Abstract: State of the art approaches being used for material replenishment planning have the disadvantage that they fall short in addressing the realities of evolving demand mixes and product utilization. The technologies leverage inflexible statistical models which are tightly coupled to specific industries, business processes, and deeper assumptions and therefore the model and any optimization produce results where the quality of results is heavily curtailed as real business processes deviate from the model assumptions and unforeseen scenarios arise the business and the broader ecosystem evolve. The method and system disclosed in the embodiments herein facilitate generating simulation of various material replenishment scenarios based on a user input. The method and system further generates one or more recommendations for material replenishment, based on an optimization process carried out.

Abstract: A system and method for collaborative product configuration optimization model. A plurality of product families and corresponding one or more product sub-families, a plurality of options families associated with each of the sub-product families is obtained. The option family includes one or more option variants. A first product family from the plurality of product families is selected and the one or more option variants of the first product family is associated with a reference value and is selected based on availability of the one or more option variants. A feasible configuration set for the selected first product family is generated by iteratively performing, for each of a unit of demand, a multi-constrained longest path search traversing across the plurality of option families in an non-increasing order of an average of the reference value of the plurality of the option families and a list of configurations is optimized based on the generated feasible configuration.