Methods, apparatus, systems, and computer program products are disclosed for utilizing specially configured machine learning models to generate incremental currency value(s) associated with one or more target merchant data objects. Some embodiments, based on one or more market record sets, identify an actual electronic currency value for a total merchant data object set, and include a counterfactual model configured to generate a counterfactual electronic currency value for use in determining a counterfactual incremental electronic currency impact, and in some embodiments for ranking other models. Embodiments, additionally or alternatively, include an incrementality-trained ensemble model for generating a predictive incremental electronic currency impact. The incrementality-trained ensemble model may be trained to predict based on the rankings of the outputs of the counterfactual model. Embodiments may further rank target merchant data objects and perform one or more additional actions, including assigning the target merchant data objects to sales account data structures for management.

The proposed G-NOA framework that is based on the goals set by the customer, accepts an input configuration with all necessary details from the customer. This framework supports multi-tenancy in a customer-centric fashion to facilitate modules of various businesses. The framework also has the capability of working according to a specific module of an organization and recommends the suitable NOA for that module. This is performed using the proposed Time-Effective Reinforcement Learning (TE-RL) model of the relevant module. The enhanced version of the TE-RL model namely Enhanced TE-RL helps in defining the state with multiple dimensions and in using ANN for predicting transition probabilities of states. The TE-RL model and the Enhanced TE-RL model are defined with time effective parameters like Time_Sliced_State (TSS), Enhanced-Time_Sliced_State (E-TSS) and Time_Sensitive_Action (TSA) for precise and accurate NOA recommendation. The model performs appropriate policy estimation and policy tuning using TSS, E-TSS and TSA parameters.

The proposed G-NOA framework that is based on the goals set by the customer, accepts an input configuration with all necessary details from the customer. This framework supports multi-tenancy in a customer-centric fashion to facilitate modules of various businesses. The framework also has the capability of working according to a specific module of an organization and recommends the suitable NOA for that module. This is performed using the proposed Time-Effective Reinforcement Learning (TE-RL) model of the relevant module. The enhanced version of the TE-RL model namely Enhanced TE-RL helps in defining the state with multiple dimensions and in using ANN for predicting transition probabilities of states. The TE-RL model and the Enhanced TE-RL model are defined with time effective parameters like Time_Sliced_State (TSS), Enhanced-Time_Sliced_State (E-TSS) and Time_Sensitive_Action (TSA) for precise and accurate NOA recommendation. The model performs appropriate policy estimation and policy tuning using TSS, E-TSS and TSA parameters.

In an example, a cloud service management node includes a knowledge base having a plurality of billing rules for a cloud computing environment, a processor, and a memory coupled to the processor. The memory may include a discount predictor module to receive an actual bill related to consumption of a cloud service in the cloud computing environment. Further, the discount predictor module may determine a variation between the actual bill and an expected cost from a public rate card by comparing the actual bill with the expected cost. Furthermore, the discount predictor module may evaluate the plurality of billing rules to predict a discount type and a discount associated with the discount type that matches the variation between the actual bill and the expected cost from the public rate card. Further, the discount predictor module may output the discount type and the discount on an interactive user interface.

The present invention comprises a novel innovation and strategic management software application consisting of an automated method and system for receiving business information and data from a user describing the business products, services and strategy and finances to translate to generated systems, processes and enhanced outcomes. Utilizing artificial intelligence and machine learning for language processing, automated systems render business innovation and strategy models, position business, products and services and performs the management of these. Management of services and processes are communicated through the software application. Communication of project status, tasks and performance are indicated alongside other communications such as audio, visual/video text, and data intelligence which are transferable between internal and external users or team members in the application. Curated education, customized analytics, and insights promote a robust system for information retrieval for users of the application that together provide enhanced competitive competencies.

Systems and methods for distributed business process management are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for configuration-driven distributed orchestration using different software components to execute a complex business process may include: (1) receiving a request for a runtime flow from a flow management adapter; (2) reading a flow configuration from the request; (3) creating an instance of the runtime flow; (4) initiating a service call to each component in the runtime flow; (5) creating a runtime instance in a database along with a state of each dependency in the runtime flow; and in response to external dependencies being met: (6) building and sending message to the components using a message builder; (7) initiating flow actions via an event-driven scheduler; and (8) making a service call to at least one of the components using the message builders.

Systems and methods for distributed business process management are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for configuration-driven distributed orchestration using different software components to execute a complex business process may include: (1) receiving a request for a runtime flow from a flow management adapter; (2) reading a flow configuration from the request; (3) creating an instance of the runtime flow; (4) initiating a service call to each component in the runtime flow; (5) creating a runtime instance in a database along with a state of each dependency in the runtime flow; and in response to external dependencies being met: (6) building and sending message to the components using a message builder; (7) initiating flow actions via an event-driven scheduler; and (8) making a service call to at least one of the components using the message builders.

The supply chain management apparatus includes: an input unit that receives input information indicating a change in market conditions; a storage unit that stores supply chain information in which constituent companies of a supply chain, T&Cs information in which T&Cs of each of the constituent companies of the supply chain is registered, and a condition for a key performance indicator which should be satisfied, the condition for a key performance indicator being set for each of the constituent companies; a T&Cs calculation unit that, when the input information is received, calculates a supply chain plan corresponding to the change in the market conditions indicated by the input information based on a predetermined calculation method, and when a key performance indicator calculated based on the supply chain plan does not meet the condition for the key performance indicator, changes the T&Cs so that the key performance indicator is optimal.

Embodiments optimize inventory allocation of a retail item, where the retail item is allocated from a plurality of different fulfillment centers to a plurality of different customer groups. Embodiments receive historical sales data for the retail item and estimate demand model parameters. Embodiments generate a network including first nodes corresponding to the fulfillment centers, second nodes corresponding to the customer groups, and third nodes between the first nodes and the second nodes, each of the third nodes corresponding to one of the second nodes. Embodiments generate an initial feasible inventory allocation from the first nodes to the second nodes and solves a minimum cost flow problem for the network to generate an optimal inventory allocation.

A system and method are disclosed for segmentation planning wherein a cost-to-serve interval and a value interval are used to generate a strategy for a micro-segment defined along a portion of the cost-to-serve interval and value interval, and similar micro-segments may be assigned to a single persona based on similar cost-to-serve and value tradeoffs.