Abstract: A device may receive scenario data identifying a scenario for enterprise resource planning that includes a base solution and processes and may generate an output file based on the scenario data. The device may generate a hierarchy for the scenario based on the output file and may identify prerequisites for the scenario based on the output file. The device may generate configurations and test scripts for the scenario based on the hierarchy and the prerequisites and may retrieve, from a data structure associated with the device, master data associated with the configurations and the test scripts. The device may process the configurations, the test scripts, and the master data, with a machine learning model, to predict a risk severity associated with the scenario and may perform one or more actions based on the risk severity associated with the scenario.

Abstract: A system for value prediction for dynamic performance optimization includes a project value predictor that receives a Key Performance Indicator (KPI) and an initiative relating to an active project having a closure date. The KPI is associated with a KPI period including multiple intervals. The project value predictor operates to identify a relevant cluster of KPIs for the KPI based on historical data, forecast a future value of the KPI based on attributes/features of the KPI relative to the closure date, predict a possibility of failure of the KPI using a trained data model to pre-classify the KPI, categorize the KPI based on the future value or the pre-classification, where the KPI is categorized as failure based on the future value being less than a target KPI value after the KPI period and added to list for retraining the model based on the categorization. The system also leads to the identification and subsequent validation of Initiatives that impact the KPIs with quantification of the level of impact.

Abstract: A system for value prediction for dynamic performance optimization includes a project value predictor that receives a Key Performance Indicator (KPI) and an initiative relating to an active project having a closure date. The KPI is associated with a KPI period including multiple intervals. The project value predictor operates to identify a relevant cluster of KPIs for the KPI based on historical data, forecast a future value of the KPI based on attributes/features of the KPI relative to the closure date, predict a possibility of failure of the KPI using a trained data model to pre-classify the KPI, categorize the KPI based on the future value or the pre-classification, where the KPI is categorized as failure based on the future value being less than a target KPI value after the KPI period and added to list for retraining the model based on the categorization. The system also leads to the identification and subsequent validation of Initiatives that impact the KPIs with quantification of the level of impact.

Abstract: A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.

Abstract: A device may receive source data identifying source enterprise software and may process the source data to determine field mapping data. The device may process the source data, with a first model, to determine implementation guide data identifying implementation guides, and the device may convert data configurations from the source data to target data configurations. The device may generate transport request management data for the target enterprise software based on the source data and the implementation guide data and may process the source data and the implementation guide data, with a second model, to determine copy functionality data identifying functionality of the source enterprise software to be copied. The device may process the source data, with a third model, to determine consolidation data identifying the source data to be consolidated and may configure the target enterprise software, based on the determined data, to generate configured target enterprise software.

Abstract: A device may receive a request to add content relating to a technology development project that is managed by a content management system. The device may convert a document associated with the content from a first format to a second format. The device may generate document information for the document that includes: document section information, intra-document relationship information, and/or inter-document relationship information. The device may generate copies of the content that are in formats that are different than the first format. The copies may be associated with replicated section information that corresponds to the document section information for the document. The device may provide the content and the copies to publishing platforms to cause the publishing platforms to make the content and the copies available to other devices to allow the other devices to access the content and the set of copies via different publishing platforms and in different formats.

Abstract: A device may receive a request to add content relating to a technology development project that is managed by a content management system. The device may convert a document associated with the content from a first format to a second format. The device may generate document information for the document that includes: document section information, intra-document relationship information, and/or inter-document relationship information. The device may generate copies of the content that are in formats that are different than the first format. The copies may be associated with replicated section information that corresponds to the document section information for the document. The device may provide the content and the copies to publishing platforms to cause the publishing platforms to make the content and the copies available to other devices to allow the other devices to access the content and the set of copies via different publishing platforms and in different formats.

Abstract: A device may receive a request for a service management plan that is to be used to implement a fully-integrated enterprise resource planning (ERP) system for an organization. The device may receive organizational data associated with the organization. The device may determine, based on the organizational data, observations that serve as hypotheses for deficiencies associated with a current state of a system of the organization. The device may identify, based on the observations, priorities that define a target state for the fully-integrated ERP system that is to be generated. The device may select, based on a machine-learning-driven analysis of the observations and the priorities, a recommendation for a configuration of the fully-integrated ERP system. The device may generate, based on the recommendation, the service management plan for the organization. The device may perform a set of actions to cause the configuration to be implemented.

Abstract: A device may receive a request for a service management plan that is to be used to implement a fully-integrated enterprise resource planning (ERP) system for an organization. The device may receive organizational data associated with the organization. The device may determine, based on the organizational data, observations that serve as hypotheses for deficiencies associated with a current state of a system of the organization. The device may identify, based on the observations, priorities that define a target state for the fully-integrated ERP system that is to be generated. The device may select, based on a machine-learning-driven analysis of the observations and the priorities, a recommendation for a configuration of the fully-integrated ERP system. The device may generate, based on the recommendation, the service management plan for the organization. The device may perform a set of actions to cause the configuration to be implemented.