Abstract: A system that compresses data during neural network training. A memory stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An anticipatory value of inventory (VOI) optimization component calculates optimal VOI and prices for immediate-future inventory levels in parallel and writes latest price updates for respective states to a price stack; and a recommendation component provides customized pricing recommendation for a product relative to a unique customer as a function of the latest price updates for respective states to the price stack.

Abstract: System, method and computer program product for effective supply-side planning of inventories during product transitions. The system for a supply-side entity that generates recommendations that include personalized shaping actions to control each customer's rate of adoption of a new product to replace an older legacy product during a transition period, and optimize overall use of available supply. The method optimizes personalized shaping actions so that a customer is transitioned at a speed that suits their personal profile as well as the seller's production/inventory constraints for each product. Shaping actions are determined based on the trajectory of each product's lifecycle. Further, customer-level adoption patterns for transitioning products are predicted from past behavior.

Abstract: An apparatus, method and computer program product for identifying fraud in transaction data. The method includes: receiving invoice data comprising a vendor, a requestor and events, receiving public data and private data sources, computing a vendor risk score using the public and private data sources matching the vendor of the invoice data, computing a requestor risk score using the public data sources and the private data sources matching the requestor of the invoice data, computing an active invoice score using the vendor risk score and the requestor risk score and when the active invoice score is greater than a predetermined amount, blocking the invoice data. In one embodiment, computing a vendor risk score comprises obtaining a weight and a confidence for the event, calculating an event vendor risk score using the weight times the confidence and combining the event vendor risk scores for all of the events.

Abstract: A system that compresses data during neural network training. A memory stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An anticipatory value of inventory (VOI) optimization component calculates optimal VOI and prices for immediate-future inventory levels in parallel and writes latest price updates for respective states to a price stack; and a recommendation component provides customized pricing recommendation for a product relative to a unique customer as a function of the latest price updates for respective states to the price stack.

Abstract: An apparatus, method and computer program product for identifying fraud in transaction data. The method includes: receiving invoice data comprising a vendor, a requestor and events, receiving public data and private data sources, computing a vendor risk score using the public and private data sources matching the vendor of the invoice data, computing a requestor risk score using the public data sources and the private data sources matching the requestor of the invoice data, computing an active invoice score using the vendor risk score and the requestor risk score and when the active invoice score is greater than a predetermined amount, blocking the invoice data. In one embodiment, computing a vendor risk score comprises obtaining a weight and a confidence for the event, calculating an event vendor risk score using the weight times the confidence and combining the event vendor risk scores for all of the events.

Abstract: System, method and computer program product for effective supply-side planning of inventories during product transitions. The system for a supply-side entity that generates recommendations that include personalized shaping actions to control each customer's rate of adoption of a new product to replace an older legacy product during a transition period, and optimize overall use of available supply. The method optimizes personalized shaping actions so that a customer is transitioned at a speed that suits their personal profile as well as the seller's production/inventory constraints for each product. Shaping actions are determined based on the trajectory of each product's lifecycle. Further, customer-level adoption patterns for transitioning products are predicted from past behavior.

Abstract: In a method for analyzing social media messages, the method includes one or more processors analyzing social media messages utilizing a set of topics and keywords associated with a brand. The method further includes one or more processors identifying social media messages that include information relating to the brand utilizing the analysis of social media messages utilizing the set of topics and keywords. The method further includes one or more processors determining relevancy scores for the identified social media messages that provide an indication of the percentage likelihood that a social media message is related to the brand. The method further includes one or more processors determining one or more recommendations of social media users associated with social media messages based on the determined relevancy scores.

Abstract: A method for balancing supply and demand for a product using demand-shaping action includes identifying an imbalance between supply and demand for a given product. A customer choice model is generated based on collected historical sales data pertaining to the given product and at least one product similar to the given product. The customer choice model is configured to estimate, for a given customer or group of customers, a likelihood of effecting a substitution between each product pair of the given product and the at least one product similar to the given product, for each of one or more available demand shaping actions. One or more of the available demand shaping actions are automatically selected to minimize an estimate of revenue shortfall or inventory holding costs resulting from the identified imbalance between supply and demand.

Abstract: A method for balancing supply and demand for a product using demand-shaping action includes identifying an imbalance between supply and demand for a given product. A customer choice model is generated based on collected historical sales data pertaining to the given product and at least one product similar to the given product. The customer choice model is configured to estimate, for a given customer or group of customers, a likelihood of effecting a substitution between each product pair of the given product and the at least one product similar to the given product, for each of one or more available demand shaping actions. One or more of the available demand shaping actions are automatically selected to minimize an estimate of revenue shortfall or inventory holding costs resulting from the identified imbalance between supply and demand.

Abstract: Robust inventory management for a supply chain network with multiple nodes may include generating a time-phased inventory deployment plan based on extreme samples and dynamic supply chain structure. The extreme samples of demand and supply chain scenarios, and dynamic supply chain structure including one or more resource constraints associated with one or more nodes in the supply chain network may be received from a user.

Abstract: A system and method for allowing an organization access to supplier data, allows an organization which has outsourced one or more supply functions to one or more suppliers, permits feeding in real-time data from suppliers and processing and analyzing the data to determine performance metrics. Monitoring of these performance metrics against thresholds, which are established periodically based on customer data and forecast data, allows for the generation of alerts when the thresholds have been exceeded. This may occur, for example, when a supplier contract may have been violated or when new controls may need to be enforced within the contracts. Corrective actions may be taken in response to alerts, and these actions may modify previously established performance metrics and provide for recalculating the performance metrics with the new data.