Strong Force Vcn Portfolio 2019, LLC

Jenna Parenti

Charles Howard Cella

Andrew Cardno

Teymour S El-Tahry

Andrew S Locke

Kunal Sharma

Brad Kell

Leon Fortin, Jr

David Stein

Benjamin D Goodman

Hristo Malchev

Andrew Bunin

Eric P Vetter

Taylor Charon

Brent Bliven

Richard Spitz

Joshua Dobrowitsky

An information technology generally including a set of monitoring facilities that are configured to monitor the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.

Control tower and enterprise management platform with a machine learning/artificial intelligence managing sensor and the camera feeds into digital twin

A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Machine-learning-driven supply chain out-of-stock inventory resolution and contract negotiation

A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Value chain network planning using machine learning and digital twin simulation

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

Dynamic edge-distributed storage in value chain network

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The query is a request for data stored at the edge device and for data stored at other edge devices. The method includes executing, by the edge device, the query to find partial query results comprising the data stored at the edge device. The method includes generating, by the edge device, statistical information based on the partial query results. The method includes determining, by the edge device, a statistical confidence associated with the partial results based on the statistical information. The method includes generating, by the edge device, an approximate response to the query based on the statistical information. The method includes transmitting the approximate response to the query device.

Edge-distributed query processing in value chain networks

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Denver, CO, United States of America

Jenna Parenti