space elasticity for a retail entity comprising a product display area, the product display area comprising the one or more products and one or more product facings arranged in a first display configuration, wherein the analyzing is based, at least in part, on a machine learning model, and wherein the machine learning model comprises one or more logarithmic functions;
• generate a space allocation recommendation based, at least in part, on a summation of ratios of a product category evaluation metric to a historical value for each product category, wherein the space allocation recommendation is subject to at least one constraint;
• generate a placement recommendation based, at least in part, on the generated space allocation recommendation;
• and
• rearrange, by automated robotic shelving machinery, the one or more products and the one or more product facings into a second display configuration, based, at least in part, on the generated space allocation recommendation and the generated placement recommendation.