Patent Application
20230094800
The present disclosure relates generally to computer networks, and, more particularly, to semantic reasoning for supply chains.
Modern supply chains are complex and diverse systems. Every day, a great variety of cargo traverses the world and is tracked and controlled by a plethora of control systems. Increasingly, different types of cargo are being shipped together, including some that are vulnerable to damage while still in transit.
Damage to shipped items may occur due to a wide variety of issues, such as a sharp impact or harsh handling of the cargo. Another source of damage may be exposure to adverse environmental conditions such as water, light, heat, radiation, etc. For instance, items such as food or medicines within a ‘cold chain’ can be subject to spoilage as a result of transport delays or breakdown of refrigeration units. These are just a few examples and there are many ways (and degrees) in which inventory can become damaged while within the supply chain.
The complexity of many supply chains makes it extremely challenging to determine the ripple effects that a disruption can cause. Indeed, damage to a particular shipment or even a shipping delay can potentially lead to a complete stoppage of a manufacturing process. For instance, interruptions in the shipment of a particular computer chip could prevent an automotive manufacturer from completing any new automobiles, entirely. With potentially tens of thousands of components being shipped daily across any number of different products being produced, it is unrealistic to expect their interrelationships to be defined and maintained, manually, meaning that the effects of supply chain disruptions are often unknown until they occur.
The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:
According to one or more embodiments of the disclosure, a device receives sensor data from a plurality of sensors. The device detects, using a semantic reasoning engine, a disruption to a first shipment based on the sensor data. The device infers, using the semantic reasoning engine, that one or more other shipments are related to the first shipment. The device initiates a mitigation action for the disruption that is performed with respect to the one or more other shipments.
A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers, cellular phones, workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to forward data from one network to another.
Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or PLC networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port such as PLC, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.
In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN utilizing a Service Provider network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:
1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/5G/LTE backup connection). For example, a particular CE router 110 shown in network 100 may support a given customer site, potentially also with a backup link, such as a wireless connection.
2.) Site Type B: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers) using a single CE router, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). A site of type B may itself be of different types:
2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).
2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). For example, a particular customer site may be connected to network 100 via PE-3 and via a separate Internet connection, potentially also with a wireless backup link.
2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).
Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site).
3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/5G/LTE backup link). For example, a particular customer site may include a first CE router 110 connected to PE-2 and a second CE router 110 connected to PE-3
Servers 152-154 may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network 100 may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc.
In some embodiments, the techniques herein may be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc.
In various embodiments, network 100 may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.
Notably, shared-media mesh networks, such as wireless or PLC networks, etc., are often deployed on what are referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network 160 may be an LLN in which CE-2 operates as a root node for devices/nodes 10-16 in the local mesh, in some embodiments.
In contrast to traditional networks, LLNs face a number of communication challenges. First, LLNs communicate over a physical medium that is strongly affected by environmental conditions that change over time. Some examples include temporal changes in interference (e.g., other wireless networks or electrical appliances), physical obstructions (e.g., doors opening/closing, seasonal changes such as the foliage density of trees, etc.), and propagation characteristics of the physical media (e.g., temperature or humidity changes, etc.). The time scales of such temporal changes can range between milliseconds (e.g., transmissions from other transceivers) to months (e.g., seasonal changes of an outdoor environment). In addition, LLN devices typically use low-cost and low-power designs that limit the capabilities of their transceivers. In particular, LLN transceivers typically provide low throughput. Furthermore, LLN transceivers typically support limited link margin, making the effects of interference and environmental changes visible to link and network protocols. The high number of nodes in LLNs in comparison to traditional networks also makes routing, quality of service (QoS), security, network management, and traffic engineering extremely challenging, to mention a few.
The network interfaces 210 include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface 210 may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.
The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242 (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory 240 and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a deep fusion reasoning engine (DFRE) process 248, as described herein.
It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.
DFRE process 248 includes computer executable instructions that, when executed by processor(s) 220, cause device 200 to provide cognitive reasoning services to a network. In various embodiments, DFRE process 248 may utilize machine learning techniques, in whole or in part, to perform its analysis and reasoning functions. In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators) and recognize complex patterns in these data. One very common pattern among machine learning techniques is the use of an underlying model M, whose hyper-parameters are optimized for minimizing the cost function associated to M, given the input data. The learning process then operates by adjusting the hyper-parameters such that the number of misclassified points is minimal. After this optimization phase (or learning phase), the model M can be used very easily to classify new data points. Often, M is a statistical model, and the minimization of the cost function is equivalent to the maximization of the likelihood function, given the input data.
In various embodiments, DFRE process 248 may employ one or more supervised, unsupervised, or self-supervised machine learning models. Generally, supervised learning entails the use of a training large set of data, as noted above, that is used to train the model to apply labels to the input data. For example, in the case of video recognition and analysis, the training data may include sample video data that depicts a certain object and is labeled as such. On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior. Self-supervised is a representation learning approach that eliminates the pre-requisite requiring humans to label data. Self-supervised learning systems extract and use the naturally available relevant context and embedded metadata as supervisory signals. Self-supervised learning models take a middle ground approach: it is different from unsupervised learning as systems do not learn the inherent structure of data, and it is different from supervised learning as systems learn entirely without using explicitly-provided labels.
Example machine learning techniques that DFRE process 248 can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) artificial neural networks (ANNs) (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like. Accordingly, DFRE process 248 may employ deep learning, in some embodiments. Generally, deep learning is a subset of machine learning that employs ANNs with multiple layers, with a given layer extracting features or transforming the outputs of the prior layer.
The performance of a machine learning model can be evaluated in a number of ways based on the number of true positives, false positives, true negatives, and/or false negatives of the model. For example, the false positives of the model may refer to the number of times the model incorrectly identified an object or condition within a video feed. Conversely, the false negatives of the model may refer to the number of times the model failed to identify an object or condition within a video feed. True negatives and positives may refer to the number of times the model correctly determined that the object or condition was absent in the video or was present in the video, respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives.
According to various embodiments,
In general, a reasoning engine, also known as a ‘semantic reasoner,’ reasoner,' or ‘rules engine,’ is a specialized form of machine learning software that uses asserted facts or axioms to infer consequences, logically. Typically, a reasoning engine is a form of inference engine that applies inference rules defined via an ontology language. As introduced herein, a DFRE is an enhanced form of reasoning engine that further leverages the power of sub-symbolic machine learning techniques, such as neural networks (e.g., deep learning), allowing the system to operate across the full spectrum of sub-symbolic data all the way to the symbolic level.
At the lowest layer of hierarchy 300 is sub-symbolic layer 302 that processes the sensor data 312 collected from the network. For example, sensor data 312 may include video feed/stream data from any number of cameras located throughout a location. In some embodiments, sensor data 312 may comprise multimodal sensor data from any number of different types of sensors located throughout the location. At the core of sub-symbolic layer 302 may be one or more DNNs 308 or other machine learning-based model that processes the collected sensor data 312. In other words, sub-symbolic layer 302 may perform sensor fusion on sensor data 312 to identify hidden relationships between the data.
At the opposing end of hierarchy 300 may be symbolic layer 306 that may leverage symbolic learning. In general, symbolic learning includes a set of symbolic grammar rules specifying the representation language of the system, a set of symbolic inference rules specifying the reasoning competence of the system, and a semantic theory containing the definitions of “meaning.” This approach differs from other learning approaches that try to establish generalizations from facts as it is about reasoning and extracting knowledge from knowledge. It combines knowledge representations and reasoning to acquire and ground knowledge from observations in a non-axiomatic way. In other words, in sharp contrast to the sub-symbolic learning performed in layer 302, the symbolic learning and generalized intelligence performed at symbolic layer 306 requires a variety of reasoning and learning paradigms that more closely follows how humans learn and are able to explain why a particular conclusion was reached.
Symbolic learning models what are referred to as “concepts,” which comprise a set of properties. Typically, these properties include an “intent” and an “extent,” whereby the intent offers a symbolic way of identifying the extent of the concept. For example, consider the intent that represents motorcycles. The intent for this concept may be defined by properties such as “having two wheels” and “motorized,” which can be used to identify the extent of the concept (e.g., whether a particular vehicle is a motorcycle).
Linking sub-symbolic layer 302 and symbolic layer 306 may be conceptual layer 304 that leverages conceptual spaces. In general, conceptual spaces are a proposed framework for knowledge representation by a cognitive system on the conceptual level that provides a natural way of representing similarities. Conceptual spaces enable the interaction between different type of data representations as an intermediate level between sub-symbolic and symbolic representations.
More formally, a conceptual space is a geometrical structure which is defined by a set of quality dimensions to allow for the measurement of semantic distances between instances of concepts and for the assignment of quality values to their quality dimensions, which correspond to the properties of the concepts. Thus, a point in a conceptual space S may be represented by an n-dimensional conceptual vector v=<d1, . . . , di, . . . , dn> where di represents the quality value for the ith quality dimension. For example, consider the concept of taste. A conceptual space for taste may include the following dimensions: sweet, sour, bitter, and salty, each of which may be its own dimension in the conceptual space. The taste of a given food can then be represented as a vector of these qualities in a given space (e.g., ice cream may fall farther along the sweet dimension than that of peanut butter, peanut butter may fall farther along the salty dimension than that of ice cream, etc.). By representing concepts within a geometric conceptual space, similarities can be compared in geometric terms, based on the Manhattan distance between domains or the Euclidean distance within a domain in the space. In addition, similar objects can be grouped into meaningful conceptual space regions through the application of clustering techniques, which extract concepts from data (e.g., observations).
Said differently, a conceptual space is a framework for representing information that models human-like reasoning to compose concepts using other existing concepts. Note that these representations are not competing with symbolic or associationistic representations. Rather, the three kinds can be seen as three levels of representations of cognition with different scales of resolution and complementary. Namely, a conceptual space is built up from geometrical representations based on a number of quality dimensions that complements the symbolic and deep learning models of symbolic layer 306 and sub-symbolic layer 302, representing an operational bridge between them. Each quality dimension may also include any number of attributes, which present other features of objects in a metric subspace based on their measured quality values. Here, similarity between concepts is just a matter of metric distance between them in the conceptual space in which they are embedded.
In other words, a conceptual space is a geometrical representation which allows the discovery of regions that are physically or functionally linked to each other and to abstract symbols used in symbolic layer 306, allowing for the discovery of correlations shared by the conceptual domains during concepts formation. For example, an alert prioritization module may use connectivity to directly acquire and evaluate alerts as evidence. Possible enhancements may include using volume of alerts and novelty of adjacent (spatially/temporally) alerts, to tune level of alertness.
In general, the conceptual space at conceptual layer 304 allows for the discovery of regions that are naturally linked to abstract symbols used in symbolic layer 306. The overall model is bi-directional as it is planned for predictions and action prescriptions depending on the data causing the activation in sub-symbolic layer 302.
Layer hierarchy 300 shown is particularly appealing when matched with the attention mechanism provided by a cognitive system that operates under the assumption of limited resources and time-constraints. For practical applications, the reasoning logic in symbolic layer 306 may be non-axiomatic and constructed around the assumption of insufficient knowledge and resources (AIKR). It may be implemented, for example, with a Non-Axiomatic Reasoning System (open-NARS) 310. However, other reasoning engines can also be used, such as Auto-catalytic Endogenous Reflective Architecture (AERA), OpenCog, and the like, in symbolic layer 306, in further embodiments. Even Prolog may be suitable, in some cases, to implement a reasoning engine in symbolic layer 306. In turn, an output 314 coming from symbolic layer 306 may be provided to a user interface (UI) for review. For example, output 314 may comprise a video feed/stream augmented with inferences or conclusions made by the DFRE.
By way of example of symbolic reasoning, consider the ancient Greek syllogism: (1.) All men are mortal, (2.) Socrates is a man, and (3.) therefore, Socrates is mortal. Depending on the formal language used for the symbolic reasoner, these statements can be represented as symbols of a term logic. For example, the first statement can be represented as “man→[mortal]” and the second statement can be represented as “{Socrates}→man.” Thus, the relationship between terms can be used by the reasoner to make inferences and arrive at a conclusion (e.g., “Socrates is mortal”). Non-axiomatic reasoning systems (NARS) generally differ from more traditional axiomatic reasoners in that the former applies a truth value to each statement, based on the amount of evidence available and observations retrieved, while the latter relies on axioms that are treated as a baseline of truth from which inferences and conclusions can be made.
In other words, a DFRE generally refers to a cognitive engine capable of taking sub-symbolic data as input (e.g., raw or processed sensor data regarding a monitored system), recognizing symbolic concepts from that data, and applying symbolic reasoning to the concepts, to draw conclusions about the monitored system.
According to various embodiments,
In various embodiments, DFRE middleware 402 may also provide services to support semantic reasoning, such as by an AIKR reasoner. For example, as shown, DFRE middleware 402 may include a NARS agent that performs semantic reasoning for structural learning. In other embodiments, OpenCog or another suitable AIKR semantic reasoner could be used.
One or more DFRE agents 404 may interface with DFRE middleware 402 to orchestrate the various services available from DFRE middleware 402. In addition, DFRE agent 404 may feed and interact with the AIKR reasoner so as to populate and leverage a DFRE knowledge graph with knowledge.
More specifically, in various embodiments, DFRE middleware 402 may obtain sub-symbolic data 408. In turn, DFRE middleware 402 may leverage various ontologies, programs, rules, and/or structured text 410 to translate sub-symbolic data 408 into symbolic data 412 for consumption by DFRE agent 404. This allows DFRE agent 404 to apply symbolic reasoning to symbolic data 412, to populate and update a DFRE knowledge base (KB) 416 with knowledge 414 regarding the problem space (e.g., the network under observation, etc.). In addition, DFRE agent 404 can leverage the stored knowledge 414 in DFRE KB 416 to make assessments/inferences.
For example, DFRE agent 404 may perform semantic graph decomposition on DFRE KB 416 (e.g., a knowledge graph), so as to compute a graph from the knowledge graph of KB 416 that addresses a particular problem. DFRE agent 404 may also perform post-processing on DFRE KB 416, such as performing graph cleanup, applying deterministic rules and logic to the graph, and the like. DFRE agent 404 may further employ a definition of done, to check goals and collect answers using DFRE KB 416.
In general, DFRE KB 416 may comprise any or all of the following:
In other words, DFRE KB 416 acts as a dynamic and generic memory structure. In some embodiments, DFRE KB 416 may also allow different reasoners to share or coalesce knowledge, have their own internal sub-graphs, and/or work collaboratively in a distributed manner. For example, a first DFRE agent 404 may perform reasoning on a first sub-graph, a second DFRE agent 404 may perform reasoning on a second sub-graph, etc., to evaluate the health of the network and/or find solutions to any detected problems. To communicate with DFRE agent 404, DFRE KB 416 may include a bidirectional Narsese interface or other interface using another suitable grammar.
In various embodiments, DFRE KB 416 can be visualized on a user interface. For example, Cytoscape, which has its building blocks in bioinformatics and genomics, can be used to implement graph analytics and visualizations.
Said differently, DFRE architecture 400 may include any or all of the following the following components:
More specifically, in some embodiments, DFRE middleware 402 may include any or all of the following:
DFRE middleware 402 may also allow the addition of new services needed by different problem domains.
During execution, DFRE agent 404 may, thus, perform any or all of the following:
To address combinatorial explosion, the DFRE knowledge graph may be partitioned such that each partition is processed by one or more DFRE agents 404, as shown in
As would be appreciated, graph decomposition can be based on any or all of the following:
In further embodiments, the DFRE framework may also support various user interface functions, so as to provide visualizations, actions, etc. to the user. To do so, the framework may leverage Cytoscape, web services, or any other suitable mechanism.
At the core of the techniques herein is a knowledge representation metamodel 700 for different levels of abstraction, as shown in
One key advantage of the DFRE knowledge graph is that human level domain expertise, ontologies, and goals are entered at the L2 level. This leads, by definition, to an unprecedented ability to generalize at the L2 level thus minimizing the manual effort required to ingest domain expertise.
More formally:
In various embodiments, L2 may comprise both expertise and experience stored in long-term memory, as well as a focus of attention (FOA) in short-term memory. In other words, when a problem is triggered at L*, a DFRE agent 702 that operates on L2-L0 may control the FOA so as to focus on different things, in some embodiments.
As would be appreciated, there may be hundreds of thousands or even millions of data points that need to be extracted at L0. The DFRE's FOA is based on the abstraction and the DFRE knowledge graph (KG) may be used to keep combinatorial explosion under control.
Said differently, metamodel 700 may generally take the form of a knowledge graph in which semantic knowledge is stored regarding a particular system, such as one or more supply chains. By representing the relationships between such real-world entities (e.g., different shipments, different types of goods, etc.), as well as their more abstract concepts (e.g., how a particular component is used), DFRE agent 702 can make evaluations regarding the particular system at different levels of extraction. Indeed, metamodel 700 may differ from a more traditional knowledge graph through the inclusion of any or all of the following, in various embodiments:
As noted above, modern supply chains are complex and diverse systems. Every day, a great variety of cargo traverses the world and is tracked and controlled by a plethora of control systems, Increasingly, different types of cargo are being shipped together, including some that are vulnerable to damage while still in transit.
Damage to shipped items may occur due to a wide variety of issues, such as a sharp impact or harsh handling of the cargo. Another source of damage may be exposure to adverse environmental conditions such as water, light, heat, radiation, etc. For instance, items such as food or medicines within a ‘cold chain’ can be subject to spoilage as a result of transport delays or breakdown of refrigeration units. These are just a few examples and there are many ways (and degrees) in which inventory can become damaged while within the supply chain.
The complexity of many supply chains makes it extremely challenging to determine the ripple effects that a disruption can cause. Indeed, damage to a particular shipment or even a shipping delay can potentially lead to a complete stoppage of a manufacturing process. For instance, interruptions in the shipment of a particular computer chip could prevent an automotive manufacturer from completing any new automobiles, entirely. With potentially tens of thousands of components being shipped daily across any number of different products being produced, it is unrealistic to expect their interrelationships to be defined and maintained, manually, meaning that the effects of supply chain disruptions are often unknown until they occur.
The techniques herein propose leveraging semantic reasoning and multimodal timeseries data, to make inferences about a supply chain. This allows the system to understand previously unseen events, infer the effects of these events, and make suggestions or other corrective measures.
Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with DFRE process 248, which may include computer executable instructions executed by the processor 220 (or independent processor of interfaces 210), to perform functions relating to the techniques described herein.
Specifically, according to various embodiments, a device receives sensor data from a plurality of sensors. The device detects, using a semantic reasoning engine, a disruption to a first shipment based on the sensor data. The device infers, using the semantic reasoning engine, that one or more other shipments are related to the first shipment. The device initiates a mitigation action for the disruption that is performed with respect to the one or more other shipments.
Operationally,
By way of example, consider the case in which a particular supplier ships goods such as computer chips and batteries to a variety of recipients/receivers. As part of this, the supplier may employ a fleet of transportation vehicles, such as cargo ships, trucks, airplanes, or the like. Other entitles that may be represented as concepts in knowledge graph 802 may include retailers, customers. or the like.
As sensor data is ingested by metamodel 700, this allows the metamodel to begin making inferences about specific instances of the various concepts in knowledge graph 802. For instance, assume that a given supplier sends a shipment of chips to a receiver using a particular truck, “truck 013.” Accordingly, metamodel 700 may begin ingesting sensor data from any sensors that are deployed on that truck, such as video cameras, microphones, temperature sensors, vibration sensors, gas sensors, moisture detectors, or the like.
In some embodiments, metamodel 700 may infer a disruption to a particular shipment, based on its ingested sensor data. For instance, assume that the truck carrying chips to the receiver experiences a weather-related event that leaves its interior flooded with water. Even if there are no sensors located within the shipping container for that shipment, metamodel 700 may infer that the shipment was damaged, based on the semantic concepts regarding the type of goods in the shipment (e.g., whether water-resistant or not), the packing material(s) for the shipment, the extent of the flooding (e.g., water touching the shipment vs. not, etc.), and the like. In other instances, metamodel 700 may determine that damage has occurred based on direct evidence in the sensor data, such as an image of damage from a video camera onboard the truck.
Another type of disruption that metamodel 700 may detect is a delay in a shipment. For instance, if the truck with the shipment remains stationary in a certain parking lot for too long, metamodel 700 may reason that the shipment will not arrive on time to its receiver. Such an inference can be made, for instance, based on historical patterns with respect to the shipping route in question.
Regardless of the specific reason for a supply chain disruption, another key functionality of 700 may be to perform causality analysis and predictions on the other elements of the supply chain, in various embodiments. More specifically, in some embodiments, metamodel 700 may identify one or more relationships between a particular shipment undergoing a disruption and one or more other shipments. For instance, metamodel 700 may determine that the disrupted shipment includes goods of a particular type and that the type of goods is related to other goods in other shipments. This type of reasoning may be based, for instance, on the goods all tending to be ordered or received by a certain receiver around the same time, which may indicate that the different goods are used by the receiver as pail of the manufacture of a particular product.
Since knowledge graph 802 can also be populated with a seed ontology and/or receive information over time from additional sources (e.g., additional data feeds), knowledge graph 802 may also include some information as to how certain types of goods are used, which can be leveraged by metamodel 700 when inferring a relationship between a disrupted shipment and other shipments.
Once metamodel 700 has determined the relationship(s) for a disrupted shipment, it may also initiate any number of mitigation actions. In a simple case, metamodel 700 may send an alert to any of the entities affected, such as the shipper or receiver of the disrupted shipment and/or the related shipment(s). Doing so allows those entities to decide how to address the disruption. In other embodiments, metamodel 700 may take corrective measures, such as delaying or canceling the related order(s). For example, say shipments A and B typically are ordered or received by the same entity around the same time and that shipment A has experienced a disruption. Or, shipment A is for a type of good usually combined with the type of good in shipment B and both are destined for the same receiver. In such cases, rather than proceeding with shipment B, which will be of little value to the receiver without shipment A, metamodel 700 may cancel shipment B.
In an additional embodiment, metamodel 700 may also initiate a mitigation action with respect to the disrupted shipment, as well. For instance, in a simple case, metamodel 700 may proceed to order a replacement shipment for the disrupted shipment, depending on the cause of the disruption (e.g., a total loss of the shipment, damage to the shipment, etc.),
As shown in
At step 1015, as detailed above, the device may detect, using a semantic reasoning engine, a disruption to a first shipment based on the sensor data. In some embodiments, the disruption may take the form of damage to the first shipment that the semantic reasoning engine infers from the sensor data. In other words, even if none of the sensors directly detect damage to the first shipment, the semantic reasoning engine may still infer that the shipment suffered damage, such as by leveraging the semantic concepts of “flooding,” “transference of force,” and the like. In further cases, the semantic reasoning engine may reason that the shipment may be delayed, based on the detection of other delays or events along the supply line (e.g., natural disasters, storms, etc.).
At step 1020, the device may infer, using the semantic reasoning engine, that one or more other shipments are related to the first shipment, as described in greater detail above. In various embodiments, the semantic reasoning engine may do so using a knowledge graph comprising concepts. For instance, the semantic reasoning engine may identify a relationship between a type of goods of the first shipment and that of the one or more other shipments, such as based on their respective destinations, timing of shipments, or the like. Indeed, many supply chains involve shipments of various components to a manufacturer. By using a semantic reasoning engine and a knowledge graph, the device may learn over time concepts such as “components of,” “consumed together,” or other relationships between otherwise seemingly unrelated shipments, particularly for different types of goods.
At step 1025, as detailed above, the device may initiate a mitigation action for the disruption that is performed with respect to the one or more other shipments. In some instances, the mitigation action may include providing an alert for display to a shipper or receiver of the one or more other shipments. In another embodiment, the mitigation action may include delaying or canceling the one or more other shipments. In a further embodiment, the mitigation action may also include ordering a replacement shipment for the first shipment. Procedure 1000 then ends at step 1030.
It should be noted that while certain steps within procedure 1000 may be optional as described above, the steps shown in
The techniques herein, therefore, allow for the use of semantic reasoning to learn and infer the relationships between shipments of different goods, such as when those goods are typically used together, such as components in an assembled product. In contrast to existing approaches that require these relationships to be explicitly defined, the techniques herein are able to learn these relationships over time and make recommendations by leveraging semantic reasoning.
While there have been shown and described illustrative embodiments that provide for semantic reasoning for supply chains, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to specific types of sensor systems, the techniques can be extended without undue experimentation to other use cases, as well.
The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.
