Abstract: In an example embodiment, a recommendation engine provides recommendations as to how decision-making units (DMUs) can improve efficiency, or savings can utilize machine learning algorithms and data envelopment analysis (DEA). DEA is a linear programming methodology, and is used in the example embodiment to identify one or more key performance indices (KPIs) that are most important to a DMU.

Abstract: A method may include applying, to various factors contributing to a sentiment that an end user exhibits towards an enterprise software application, a first machine learning model trained to determine, based on the factors, a sentiment index indicating the sentiment that the end user exhibits towards the enterprise software application. In response to the sentiment index exceeding a threshold value, a second machine learning model may be applied to identify remedial actions for addressing one or more of the factors contributing to the sentiment of the end user. A user interface may be generated to display, at a client device, a recommendation including the remedial actions. The remedial actions may be prioritized based on how much each corresponding factor contribute to the sentiment of the end user. Related systems and articles of manufacture are also provided.

Abstract: In an example embodiment, a recommendation engine provides recommendations as to how decision-making units (DMUs) can improve efficiency, or savings can utilize machine learning algorithms and data envelopment analysis (DEA). DEA is a linear programming methodology, and is used in the example embodiment to identify one or more key performance indices (KPIs) that are most important to a DMU.

Abstract: A method may include applying, to various factors contributing to a sentiment that an end user exhibits towards an enterprise software application, a first machine learning model trained to determine, based on the factors, a sentiment index indicating the sentiment that the end user exhibits towards the enterprise software application. In response to the sentiment index exceeding a threshold value, a second machine learning model may be applied to identify remedial actions for addressing one or more of the factors contributing to the sentiment of the end user. A user interface may be generated to display, at a client device, a recommendation including the remedial actions. The remedial actions may be prioritized based on how much each corresponding factor contribute to the sentiment of the end user. Related systems and articles of manufacture are also provided.