SYSTEMS, METHODS, KITS, AND APPARATUSES FOR USING ARTIFICIAL INTELLIGENCE FOR INSTRUCTING SMART MACHINES IN VALUE CHAIN NETWORKS
A VCN process may receive 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 on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.
1 . A computer-implemented method comprising:
receiving, by a computing device, information associated with a value chain network, the information generated by at least one of: a set of sensors of a 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;
providing 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 on a training data set of a set of value chain network entities operating data 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 on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network; and
providing a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.
2 . The computer-implemented method of claim 1 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to move an item throughout the value chain network.
3 . The computer-implemented method of claim 2 , wherein the machine includes one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone.
4 . The computer-implemented method of claim 1 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to detect defects and quality issues in an item in the value chain network.
5 . The computer-implemented method of claim 4 , wherein the information associated with the value chain network includes one or more of a video and a photo associated with the item to detect the defects and quality issues in the item in the value chain network.
6 . The computer-implemented method of claim 1 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to predict when an item in the value chain network will fail.
7 . The computer-implemented method of claim 6 , wherein the information associated with the value chain network includes data from one or more sensors associated with the item to predict when the item in the value chain network will fail.
8 . The computer-implemented method of claim 1 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to:
identify a value chain process capable of optimization based upon, at least in part, analyzing the information associated with the value chain network; and
optimize the value chain process.
9 . The computer-implemented method of claim 8 , wherein the value chain process includes one or more of transportation routing, inventory management, supplier selection, or warehouse management.
10 . The computer-implemented method of claim 1 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to transport the item between locations.
11 . The computer-implemented method of claim 1 , wherein the set of the value chain network entities includes at least one of: products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.
12 . The computer-implemented method of claim 1 , wherein the set of AI-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.
13 . The computer-implemented method of claim 1 , wherein the training data set for the set of AI-based learning models includes one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that includes at least one of: the operating state, the fault condition, the operating flow, or the behavior.
14 . A computing system including one or more processors and one or more memories configured to perform operations comprising:
receiving, by a computing device, information associated with a value chain network, the information generated by at least one of: a set of sensors of a 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;
providing 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 on a training data set of a set of value chain network entities operating data 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 on the training data set to determine, upon receiving the classification of the at least one of the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network; and
providing a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.
15 . The computing system of claim 14 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to move an item throughout the value chain network.
16 . The computing system of claim 15 , wherein the machine includes one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone.
17 . The computing system of claim 14 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to detect defects and quality issues in an item in the value chain network.
18 . The computing system of claim 17 , wherein the information associated with the value chain network includes one or more of a video and a photo associated with the item to detect the defects and quality issues in the item in the value chain network.
19 . The computing system of claim 14 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to predict when an item in the value chain network will fail.
20 . The computing system of claim 19 , wherein the information associated with the value chain network includes data from one or more sensors associated with the item to predict when the item in the value chain network will fail.
21 . The computing system of claim 14 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to:
identify a value chain process capable of optimization based upon, at least in part, analyzing the information associated with the value chain network; and
optimize the value chain process.
22 . The computing system of claim 21 , wherein the value chain process includes one or more of transportation routing, inventory management, supplier selection, or warehouse management.
23 . The computing system of claim 14 , wherein providing the computer code instruction set to the machine to execute the task includes instructing the machine to transport the item between locations.
24 . The computing system of claim 14 , wherein the set of the value chain network entities includes at least one of: products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.
25 . The computing system of claim 14 , wherein the set of AI-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.
26 . The computing system of claim 14 , wherein the training data set for the set of AI-based learning models includes one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that includes at least one of: the operating state, the fault condition, the operating flow, or the behavior.