Intelligent scoring systems and methods for scoring a level of performance of a business unit of a plurality of business units in an organization

A method for intelligent assessment of a business unit of a plurality of business units of an organization, the method comprising:

• receiving with one or more processors of an intelligent scoring system, a parameter rating for a scorecard of the business unit for each of a first portion of a plurality of business performance parameters;
• automatically inputting the parameter rating for the scorecard of the business unit for each of a second portion of the plurality of business performance parameters;
• automatically determining, via a neural network model, a weighting associated with each parameter rating;
• automatically estimating, via the neural network model, a parameter outcome score for each of the first portion and the second portion of the plurality of business performance parameters based on each respective parameter rating and each associated weighting;
• automatically estimating, via the neural network model, an overall outcome score indicating a level of performance of the business unit of the plurality of business units of the organization based on each parameter outcome score;
• automatically generating one or more recommendations for improving the level of performance of the business unit based on the overall outcome score;
• automatically displaying one or more portions of the scorecard including the weighting applied to each parameter rating as an indicator of an impact of each parameter to the overall outcome score;
• storing, to a training data server, at least one of the parameter rating, the weighting, the parameter outcome score, the overall outcome score, and/or the one or more recommendations as a historical parameter rating, a historical weighting, a historical parameter outcome score, a historical overall outcome score, and/or a historical recommendation;
• updating the neural network model by retraining the neural network model with the historical parameter rating, the historical weighting, the historical parameter outcome score, the historical overall outcome score, and/or the historical recommendation;
• adjusting the weighting, via the neural network model, of each parameter rating;
• and
• generating one or more micro-organizational scores for one or more subgroups of the business unit, wherein the one or more recommendations for improving the level of performance of the business unit comprises a recommendation to at least one micro-organizational entity including a first sub group selected from the one or more subgroups and focused on a product or a geographic location.

A method for intelligent assessment of a business unit of a plurality of business units of an organization, the method comprising:

• retrieving, with one or more processors, business data stored on one or more memory components communicatively coupled to the one or more processors;
• automatically generating, via a neural network model, a rating parameter for one or more business performance parameters based the business data;
• automatically determining, via the neural network model, a weighting associated with each parameter rating;
• automatically estimating, via the neural network model, a parameter outcome score for each of the one or more business performance parameters based on each respective parameter rating and each associated weighting;
• automatically estimating, via the neural network model, an overall outcome score indicating a level of performance of the business unit of the plurality of business units of the organization based on each parameter outcome score;
• automatically displaying a scorecard including the rating parameter, the weighting applied to each parameter rating, the parameter outcome score and the overall outcome score as an indicator of an impact of each parameter to the overall outcome score;
• storing, to a training data server, at least one of the parameter rating, the weighting, the parameter outcome score, and/or the overall outcome score as a historical parameter rating, a historical weighting, a historical parameter outcome score, and/or a historical overall outcome score;
• updating the neural network model by retraining the neural network model with the historical parameter rating, the historical weighting, the historical parameter outcome score, and/or the historical overall outcome score;
• adjusting the weighting, via the neural network model, of each parameter rating;
• automatically generating, via a neural network recommendation model, one or more recommendations for improving the level of performance of the business unit;
• and
• generating one or more micro-organizational scores for one or more subgroups of the business unit, wherein the one or more recommendations for improving the level of performance of the business unit comprises a recommendation to at least one micro-organizational entity including a first sub group selected from the one or more subgroups and focused on a product or a geographic location.