Abstract: Generation of an available to promise (ATP) value. A method identifies business constraints and goals for an omnichannel retailer based on input quantitative and qualitative data. The method determines numerical parameters for use in an executable optimization model and using a machine learning model. Additionally, the method builds the executable optimization model using at least one of the goals as a respective at least one objective in the optimization model, at least one of the constraints as a respective at least one constraint in the optimization model, and at least one of the numerical parameters as at least one additional parameter in the executable optimization model. In addition, the method executes the executable optimization model and generates an ATP value, and outputs the ATP value to an e-commerce system.

Abstract: Embodiments described herein provide approaches for detecting anomalies in electronic communications. Specifically, a set of response metrics is obtained from each of a plurality of communications. Each of the plurality of communications is part of a communications batch. Based on an aggregation of the response metric over the plurality of communications, a response metric value for one or more of the set of response metrics is computed. This response metric value is compared to a predicted metric value corresponding to the response metric. Based on this comparison, it is determined whether the response metric value is anomalous. If the response metric value is anomalous, a remediation activity is performed.

Abstract: Embodiments described herein provide approaches for detecting anomalies in electronic communications. Specifically, a set of response metrics is obtained from each of a plurality of communications. Each of the plurality of communications is part of a communications batch. Based on an aggregation of the response metric over the plurality of communications, a response metric value for one or more of the set of response metrics is computed. This response metric value is compared to a predicted metric value corresponding to the response metric. Based on this comparison, it is determined whether the response metric value is anomalous. If the response metric value is anomalous, a remediation activity is performed.