Abstract: System and method for early detection of vehicle parts failure are disclosed. The method includes identifying discriminative rules from unstructured and structured data corresponding to subsystems of a vehicle. Causal parts categories are mapped to the subsystems based on the discriminative rules to obtain a plurality of causal part-subsystem pairs. The causal part categories are representative of vehicle parts responsible for failure of corresponding subsystems. Scores are assigned to the causal part-subsystem pairs based on an occurrence of causal part categories to a corresponding subsystem within a source. An emerging issue score is computed based on the scores, a corresponding weightage associated with the sources of the causal part category, and an extent of coverage of the each causal part in each of the plurality of causal part-subsystem pairs. The emerging issue score is compared with the threshold vehicle part failure score to identify causal part categories associated with vehicle parts failure.

Abstract: System and method for early detection of vehicle parts failure are disclosed. The method includes identifying discriminative rules from unstructured and structured data corresponding to subsystems of a vehicle. Causal parts categories are mapped to the subsystems based on the discriminative rules to obtain a plurality of causal part-subsystem pairs. The causal part categories are representative of vehicle parts responsible for failure of corresponding subsystems. Scores are assigned to the causal part-subsystem pairs based on an occurrence of causal part categories to a corresponding subsystem within a source. An emerging issue score is computed based on the scores, a corresponding weightage associated with the sources of the causal part category, and an extent of coverage of the each causal part in each of the plurality of causal part-subsystem pairs. The emerging issue score is compared with the threshold vehicle part failure score to identify causal part categories associated with vehicle parts failure.

Abstract: A method and system is provided for mitigating risk in a supply chain. The present application provides a method and system for mitigating risk in a multi echelon stochastic flexible supply chain, comprises categorizing a plurality of supply chain risks pertaining into a plurality of supply chain risk sub categories; developing a risk decision model for each of the plurality of supply chain risk sub categories; extracting supply chain risk information from a plurality of information sources including a plurality of social media information sources; validating, customizing and estimating social media risk score for each of the plurality of supply chain risk sub categories; integrating estimated social media risk score for each of the plurality of supply chain risk sub categories for each supply chain member; and utilizing consolidated social media risk score in the developed risk decision model for mitigating risk.

Abstract: This disclosure relates generally to computation of credit scores, and more particularly to methods and devices for computing optimized credit scores. A computer implemented method for computing optimized credit scores comprises transmitting a data retrieval request to a data source for retrieving profile data associated with a plurality of individuals. Availability of social media data for each individual is ascertained based on corresponding profile data. Each of the plurality of individuals is classified into a first set and second set based on the availability of the social media data associated with the individual. A social media score for each individual present in the first set is determined. A social media score for each individual present in the second set is determined based on social media scores of the first set. A credit score for each individual is determined based on corresponding social media score of the individual.

Abstract: Disclosed is a method and system for identifying an industry specific e-maven by analyzing textual data from social media. The system may collect textual data posted by users on social media. The system may then determine characteristics of the users by analyzing the textual data. The system may further calculate a maven score for the users by using the characteristics. The system may compute an industrial maven score for the users by using the maven scores and an industry adjustment factor. An industry specific e-maven may thus be identified based on the industry adjustment factor.

Abstract: Methods and systems for optimizing inventory management by forecasting Stock Keeping Units (SKUs) to be supplied by a consumer packaged goods (CPG) manufacturer to a retailer are provided. A data receiving module receives marketing data, point of sale data, social-media data, and miscellaneous data from one or more data sources. An analysis module, analyzes the social-media data, computes a social-media score. The analysis module generates one or more variables from the marketing data, the point of sale data and the miscellaneous data. The analysis module transforms the one or more variables and the social-media score into one or more transformed variables. A forecasting module forecasts a demand of the SKUs. The forecasting module determines a probability of the OOS and a replenishment time in order to obtain an optimal solution indicating the SKUs to be supplied by the CPG manufacturer to the retailer.