Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support optimized product design processes. During the design process, information identifying a set of features for a product design are received and evaluated against machine learning logic to identify a set of components that includes components corresponding to the set of features. One or more candidate components may be identified as alternatives to one or more set of components based on the characteristics, and modifications to optimize (e.g., reduce cost, weight, etc.) the set of components may be determined based on at least one design metric and the one or more candidate components. A final set of components that are optimized with respect to the at least one design metric may be output.

Abstract: A new product development system may provide, to one or more client devices and server devices, one or more tools to enable development of a new product. The new product development system may receive current project data identifying a current project for developing the new product, historical project data identifying historical projects for developing historical products, and industry data associated with the new product. The new product development system may process the current project data, the historical project data, and the industry data, with a machine learning model, to identify one or more variables for a predictive model and may process the one or more variables, with the predictive model, to predict a predicted success rate of the current project. The new product development system may perform one or more actions based on the predicted success rate of the current project.

Abstract: Aspects of the present disclosure provide methods, devices, and computer-readable storage media that support adaptive scheduling of electric vehicles (EVs) of an EV fleet for order deliveries. In some implementations, one or more aspects of the adaptive EV scheduling may be customized for EVs. For example, the adaptive EV scheduling may include identifying an energy efficient route that also reduces stress on a battery of an EV and may be based at least in part on a charging parameter associated with the EV. In some examples, the charging parameter may include one or more of a state of charge (SOC) associated with the battery, a state of health (SOH) associated with the battery, a location of a charging station for the EV, an average charging duration associated with the EV, or an intelligent charging parameter associated with the EV.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support optimized product design processes. During the design process, information identifying a set of features for a product design are received and evaluated against machine learning logic to identify a set of components that includes components corresponding to the set of features. One or more candidate components may be identified as alternatives to one or more set of components based on the characteristics, and modifications to optimize (e.g., reduce cost, weight, etc.) the set of components may be determined based on at least one design metric and the one or more candidate components. A final set of components that are optimized with respect to the at least one design metric may be output.

Abstract: A new product development system may provide, to one or more client devices and server devices, one or more tools to enable development of a new product. The new product development system may receive current project data identifying a current project for developing the new product, historical project data identifying historical projects for developing historical products, and industry data associated with the new product. The new product development system may process the current project data, the historical project data, and the industry data, with a machine learning model, to identify one or more variables for a predictive model and may process the one or more variables, with the predictive model, to predict a predicted success rate of the current project. The new product development system may perform one or more actions based on the predicted success rate of the current project.