Abstract: Embodiments provide systems and methods for predicting animal health of future generations of animals. Historical health and environmental data are collected and analyzed using machine learning to predict animal health of future generations and to understand which factors of health and environmental data affect animal health. Analysis also provides other insights relative to the field of animal husbandry such as anomaly detection.

Abstract: Embodiments provide systems and methods for predicting animal health of future generations of animals. Historical health and environmental data are collected and analyzed using machine learning to predict animal health of future generations and to understand which factors of health and environmental data affect animal health. Analysis also provides other insights relative to the field of animal husbandry such as anomaly detection.

Abstract: A common metrics manager analyzes customer feedback responses, and assigns a weighted priority to each feedback response. The weighted priority corresponds to a particular feedback response's impact on business goals, such as customer satisfaction. The common metrics manager uses weighted priority feedback responses to generate and rank a set of common metrics. The common metrics manager uses the common metrics to generate phase goals for each phase of a product's lifecycle. The number of phase goals for each product phase corresponds to the ranking of each particular common metric. Once a product ships to a customer which is developed using the common metrics, the customer sends feedback responses to the common metrics manager which the common metrics manager uses to generate new common metrics for use with a new product lifecycle.