Artificial intelligence prediction of component failure 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 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 at least one value chain entity of the set of value chain network entities. A VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.
1 . A computer-implemented method comprising:
receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, wherein the information is 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;
providing, by the computing device, the information to a set of Artificial Intelligence (AI)-based learning models, wherein the set of AI-based learning models includes a classification model;
training, by the computing device, the classification model on a training data set to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities, wherein a classification taxonomy includes at least one of: the operating state, the fault condition, the operating flow, or the behavior, and wherein the training data set includes:
(i) the information associated with the set of value chain network entities, including operating data of the set of value chain network entities, and
(ii) at least one of a set of objects or a set of events that are labeled to classify the at least one of the set of objects or the set of events according to the classification taxonomy;
determining, by the computing device, a task to be completed for the value chain network based on a classification generated by the classification model;
configuring, by the computing device, a maintenance robot to execute the task based on the classification generated by the classification model;
executing, by the computing device, the task to facilitate an improvement in the value chain network, wherein:
the executing the task includes predicting, by the computing device executing the classification model, when a component of a value chain network entity of the set of value chain network entities will fail,
the predicting when the component will fail includes predicting, using the classification model, a time window in which the component will fail,
the information includes historical data and current operational data from at least one of the set of sensors associated with the value chain network entity, and
the executing the task includes deploying the maintenance robot to the value chain network entity to at least one of; repair or replace the component prior to the predicted time window;
receiving, by the computing device, feedback from the maintenance robot during the executing the task, wherein the feedback includes (i) a set of circumstances that led to the prediction of the time window and (ii) an outcome of the prediction; and
in response to receiving the feedback, retraining, by the computing device, the classification model using the feedback.
2 . The computer-implemented method of claim 1 , wherein executing the task includes predicting future demand for an item in the value chain network.
3 . The computer-implemented method of claim 2 , wherein the information includes one or more of historical sales data and market trends associated with the item.
4 . The computer-implemented method of claim 2 , wherein executing the task includes detecting defects and quality issues in an item in the value chain network.
5 . The computer-implemented method of claim 4 , wherein the information includes one or more of a video and a photo associated with the item.
6 . The computer-implemented method of claim 4 , wherein the information includes data from one or more sensors associated with the item.
7 . The computer-implemented method of claim 1 , wherein executing the task includes:
identifying a value chain process capable of optimization based on analyzing the information associated with the value chain network; and
optimizing the value chain process.
8 . The computer-implemented method of claim 7 , wherein the value chain process includes one or more of transportation routing, inventory management, or supplier selection.
9 . The computer-implemented method of claim 1 , wherein executing the task includes:
analyzing user data of a user from at least one source; and
identifying one or more attributes of the user based on the user data.
10 . The computer-implemented method of claim 1 , wherein the set of value chain network entities includes at least one of: operating facilities, mobile devices, wearable devices, supply chain infrastructure facilities, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, delivery systems, floating assets, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.
11 . 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.
12 . The computer-implemented method of claim 1 , further comprising:
developing a maintenance plan based on the prediction,
wherein the deploying the maintenance robot includes deploying the maintenance robot in accordance with the maintenance plan.
13 . The computer-implemented method of claim 1 , wherein:
the classification model is a neural network; and
the retraining includes changing the neural network from a first neural network type to a second neural network type.
14 . The computer-implemented method of claim 1 , wherein the set of circumstances includes attributes of the component and attributes of the classification model.
15 . The computer-implemented method of claim 1 , wherein:
the value chain network entity is a robotic operating unit, and
the component is at least one of: an end effector, a motive adaptor, a sensor, an image processing module, a manipulator, a skeletal component, an appendage, a battery, a motor, or an environmental shielding component.
16 . The computer-implemented method of claim 1 , wherein the maintenance robot is configured to, while deployed, use three-dimensional (3D) printing to produce a part used to at least one of: repair or replace the component.
17 . The computer-implemented method of claim 1 , wherein the deploying the maintenance robot includes routing the maintenance robot from a storage location to the value chain network entity.
18 . The computer-implemented method of claim 1 , wherein:
the value chain network entity is a robotic operating unit,
the component of the value chain network entity is at least one of: an end effector, a motive adaptor, a manipulator, a skeletal component, or an appendage, and
the maintenance robot is configured to, while deployed, use three-dimensional (3D) printing to produce a replacement component to replace the component.
19 . A computing system comprising one or more processors and one or more memories configured to perform operations including:
receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, wherein the information is 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;
providing, by the computing device, the information to a set of Artificial Intelligence (AI)-based learning models, wherein the set of AI-based learning models includes a classification model;
training, by the computing device, the classification model on a training data set to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities, wherein a classification taxonomy includes at least one of: the operating state, the fault condition, the operating flow, the behavior, and wherein the training data set includes:
(i) the information associated with the set of value chain network entities, including operating data of the set of value chain network entities, and
(ii) at least one of a set of objects or a set of events that are labeled to classify the at least one of the set of objects or the set of events according to the classification taxonomy;
determining, by the computing device, a task to be completed for the value chain network based on a classification generated by the classification model;
configuring, by the computing device, a maintenance robot to execute the task based on the classification generated by the classification model;
executing, by the computing device, the task to facilitate an improvement in the value chain network, wherein:
the executing the task includes predicting, by the computing device executing the classification model, when a component of a value chain network entity of the set of value chain network entities will fail,
the predicting when the component will fail includes predicting, using the classification model, a time window in which the component will fail,
the information includes historical data and current operational data from at least one of the set of sensors associated with the value chain network entity, and
the executing the task includes deploying the maintenance robot to the value chain network entity to at least one of; repair or replace the component prior to the predicted time window;
receiving, by the computing device, feedback from the maintenance robot during the executing the task, wherein the feedback includes (i) a set of circumstances that led to the prediction of the time window and (ii) an outcome of the prediction; and
in response to receiving the feedback, retraining, by the computing device, the classification model using the feedback.
20 . The computing system of claim 19 , wherein executing the task includes predicting future demand for an item in the value chain network.
21 . The computing system of claim 20 , wherein the information includes one or more of historical sales data and market trends associated with the item.
22 . The computing system of claim 19 , wherein executing the task includes detecting defects and quality issues in an item in the value chain network.
23 . The computing system of claim 22 , wherein the information includes one or more of a video and a photo associated with the item.
24 . The computing system of claim 20 , wherein the information includes data from one or more sensors associated with the item.
25 . The computing system of claim 19 , wherein executing the task includes:
identifying a value chain process capable of optimization based on analyzing the information associated with the value chain network; and
optimizing the value chain process.
26 . The computing system of claim 25 , wherein the value chain process includes one or more of transportation routing, inventory management, or supplier selection.
27 . The computing system of claim 19 , wherein executing the task includes:
analyzing user data of a user from at least one source; and
identifying one or more attributes of the user based on the user data.
28 . The computing system of claim 19 , wherein the set of value chain network entities includes at least one of: operating facilities, mobile devices, wearable devices, supply chain infrastructure facilities, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, delivery systems, floating assets, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.
29 . The computing system of claim 19 , 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.