In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.

A method for assisting manufacturing outsourcing from a manufacturer to a processor of a component is disclosed. The method includes receiving, from a manufacturer terminal of the manufacturer, two-dimensional data expressing the component in a two-dimensional manner. The method includes extracting designation information from the two-dimensional data. The method includes determining outsourcing requirements corresponding to the designation information. The method includes adding, to the two-dimensional data, additional data corresponding to at least part of the outsourcing requirements. The method includes transmitting, to a processor terminal of the processor, the two-dimensional data to which the additional data is added.

A management method for an electric vehicle charging station system. The charging station system includes: several charging stations, each equipped with several charging terminals and one local manager able to pilot the charging terminals; and a main manager able to drive the charging stations. The method includes: the local manager of each charging station getting information associated with vehicles connected to the charging terminals and determining an optimization proposal for electric energy transfer at the level of the charging station; the main manager allocating electric power each charging station on the basis of the optimization proposals; and the local manager of each charging station driving charging terminals based on the allocated power.

A method, system, and medium for automated analysis of business intelligence each: receive natural language input from a user; evaluate, via a natural language understanding processor that includes a parser and an interpreter, the natural language input to determine an intent of the user; determine the intent of the user and generate a query based on a context manager; send an identification of the failure to a failure analysis system for human intervened analysis and refinement of a natural language model used by the natural language understand processor; assess, via a context manager processor, to determine a user interest in one or more portions of results of the query, a scrolling of the user through the results of the query; and refine, based on the user interest in the one or more portions of the results of the query, an output of the results of the query.

Systems and apparatuses include one or more processing circuits comprising one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to: receive vehicle information including operating conditions and historical vehicle information, develop a vehicle model using a machine learning engine that receives the vehicle information, determine a fleet failure probability based on the vehicle model and fleet information including fleet usage and fleet vehicle types, and determine a predictive maintenance schedule based on the fleet failure probability.

A method for optimizing production of sheet-metal parts, the production comprising cutting out and singularizing the sheet-metal parts and bending the sheet-metal parts, wherein the method includes: (A) training a neural network, which is executed on a Monte Carlo tree search framework, by means of supervised learning and self-play with reinforcement learning; (B) recording constraints for the sheet-metal parts, the constraints comprising geometric data of the sheet-metal parts; (C) creating an optimized production schedule by way of the neural network; and (D) outputting the production schedule.

Various embodiments described herein relate to performance assurance modeling for a portfolio of assets. In this regard, a request to generate one or more performance assurance insights related to one or more assets is received. The request includes a fault descriptor describing one or more faults associated with the one or more assets. In response to the request, a first risk level associated with the one or more faults is determined based on the fault descriptor and asset data associated with the one or more assets. Additionally, in response to the request, a second risk level associated with the one or more faults is generated based on one or more predetermined relationships between faults and asset performance indicator thresholds. The one or more performance assurance insights are then generated based on a comparison between the first risk level and the second risk level.

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. An embodiment of the present invention is configured to chronologically store log data in a first storage medium, and store attribute information indicating a relevance between a type of a failure expected to occur in a facility and each of a plurality of data users; The embodiment is further configured to select, at the occurrence of a failure in the facility, a data user who has a relevance to a type of the failure from among the plurality of data users based on the attribute information, selectively read log data relating to an operation state of the facility in which the failure has occurred, and transmits the read log data to the selected data user.

A dark web monitoring, analysis and alert system comprising a data receiving module configured to receive data collected from the dark web and structured; a Structured Data Database (SDD) connected with the data receiving module, the SDD configured to store the structured data; a Text Search and Analytic Engine (TSAE) connected with the SDD, the TSAE configured to enable advanced search and basic analysis in the structured data; a Knowledge Deduction Service (KDS) connected with the TSAE, the KDS configured to deeply analyze the collected data; the deep analysis comprises extracting insights regarding dark web surfers behavioral patterns and interactions; a Structured Knowledge Database (SKD) connected with the KDS, the SKD configured to store the deep analysis results; and an Alert Service connected with the TSAE and the SKD, the Alert Service configured to provide prioritized alerts based on the deep analysis.

A system and method may be provided to predict a value of a field of interest about a patient procedure. Data may be received from a health provider. A statistical model or machine learning model may be built based on the data in order to predict the value of the field of interest. In some embodiments, a plurality of models are used to predict different aspects of the procedure and are combined by a main model.