Various embodiments of the teachings herein include an apparatus for optimizing production planning for production of a product, wherein workers are assigned to operate machines for producing the product. An example includes: an interface to import planning data for the production and competency information regarding machine operation and availability information of the workers; a first optimization module to determine an optimized percentage assignment of the workers to the respective machines for a defined time period based on the planning data, the competency information, and the availability information; a second optimization module to determine optimized production planning data, wherein an optimized time sequence of respective production steps is determined based on the planning data with respect to the workers and machines according to the percentage assignment and the number of workers needed for a production step; and an output module to distribute the optimized production planning data.

An example embodiment may involve: obtaining static data from work items of a process and dynamic data from event logs of the process; generating, from the static data and the dynamic data, a causal graph of dependencies between features of the process; providing, to a natural language model, representations of the causal graph and the dependencies; and obtaining, from the natural language model, indications of an inefficiency in the process.

An industry development state assessment device includes: an industry data acquisition module for acquiring time-series-based industry data of different industries; a dissipative structure model definition module for mapping a nonlinear mutual feedback relationship between industry development core elements, and for obtaining a dissipative structure state function of industry development; an industry development state value calculation module for sequentially substituting the time-series-based industry data of the different industries into the dissipative structure state function to obtain time-series-based data of industry development states of the different industries; and an assessment module for clustering the time-series-based data of the industry development states of the different industries to obtain at least one industry collection with similar evolutionary laws. The device can quantitatively describe the stability of the industry development process and improve the accuracy and reliability of the industry development assessment.

Methods, systems, and techniques for performing a multi-agent simulation. A first large language model (LLM) is prompted to act as a sales agent of a financial institution to generate a sales pitch. A second LLM is prompted to act as a client of the financial institution to engage in a conversation with the first LLM in response to the sales pitch. A third LLM is prompted to act as a judge to generate and output a score of the conversation between the first and second LLMs. The score is saved and/or output to a display. The multi-agent simulation is used to create a digital twin of an actual conversation between the sales agent and client.

Provided are techniques for resolving issues by building a re-playable simulated customer environment. Environment data of an original customer environment is stored in a data hub. The environment data from the data hub us used to create a simulated customer environment. The simulated customer environment is played. In response to the playing, one or more issues in the original customer environment are identified using the simulated customer environment and one or more solutions for the one or more issues are identified. A recommendation is provided with a solution of the one or more solutions for solving at least one of the issues in the original customer environment. Application of the recommendation to the original customer environment to resolve the at least one of the issues is automatically initiated.

Embodiments of the present disclosure provide a computer-implemented method. The computer-implemented method performed by a controller includes generating a plurality of virtual agents with Artificial Intelligence (AI) capabilities based at least on organizational data. Furthermore, the computer-implemented method includes delegating at least a portion of operational workflows to one or more of the plurality of virtual agents. The computer-implemented method further includes generating a plurality of employee digital twins associated with a plurality of respective employees, based at least on employee-specific data. Furthermore, the computer-implemented method includes generating at least one workplace digital twin representing at least one respective workplace environment. The computer-implemented method also includes enabling communication amongst a plurality of disparate groupings generated by selecting a plurality of entities from one or more of a plurality of employee devices, the plurality of virtual agents, the plurality of employee digital twins, or the at least one workplace digital twin.

A system and method is provided for accurately allocating tasks to workers on occurrence of a hazardous event in an industrial environment. The method includes executing, by the processing unit, simulated training session in computer simulated environment. The workers participate in training session via user devices. The method includes acquiring data from sensors associated with the workers. The sensors acquire data pertaining to performance of workers in training session. The method includes generating behavior matrix for workers in training session. The behavior matrix is designed based on a response of worker to hazardous event in training session. The method includes mapping workers to tasks defined in training session. The method includes allocating, tasks to each of the workers when hazardous event occurs in the real-world in industrial environment based on the mapping.

A system is provided. A system, including at least one computing device that is configured to determine a first set of input data associated with a business, where the first set of input data being associated with one of a plurality of predefined business sectors; select a model for analyzing data based at least on the first set of input data, where the model includes a plurality of model weights and variables associated with the one of the plurality of predefined business sectors; request, for a user via a user device, at least a third set of input data for use by the selected model, where the third set of input data being associated with the business and different from the first set of input data; analyze using the selected model.

A computer-implemented system and method for predicting, visualizing, and applying workplace strengths profiles are disclosed. The system includes a processor executing modules that: (i) map user-identified strengths and external data (e.g., LinkedIn, HR, team benchmarks) to one or more enneagram types; (ii) transform those types into prescriptive guidance, predictive insights, and adaptive developmental plans using machine-learning refinement; and (iii) generate dynamic visualizations of team strengths and what-if scenarios through a visualization analytics engine. The system continuously personalizes recommendations and action plans by learning from user data, organizational context, and proprietary prescriptive content.

A method may include receiving a data stream of complex data and receiving a type of a simulated organic life model and a type of a simulated environment. The method may include selecting a scenario for a simulation, parsing each variable in the data stream to a variable of the simulated organic life model or a variable of the simulated environment, and processing a simulation of the simulated organic life model in the simulated environment. The method may include altering one or more variables of the simulated organic life model based on one or more variables of the simulated environment, producing output data sets containing a continuum of data ranging from the data stream to predicted endpoint values for each data stream variable, and changing the simulated organic life model based on the altered one or more variables of the simulated organic life model.