Abstract: The present disclosure relates to management of inventories and supply chains and, more particularly, to a predictive inventory management system and methods of use. The method of inventory management, includes: obtaining information associated with an item; predicting demand of the item based on the obtained information; determining whether additional inventory is required based on the predicted demand of the item; and obtaining additional inventory of the item when the current inventory level does not match the predicted demand.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with employment; performs iterative analysis on the data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted employment values for predefined geographic regions; converts the predicted employment values in the database into percentages of observed employment values for the predefined geographic regions over a specified time period to create indices of workforce elasticity for each geographic region; and rank orders the predefined geographic regions according to their indices of workforce elasticity.

Abstract: Systems and methods are described for dynamic and predictive pricing for ecommerce systems and brick-and-mortar retail businesses for a selected geographic location or territory. In one example, a system comprises a computing device that is configured to receive a request to display a network page of an item on a client device. The computing device is further configured to determine a geographic location of the client device and determine a price for the item using a machine learning model based at least in part on the geographic location. The network page is displayed on the client device to include the price of the item.

Abstract: Disclosed herein are systems and methods for predicting unemployment rates. A computing device may construct a predictive model based at least in part on an iterative analysis of a first set of sample data. The first set of sample data can comprise payroll data associated with a plurality of employees, data from at least one employment survey report, and potentially other data. The computing device can generate predicted employment data using the predictive model based at least in part on a second set of sample data. The computing device can generate a report based at least in part on the predicted employment data.

Abstract: The present disclosure relates generally to a forecasting tool and, more particularly, to a method, system and computer program product for forecasting (e.g., predicting) and reporting future environmental (e.g., climate) changes. A computer-implemented method includes: retrieving, by a computer system, employment data; aggregating, by the computer system, the employment data; injecting the aggregated employment data and climate components into a climate modeling application which analyzes the aggregated employment data and the climate components to generate a predictive climate model; and obtaining a report related to the predictive climate model.

Abstract: The present disclosure relates generally to managing infrastructure projects and, more particularly, to a computer-implemented method and system of indexing and predicting infrastructure project requirements. A computer-implemented method includes: aggregating, by a computer system, employment data; analyzing, by the computer system, the employment data to generate predicted infrastructure projects; and indexing, by the computer system, each of the predicted infrastructure projects for different geographic regions.

Abstract: A method, apparatus, system, and computer program product for creating a forecasting model for payroll records. Payroll records are received for a group of employers. The payroll records comprise granular data parameters about employees of the group of employers. A forecasting model is created the that aligns the payroll records to high-level employment data. Creating the forecasting model includes identifying predictor variables from the granular data parameters of the payroll records. Creating the forecasting model includes generating a set of basis functions from the predictor variables. Creating the forecasting model includes combining the set of basis functions to create the forecasting model.

Abstract: A method, apparatus, system, and computer program product for creating a forecasting model for payroll records. Payroll records are received for a group of employers. The payroll records comprise granular data parameters about employees of the group of employers. A forecasting model is created that aligns the payroll records to high-level employment data. Creating the forecasting model includes identifying predictor variables from the granular data parameters of the payroll records. Creating the forecasting model includes generating a set of basis functions from the predictor variables. Creating the forecasting model includes combining the set of basis functions to create the forecasting model.

Abstract: The present disclosure relates to management of inventories and supply chains and, more particularly, to a predictive inventory management system and methods of use. The method of inventory management, includes: obtaining information associated with an item; predicting demand of the item based on the obtained information; determining whether additional inventory is required based on the predicted demand of the item; and obtaining additional inventory of the item when the current inventory level does not match the predicted demand.

Abstract: A method, computer system, and computer program product for on-demand job messaging to predicted employees. A machine intelligence application compares data for an employee to a job change interest model. Responsive to comparing the data for the employee to the job change interest model, the machine intelligence application determines whether the employee is a predicted employee. The predicted employee enables improved targeting of job messaging by a job messaging application in a computer system.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with income and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of average income for a selected set of predefined geographic regions; converts the predicted values of average income in the database into percentages of observed values of average income for geographic regions within the selected set over a specified time period to create indices of average income; and rank orders the regions within the selected set according to their indices of average income.

Abstract: A method, apparatus, and computer program product for digitally presenting statistically-relevant business insights into a set of business metrics for an organization. A computer system identifies a set of organizational characteristics from human resources data of employees of a plurality of organizations, and applies a selected inclusion criteria to the set of organizational characteristics to identify a set of candidate organizations. The computer system identifies a set of benchmark organizations from the set of candidate organizations, and creates a fixed panel of the benchmark organizations. The computer system applies the fixed panel to the human resources data of employees of a plurality of organizations to create an analysis dataset that consists of human resources data of employees of the benchmark organizations. The computer system generates a business insight into the set of business metrics of the organization based on the analysis dataset, and digitally presents the business insight.

Abstract: A method for generating a communicable disease policy plan by using machine learning. The process identifies a number of workplace policies for a number of business entities. The workplace policies comprise a number of dimensions of data collected from a number of sources. The process collects employment data for each of the business entities. The employment data includes sick leave data about employees of the plurality of business entities. The process determines metrics for the sick leave data during a given time interval; simultaneously models the workplace policies and the metrics for the sick leave data to identify correlations among the number of dimensions of data and generalize rules for predicting effective workplace policies; predicts a number of effective workplace policies for a particular business entity; and generates a communicable disease policy plan for the particular business entity based on the number of effective workplace policies.

Abstract: A method for generating a communicable disease policy plan by using machine learning. The process identifies a number of workplace policies for a number of business entities. The workplace policies comprise a number of dimensions of data collected from a number of sources. The process collects employment data for each of the business entities. The employment data includes sick leave data about employees of the plurality of business entities. The process determines metrics for the sick leave data during a given time interval; simultaneously models the workplace policies and the metrics for the sick leave data to identify correlations among the number of dimensions of data and generalize rules for predicting effective workplace policies; predicts a number of effective workplace policies for a particular business entity; and generates a communicable disease policy plan for the particular business entity based on the number of effective workplace policies.

Abstract: A method, apparatus, system, and computer program product for creating a forecasting model for payroll records. Payroll records are received for a group of employers. The payroll records comprise granular data parameters about employees of the group of employers. A forecasting model is created the that aligns the payroll records to high-level employment data. Creating the forecasting model includes identifying predictor variables from the granular data parameters of the payroll records. Creating the forecasting model includes generating a set of basis functions from the predictor variables. Creating the forecasting model includes combining the set of basis functions to create the forecasting model.

Abstract: A method, a computer system, and a computer program product for managing inventory for an organization. The computer system identifies human resources data regarding employees of organizations. The human resources data comprises geolocation data based at least partially on a geolocation of the organization and geolocations of the employees. The computer system determines a change in market dynamics relating to a set of geographic regions based at least partially on the human resources data. The computer system identifies transaction data for transactions involving a business merchant within a particular geographic region and a consumer. The computer system generates a suggested inventory for the business merchant. The suggested inventory is based at least partially on the determined change in market dynamics for the set of geographic regions in the transaction data for the business merchant.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with employment and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of real estate demand for a selected set of predefined geographic regions; converts the predicted values of real estate demand in the database into percentages of observed values of real estate demand for geographic regions within the selected set over a specified time period to create indices of real estate demand; and rank orders the geographic regions within the selected set according to their indices of real estate demand.

Abstract: A method, computer system, and computer program product that aggregates data regarding a plurality of factors correlated with income; performs iterative analysis on the data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted income values for a selected range of housing costs; converts the predicted income values in the database into percentages of observed income values for a selected group of people within the selected range of housing costs over a specified time period to create indices of financial stress; and rank orders the people within the selected group according to their indices of financial stress.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with income and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of encountered income for a selected set of predefined signage locations; converts the predicted values of encountered income in the database into percentages of observed values of encountered income for signage locations within the selected set over a specified time period to create indices of encountered income; and rank orders the signage locations within the selected set according to their indices of encountered income.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with employment and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of real estate demand for a selected set of predefined geographic regions; converts the predicted values of real estate demand in the database into percentages of observed values of real estate demand for geographic regions within the selected set over a specified time period to create indices of real estate demand; and rank orders the geographic regions within the selected set according to their indices of real estate demand.