AU2021104512A4 - SUPPLY CHAIN MANAGEMENT SYSTEM AND METHOD CONFIGURED BY AN AI EQUIPPED INTEGRATED IoT PLATFORM - Google Patents
SUPPLY CHAIN MANAGEMENT SYSTEM AND METHOD CONFIGURED BY AN AI EQUIPPED INTEGRATED IoT PLATFORM Download PDFInfo
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- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
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- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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Abstract
The present invention generally relates to a system and method for supply
chain management based on an AI equipped integrated IoT platform.The
system comprises of a plurality of IoT devices, a controller module that
controls the IoT devices, an analytics module to analyze an IoT data, a
storage module comprising a plurality of servers for storing the IoT data and
the processed data on cloud, a plurality of remote interfaces for
communication of the processed data to a plurality of end-users and a
communication module for transfer of data between the IoT devices, the
controller module, the storage module and the plurality of remote interfaces
via Internet or a WiFi network. The method comprises of monitoring and
management of physical assets, management of shipping and delivery,
processing data to generate forecasts, prediction outcomes and optimization
data and communicating relevant data to a plurality of entities.
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SUPPLY CHAIN MANAGEMENTSYSTEM AND METHOD CONFIGURED BY AN AI EQUIPPED INTEGRATED IoT PLATFORM
The present invention relates to an Artificial Intelligence (AI)
& Internet of Things (IoT) platform. In particular, the present invention relates to a system and method for supply chain management based on an AI equipped integrated IoT platform.
Supply chain management (SCM) is a business process through which businesses deliver goods to consumers via suppliers. SCM systems monitor and administer the movement of goods from manufacturing centre to the final delivery location. With the advancement of technology, innovative systems are on the rise. Existing SCM systems do not incorporate intelligent technology solutions like AI, IoT, Cloud computing etc. which may facilitate enterprises to gain competitive edge.
In the view of the forgoing discussion, it is clearly portrayed that there is a need to have a system and a method for supply chain management based on an AI equipped integrated IoT platform. The proposed invention aims to boost productivity, reduce delivery time and costs, and improve system resilience by forecasting system failures. Use of predictive analytics in the system, makes the decision making process for managers more efficient and robust.
The present disclosure seeks to provide a system that employs IoT-based AI programming to integrate technology into industries' logistics. The invention is aimed to provide a novel supply chain management system that facilitates valuable tools for businesses by the amalgamation of new IoT technologies with traditional SCM processes. It ensures to maximize productivity by efficient administration of raw materials, component parts, and finished goods as they move from the manufacturing centre to the final consumer. The invention's primary benefits are cost savings, increased profitability, and the ability for enterprises to gain a competitive edge. The integrated Internet of Things (IoT) platform combines sensory, communication and information processing technologies in a networked environment.
In an embodiment, a supply chain management system configured by an artificial intelligence equipped integrated IoT platform comprises a plurality of IoT devices comprising of one or more sensors, trackers, and smart devices, wherein each IoT device is associated with atleast one physical asset. The system further comprises a controller module that controls the IoT devices and obtains a sensor data from the sensor, wherein the sensor data relates to one or more variables of the physical asset. The system further comprises an analytics module comprising of software programs needed to analyze an IoT data collected from the IoT devices, wherein the software programs employ a plurality of analysis methods to generate a processed data comprising of forecasts and optimization outcomes. The system further comprises a storage module comprising a plurality of servers for storing the IoT data and the processed data on cloud. The system further comprises a plurality of remote interfaces for communication of the processed data to a plurality of end-users, wherein the end-users comprise of managers and consumers and wherein the remote interfaces include applications on the smart phone, tablets, and laptops of the end-users. Lastly, the system comprises a communication module for transfer of data between the IoT devices, the controller module, the storage module and the plurality of remote interfaces via Internet or a WiFi network.
In an embodiment, the method for supply chain management configured by an artificial intelligence equipped integrated IoT platform comprises of monitoring and management of a plurality of physical assets by location trackers, warehouse condition monitors and automated inventory managers wherein the plurality of assets comprises of inventories, goods, machineries and transport vehicles. The method further comprises management of shipping and delivery of a good by use of real-time geo-trackers, intelligent navigators and vehicle environment sensor-controllers for capturing detailed insights into the movement of the good and mitigating delay risks. Here, intelligent navigators aid the delivery personnel to get optimized route recommendations and diversion choices for avoiding traffic congestion and enabling faster deliveries. Also, vehicle environment sensors-controller measure and regulate temperature, pressure, and humidity inside vehicles for safeguarding the good's integrity. The method further comprises processing a physical asset data and a delivery data to generate an output data. Here, the output data comprise of forecasts, prediction outcomes and optimization data. Also, the output data is utilized in detecting equipment failure, detecting resource leaks, forecasting potential system failure, predicting system maintenance and developing contingency plans. The method further comprises communicating a relevant data to a plurality of entities. Here, the plurality of entities comprise of managers, operators, consumers and business owners. Here, the relevant data comprise of the physical asset data, the delivery data and the output data.
In another embodiment, the analytics module of the supply chain management system comprises of a plurality of processors for mathematical computation and data analysis using machine learning and predictive analytics techniques.
In another embodiment, the sensor data of the supply chain management system comprise of purchase order data, delivery data, vehicle data and environmental data.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of a supply chain management system based on an AI equipped integrated IoT platform in accordance with an embodiment of the present disclosure.
Figure 2 illustrates a flow chart of a method for supply chain management configured by an AI equipped integrated IoT platform in accordance with an embodiment of the present disclosure.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein. DETAILED DESCRIPTION
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Figure 1 illustrates a block diagram of a supply chain management system based on an AI equipped integrated IoT platform in accordance with an embodiment of the present disclosure.
The supply chain management system 100 configured by an artificial intelligence equipped integrated IoT platform comprises a plurality of IoT devices 102 comprising of one or more sensors, trackers, and smart devices, wherein each IoT device 102 is associated with atleast one physical asset. The system 100 further comprises a controller module 104 that controls the IoT devices 102 and obtains a sensor data from the sensor, wherein the sensor data relates to one or more variables of the physical asset. The system 100 further comprises an analytics module 106 comprising of software programs 108 needed to analyze an IoT data collected from the IoT devices 102, wherein the software programs 108 employ a plurality of analysis methods to generate a processed data comprising of forecasts and optimization outcomes. The system 100 further comprises a storage module 110 comprising a plurality of servers 112 for storing the IoT data and the processed data on cloud. The system 100 further comprises a plurality of remote interfaces 114 for communication of the processed data to a plurality of end-users, wherein the end-users comprise of managers and consumers and wherein the remote interfaces 114 include applications on the smart phone, tablets, and laptops of the end-users. Lastly, the system 100 comprises a communication module 116 for transfer of data between the IoT devices 102, the controller module 104, the storage module 110 and the plurality of remote interfaces 114 via Internet or a WiFi network.
The system facilitates different groups of entities like warehouse workers, managers and consumers. It aids employees, managers, operators which work with the IoT devices in decision making processes. Businesses can connect with consumers remotely via the system's application interface.
A business may have many physical assets like machinery, vehicles, containers, raw materials, goods/products etc. IoT devices like sensors, smart glasses, location trackers, inventory drones, communication devices and other hardware are connected with each of these physical assets. Smart glasses assist in improving worker efficiency and awareness. Real-time position trackers for goods can be used by warehouse staff for faster retrieval of goods. These devices help in uniquely identifying and monitoring each physical asset. They also facilitate real-time monitoring as well as acquisition of data about the physical assets during critical logistics activities.
Controllers provide an interface between remote operators for controlling the IoT devices and displaying information about the IoT ecosystem.
Analytics module may comprise of algorithms for statistical analysis, predictive modelling, data mining, text analytics, combinatorial optimization techniques, real-time scoring, and machine learning. These work on the data collected by IoT devices. This processed data is then used for precision forecasting and demand prediction throughout the supply chain. SCM managers rely on these forecasts and optimization outcomes to maintain status of assets and create adaptable contingency plans.
Figure 2 illustrates a flow chart of a method for supply chain management configured by an AI equipped integrated IoT platform in accordance with an embodiment of the present disclosure.
The method 200 for supply chain management configured by an artificial intelligence equipped integrated IoT platform comprises of monitoring and management 202 of a plurality of physical assets by location trackers, warehouse condition monitors and automated inventory managers wherein the plurality of assets comprises of inventories, goods, machineries and transport vehicles. The method 200 further comprises management 204 of shipping and delivery of a good by use of real-time geo-trackers, intelligent navigators and vehicle environment sensor-controllers for capturing detailed insights into the movement of the good and mitigating delay risks. Here, intelligent navigators aid the delivery personnel to get optimized route recommendations and diversion choices for avoiding traffic congestion and enabling faster deliveries. Also, vehicle environment sensors-controller measure and regulate temperature, pressure, and humidity inside vehicles for safeguarding the good's integrity. The method 200 further comprises processing 206 a physical asset data and a delivery data to generate an output data. Here, the output data comprise of forecasts, prediction outcomes and optimization data. Also, the output data is utilized in detecting equipment failure, detecting resource leaks, forecasting potential system failure, predicting system maintenance and developing contingency plans. The method 200 further comprises communicating 208 a relevant data to a plurality of entities. Here, the plurality of entities comprise of managers, operators, consumers and business owners. Here, the relevant data comprise of the physical asset data, the delivery data and the output data.
The physical asset Management process monitors and manages assets. Connected technologies such as sensors, RFID tags, beacons, and smart materials, effortlessly track each item. Warehouse operations are automated and remote controlled by connecting inventory drones to the warehouse infrastructure. It may also help in determining the optimal quantity of each good to order, thus minimizing costs on raw materials.
Logistics process comprise of delivery and fleet management. Goods are tracked as they move from the manufacturing centre to the final consumer by high-precision geo-trackers. Navigation for on-the-road drivers are improved by use of intelligent route finders which offer diversion choices and recommend routes that reduce traffic congestion. Logistic managers are fed with real-time data regarding the good's location and detailed insights into the movement of goods. Environmental sensors in vehicles monitor cargo conditions like temperature, pressure, and humidity, to safeguard the good's integrity. They help in maintaining the freshness of perishable or sensitive commodities. Additionally, the degrees of stress and vibration also are measured during the shipment.
Analysis of sensor data helps in detecting resource leaks, identifying inefficiencies and forecasting potential equipment/machine failure. This in turn can help SCM mangers to predict maintenance, planning for such failures.
Managers can track drivers and shipping to ensure they adhere to internal policies, that good is stored properly, and that no delays occur between the warehouse and the customer's doorstep. With real-time access to location and environmental data, managers can also communicate delivery information to consumers, resulting in a more personalized service.
In another embodiment, the analytics module of the supply chain management system comprises of a plurality of processors for mathematical computation and data analysis using machine learning and predictive analytics techniques.
In another embodiment, the sensor data of the supply chain management system comprise of purchase order data, delivery data, vehicle data and environmental data.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (4)
1. A supply chain management system configured by an artificial intelligence equipped integrated IoT platform, wherein the supply chain management system comprises: a plurality of IoT devices comprising of one or more sensors, trackers, and smart devices, wherein each IoT device is associated with atleast one physical asset;
a controller module that controls the plurality of IoT devices and obtains a sensor data from one of the one or more sensors, wherein the sensor data relates to one or more variables of the physical asset;
an analytics module comprising of a plurality of software programs for analyzing an IoT data collected from atleast one of the plurality of IoT devices, wherein the plurality of software programs employ analysis methods to generate a processed data comprising of forecasts and optimization outcomes;
a storage module comprising a plurality of servers for storing the IoT data and the processed data on cloud;
a plurality of remote interfaces for communication of the processed data to a plurality of end-users, wherein the plurality of end-users comprise of managers, business-owners and consumers and wherein the plurality of remote interfaces are system applications running on the smart phone, tablets and laptops of the plurality of end-users; and
a communication module for data-transfer between the plurality of IoT devices, the controller module, the storage module and the plurality of remote interfaces via Internet or a WiFi network.
2. The supply chain management system of claim 1, wherein the analytics module comprises of a plurality of processors for mathematical computation and data analysis using machine learning and predictive analytics techniques.
3. The supply chain management system of claim 1, wherein the sensor data comprises of purchase order data, delivery data, vehicle data and environmental data.
4. A method for supply chain management configured by an artificial intelligence equipped integrated IoT platform comprising following steps: monitoring and management of a plurality of physical assets by location trackers, warehouse condition monitors and automated inventory managers, wherein the plurality of physical assets comprises of inventories, goods, machineries and transport vehicles;
management of shipping and delivery of a good by use of real time geo-trackers, intelligent navigators and vehicle environment sensor-controllers for capturing detailed insights of the good's movement and mitigating delay risks, wherein the intelligent navigators aid delivery personnel to get optimized route recommendations and diversion choices by avoiding traffic congestion and enabling faster deliveries and wherein the vehicle environment sensors-controller measure and regulate temperature, pressure, and humidity inside vehicles for safeguarding the good's integrity; processing a physical asset data and a delivery data to generate an output data, wherein the output data comprise of forecasts, prediction outcomes and optimization data and wherein the output data is utilized in detecting equipment failure, detecting resource leaks, forecasting potential system failure, predicting system maintenance and developing contingency plans; and communicating relevant data to a plurality of entities, wherein the plurality of entities comprise of managers, operators, consumers and business owners and wherein the relevant data comprise of the physical asset data, the delivery data and the output data.
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