Abstract: A computer system comprising of an orchestration engine that allows for optimizing of investment allocation across plural levers, the orchestration engine configured to: receive input data that includes historic data regarding pricing and sales according to plural levers; prepare the received input data by converting the plural levers in the received input data into a common currency; calculate historical return on investment based on the common currency; simulate return on investment using the calculated historical return on investment and a machine learning model of a given campaign based on historical sales, campaign definitions, and investment metrics; optimize the simulated return on investment from the campaign with respect to one or more optimization goals and constraints; and output from the optimizer an optimized, campaign plan.

Abstract: Described is a markdown engine that allows for optimizing of an allocation of markdowns across plural levers. The markdown engine including instructions that configured a computer system to receive input data that includes historical and markdown scope information about the products and store, price information as well as stock, stock-out information, prepare the received input data into data structures. cluster the data structures according to product metrics into one or more clusters. For each of the one or more clusters the computer system is configured to determine a demand forecast according to a markdown plan for the one or more clusters, optimize the markdown plan with respect to one or more optimization goals and constraints for the one or more clusters, and output an optimized recommended set of markdowns for the one or more clusters.