Abstract: According to some embodiments, a system may facilitate collaborative transaction processing associated with a supply chain having a first entity and a second entity. In particular, a first entity database may store electronic records including information associated with at least a portion of the supply chain, and a first entity communication port may exchange information via a distributed computer system. A first entity computer processor may retrieve from the first entity database the information associated with the at least a portion of the supply chain. A subset of information about the supply chain may be identified by the first entity computer processor as being of interest to the second entity. The identified subset of information about the supply chain may then be recorded via a secure, distributed transaction ledger.

Abstract: Some embodiments provide a system and method comprising a memory and a processor to cause the system to: receive a first and second data distribution for a first and second variable, respectively; determine a first and second data optimum number of bins for the first and second data distribution, respectively; create a first and second model for the first and second data distribution using the first and second data optimum number of bins, respectively; apply the first model to the second data distribution to calculate a smallest descriptive size of the second data distribution given the first model; apply the second model to the first data distribution to calculate a smallest descriptive size of the first data distribution given the second model; and determine a causal direction between the first variable and the second variable based on the application of the first and second model. Numerous other aspects are provided.

Abstract: A cyber-physical system may have monitoring nodes that generate a series of current monitoring node values over time that represent current operation of the system. A hierarchical abnormality localization computer platform accesses a multi-level hierarchy of elements, and elements in a first level of the hierarchy are associated with elements in at least one lower level of the hierarchy and at least some elements may be associated with monitoring nodes. The computer platform may then determine, based on feature vectors and a decision boundary, an abnormality status for a first element in the highest level of the hierarchy. If the abnormality status indicates an abnormality, the computer platform may determine an abnormality status for elements, associated with the first element, in at least one level of the hierarchy lower than the level of the first element. These determinations may be repeated until an abnormality is localized to a monitoring node.

Abstract: According to some embodiments, a system may facilitate collaborative transaction processing associated with a supply chain having a first entity and a second entity. In particular, a first entity database may store electronic records including information associated with at least a portion of the supply chain, and a first entity communication port may exchange information via a distributed computer system. A first entity computer processor may retrieve from the first entity database the information associated with the at least a portion of the supply chain. A subset of information about the supply chain may be identified by the first entity computer processor as being of interest to the second entity. The identified subset of information about the supply chain may then be recorded via a secure, distributed transaction ledger.

Abstract: Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abstract: A cyber-physical system may have monitoring nodes that generate a series of current monitoring node values over time that represent current operation of the system. A hierarchical abnormality localization computer platform accesses a multi-level hierarchy of elements, and elements in a first level of the hierarchy are associated with elements in at least one lower level of the hierarchy and at least some elements may be associated with monitoring nodes. The computer platform may then determine, based on feature vectors and a decision boundary, an abnormality status for a first element in the highest level of the hierarchy. If the abnormality status indicates an abnormality, the computer platform may determine an abnormality status for elements, associated with the first element, in at least one level of the hierarchy lower than the level of the first element. These determinations may be repeated until an abnormality is localized to a monitoring node.

Abstract: According to some embodiments, system and methods are provided, comprising a flow module to receive one or more objectives for a flow in the production of an object; a memory for storing program instructions; a flow processor, coupled to the memory, and in communication with the flow module and operative to execute program instructions to: receive a request from a user for a current state of a flow for the object; retrieve metadata and data from the physical world associated with the object; determine a current phase in the flow for the object; select an analytic model to determine the current state of the flow for the object; instantiate a digital twin for the current phase in the flow for the object; execute the selected analytic for the instantiated digital twin; generate a response to the request including the current state of the flow for the object based on the executed analytic; and display the response on a display. Numerous other aspects are provided.

Abstract: Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abstract: An industrial part modeling system may include a digital twin industrial part modeling platform containing a plurality of learning models, each learning model describing characteristics of an industrial part available to be incorporated into an industrial asset. The system may also include an application server platform and a user interface platform to receive an industrial part search or analysis requests from a user. The application server platform may receive information about the industrial part search or analysis request and execute at least one search or analysis algorithm to evaluate learning models in the digital twin industrial part modeling platform. Based on said evaluation, the application server platform may provide an industrial part search or analysis result report to the user. Moreover, the application server platform may automatically arrange for at least one of a search or analysis algorithm and a learning model to be updated based on interaction with the user.

Abstract: An optimum order quantity (OOQ) of the material or component using received data is dynamically determined in real-time. An optimum proposed fulfillment timing for filling open purchase orders of the material or component using the received data is determined. The OOQ and optimum proposed fulfillment timing are rendered to a supply chain owner/materials manager at a graphical electronic display. The OOQ and the optimized proposed fulfillment timing are re-determined as additional data is received and by utilizing the new order quantity.