SMART ADAPTIVE SUPPLY CHAIN OPTIMIZATION SYSTEM (SASOS)

Abstract

The present invention provides smart adaptive supply chain optimization system (SASOS) (100) with a Real-time Data Integration Module (RTDIM) (101) designed to seamlessly aggregate, process, and deliver both real-time and historical data for applications requiring continuous monitoring and rapid decision-making. The system comprises a RTDIM (101) for collecting and processing live data streams, and a Data Integration Module (DIM) (102) for retrieving and synchronizing stored data. An Application Layer(103) processes the integrated data, interacting with an Operational Environment(104) to execute tasks or trigger events. A User Interface (UI) (105) enables real-time user interaction through dashboards and input mechanisms, while a Communication Interface (CI) (106) ensures secure, bi-directional communication with external devices and networks. This modular architecture enables efficient, scalable, and reliable operations, making it suitable for industrial automation, IoT platforms, smart monitoring systems, and other real-time applications.

Specification

Description:The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present invention relates to supply chain management systems, specifically to an intelligent and adaptive platform for optimizing supply chain operations. It encompasses real-time data processing, predictive analytics, and automated decision-making to improve the efficiency, resilience, and sustainability of supply chains. The invention integrates advanced technologies such as artificial intelligence (AI), Internet of Things (IoT), and blockchain for seamless coordination across suppliers, manufacturers, logistics providers, and customers, ensuring adaptive and demand-responsive supply chain optimization.
TECHNICAL FIELD
[002] The present invention pertains to the technical field of supply chain optimization and management systems. It involves the application of AI, machine learning, IoT, blockchain technology, and predictive analytics to enhance the performance, transparency, and adaptability of supply chains. The invention focuses on automating decision-making processes, real-time monitoring of assets, and dynamic coordination of supply chain entities to ensure optimal resource utilization, minimize operational disruptions, and improve demand forecasting.

BACKGROUND
[003] Traditional supply chains often suffer from inefficiencies, delays, and a lack of transparency due to fragmented data management and manual coordination. These issues lead to increased costs, underutilization of resources, and poor customer satisfaction. Furthermore, unexpected disruptions such as supplier issues or transportation delays are difficult to predict and mitigate effectively with legacy systems.
[004] Furthermore, modern supply chains require seamless data exchange across suppliers, warehouses, and logistics providers. However, the absence of real-time data integration makes it difficult to respond quickly to fluctuating market demands. This often results in overstocking or stockouts, increasing operational risks and reducing the overall agility of the supply chain.
[005] Supply chain optimization today demands not only real-time monitoring but also the ability to predict disruptions and adapt accordingly. AI and machine learning algorithms can forecast demand patterns and supply chain bottlenecks, enabling companies to adjust operations proactively. Yet, many existing systems lack these advanced capabilities.
[006] IoT devices provide real-time tracking of goods and assets, enabling companies to monitor shipments and inventory throughout the supply chain. Blockchain technology enhances transparency and trust by creating tamper-proof records of transactions and activities. However, integrating these technologies within a unified platform remains a significant challenge.
[007] Environmental concerns and unpredictable events-such as pandemics or natural disasters-have highlighted the need for resilient and sustainable supply chains. Companies must balance operational efficiency with environmental responsibility, ensuring that supply chain operations are agile and sustainable. There is a growing demand for systems that can dynamically optimize resources, minimize waste, and support sustainability goals.
[008] CN-202410554191-A is related to relates to the technical field of intelligent manufacturing and industrial automation, in particular to a data processing method, a system, a device and a storage medium of an intelligent manufacturing factory, which integrate Internet of things equipment, a machine learning technology and a digital twin model to optimize the production flow of the intelligent manufacturing factory; by collecting and analysing critical operational data on the production line in real time, the system is able to generate accurate predictive models based on historical and real time data; the models help to simulate the risk- free production flow on the digital twin platform, identify potential risks and propose process optimization suggestions; in addition, the system combines production data with market data, predicts market demands through machine learning analysis, and supports the establishment of a data- driven production adjustment strategy; through the measures, the invention obviously improves the production efficiency, reduces the operation cost, improves the product quality and the market response speed, and enhances the competitiveness of enterprises. Integrating IoT equipment, machine learning models, and digital twin technology into a seamless system requires significant technical expertise and financial investment. Many small and medium-sized enterprises (SMEs) may find it challenging to adopt such a comprehensive solution due to the high costs of infrastructure, software development, and skilled labour required to maintain the system. This can limit the widespread adoption of the technology. SASOS provides real-time adaptive control across multiple nodes in the supply chain, making it more resilient to external disruptions compared to the fixed optimization within a manufacturing setup

[009] EP-4099238-A1 relates to an integrated system for supply chain management, which comprises a data interface for gathering external data, a control tower for performing integrated simulation, analysis, and forecast on a process of the supply chain management with the gathered data and for providing new or changed plans of the supply chain, and a service repository for providing corresponding services to the control tower in order to complete the integrated simulation, analysis, and forecast. The patent describes a central control tower as the core component for managing supply chain processes, simulations, and forecasts. This centralization can create bottlenecks in decision-making and reduce the system's responsiveness, especially in highly dynamic or distributed supply chains. However, in cases of network disruptions or control tower failures, the entire supply chain may face delays or disruptions, lacking the flexibility of decentralized or edge-computing-based approaches. Moreover, the lack of real-time, automated intervention mechanisms makes it less effective in dealing with unexpected disruptions, as it may rely heavily on pre-planned scenarios or manual input to adjust supply chain operations. Our invention involves the distributed adaptive decision-making in SASOS which ensures superior real-time operational agility and resilience across the supply chain, whereas EP-4099238-A1 focuses on centralized simulation and forecasting, limiting its responsiveness to unforeseen events.

[0010] US-20230306347-A1 relates to systems and methods for intelligently optimizing supply chain is provided. In particular, the systems and methods provide the capability to configure supply chain systems so as to balance between cost and service is optimized and profitability maximized; configure system parameters to respond to both current and future risks; ensure that variability is built into plans enabling maximized efficiency; and human error and bias are eliminated from the planning process such that pro- active rather than reactive behaviour becomes the norm. However, if data is inaccurate, incomplete, or outdated, the system's ability to predict risks, optimize costs, or balance service levels could be compromised. Moreover, the system needs manual interventions in high-complexity scenarios, reducing its ability to provide seamless real-time optimizations. In contrast, SASOS is our invention goes beyond predictive optimization by offering real-time, adaptive adjustments across the entire supply chain. It continuously responds to disruptions such as transport delays, supplier failures, and demand fluctuations using decentralized nodes and dynamic coordination among multiple actors. Furthermore, SASOS is a real-time adaptive and decentralized architecture which ensures that supply chain operations remain agile and disruption-proof, offering superior resilience.

[0011] The present invention addresses the above shortcomings of the prior art. However, the present invention is entirely different from the prior art in terms of novelty and technological advancement.
OBJECT
[0012] The primary objective of the SASOS is to significantly enhance real-time agility within supply chain operations. By leveraging advanced technologies such as artificial intelligence, machine learning, and IoT devices, SASOS aims to create a responsive and adaptive supply chain environment. The system continuously monitors supply chain activities and gathers real-time data across various nodes, including suppliers, manufacturers, and logistics providers. This information enables the system to identify potential disruptions, demand fluctuations, and operational inefficiencies instantaneously. By implementing adaptive algorithms, SASOS can automatically adjust processes, inventory levels, and logistics strategies in response to changing conditions, ensuring minimal downtime and optimized resource utilization. Ultimately, this objective seeks to foster a more resilient supply chain that can quickly adapt to external challenges, thereby improving overall operational efficiency, reducing costs, and enhancing customer satisfaction through timely and reliable service delivery.
[0013] Another key objective is to Another key objective of SASOS is to promote data-driven decision-making that supports sustainability within the supply chain. By integrating predictive analytics and machine learning, the system aims to analyse vast amounts of historical and real-time data to forecast demand patterns, identify waste, and optimize resource allocation. This objective is centred around enabling organizations to make informed decisions that align with sustainability goals, such as reducing carbon footprints and minimizing resource consumption. SASOS facilitates a holistic view of the supply chain, allowing stakeholders to assess the environmental impact of their operations and implement strategies for sustainable practices. By combining production data with market insights, the system helps organizations to develop sustainable sourcing strategies, enhance product lifecycle management, and reduce waste through efficient logistics planning. Ultimately, this objective seeks to transform supply chain management into a more sustainable practice, ensuring long-term viability and compliance with environmental regulations while enhancing corporate social responsibility.

SUMMARY
[0014] The present invention discloses a method for SASOS leverages advanced technologies such as artificial intelligence, machine learning, and IoT devices to collect and analyse real-time data across various nodes in the supply chain, including suppliers, manufacturers, and logistics providers. This integration enables comprehensive monitoring of supply chain activities.
[0015] The system employs adaptive algorithms that automatically adjust supply chain processes in response to real-time disruptions, demand fluctuations, and operational inefficiencies. This capability enhances the agility of supply chain operations, ensuring minimal downtime and optimized resource utilization.
[0016] SASOS utilizes predictive analytics to forecast demand patterns and identify potential risks. By analysing historical and real-time data, the system provides actionable insights for proactive decision-making, helping organizations anticipate changes in market conditions and adjust their strategies accordingly.
[0017] The system emphasizes sustainability by promoting data-driven decision-making that reduces waste, optimizes resource allocation, and minimizes carbon footprints. SASOS supports the development of sustainable sourcing strategies and enhances product lifecycle management to ensure compliance with environmental regulations.
[0018] By improving operational efficiency, reducing costs, and fostering timely responses to customer needs, SASOS aims to enhance overall customer satisfaction. The system's ability to provide reliable service delivery positions organizations to better meet market demands and strengthen their competitive edge in the industry.
[0019] In yet another embodiment, the combination of precise laser micromachining and effective adhesive bonding results in a repair that withstands mechanical stresses and environmental conditions, extending the service life of the composite structure.
BRIEF DESCRIPTION
[0020] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present subject matter, an example of the construction of the present subject matter is provided as figures; however, the invention is not limited to the specific method disclosed in the document and the figures.
[0021] The present subject matter is described in detail with reference to the accompanying figures. In the figures, reference numbers are used to indicate a composite, its components and the process steps. The same numbers are used throughout the drawings to refer to various features of the present subject matter.
[0022] SASOS is an innovative platform designed to enhance supply chain efficiency and resilience by integrating real-time data collection, artificial intelligence, and machine learning technologies. By continuously monitoring supply chain activities across multiple nodes, SASOS dynamically adjusts processes in response to disruptions, demand fluctuations, and operational inefficiencies. This adaptability ensures minimal downtime and optimized resource utilization, enabling organizations to respond swiftly to changing market conditions and improve overall operational performance.
[0023] The system also emphasizes sustainability by leveraging predictive analytics to forecast demand patterns and identify potential risks, facilitating data-driven decision-making. By analysing historical and real-time data, SASOS helps organizations implement sustainable practices, reduce waste, and optimize resource allocation throughout the supply chain. This focus on sustainability not only ensures compliance with environmental regulations but also enhances corporate social responsibility, positioning organizations to meet customer expectations while contributing to a greener economy.
[0024] FIG 1: Illustrates a smart adaptive supply chain optimization system (SASOS).

DETAILED DESCRIPTION
[0025] Some of the embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.
[0026] The following description includes the preferred best mode of one embodiment of the present invention. It shall be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting.
[0027] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art are able to recognize that the invention is not limited to the embodiments of drawings or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in a certain figure, for ease of illustration, and such omissions do not limit the embodiment outlined in any way. It should be understood that the drawings and details thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim. It is not suggested or represented that any or all of these matters form any part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0028] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure is thorough and complete and conveys fully the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0029] The system architecture smart adaptive supply chain optimization system (SASOS) (100) depicted in the drawing comprises multiple interconnected modules that work in synergy to provide real-time data integration and communication. Each module plays a critical role in ensuring the flow of data from integration to the final user interaction. Real-time Data Integration Module (RTDIM) (101) is responsible for aggregating and integrating data in real time from various internal and external sources. It acts as a dynamic interface, collecting, processing, and sending data continuously to ensure that the rest of the system works with the latest information. The RTDIM (101) operates in conjunction with the Data Integration Module (DIM) (102) to synchronize batch data streams with real-time events. DIM (102) acts as a complementary unit to the RTDIM (101) by handling bulk data processing and historical data integration. It stores and retrieves data sets needed for operations that do not require real-time input. The interaction between DIM (102) and RTDIM (101) ensures seamless data flow, preventing delays in applications that require both historical and live data access. Application Layer (103) serves as the core processing unit, receiving inputs from the RTDIM and interacting with the Operational Environment (104) to implement application logic. This layer ensures that incoming data is processed according to the application's requirements and outputs the relevant results. The user interface (UI) (105) is a front-end module that enables user interaction with the system. It receives processed data from the application layer and displays it to the user in a comprehensible format. The UI allows users to input commands or configurations, which are sent back to the system for processing. The communication interface (CI) (106) handles the transmission and reception of data between the system and external devices or communication channels. It ensures that data can be securely transmitted and received in real time, enabling remote operations and notifications.
[0030] In an embodiment, the SASOS (100) as shown in FIG. 1, collects real-time data and integrates it with the bulk data managed by the DIM (102). The Application Layer (103) processes the incoming data streams and communicates with the Operational Environment (104) to execute real-world tasks. Processed data is delivered to the User Interface (UI) (105), where it is displayed and interacted with by the user. The Communication Interface (CI) (106) enables external systems to send or receive commands and data from the central system, ensuring seamless remote communication.
[0031] In another embodiment, the above disclosure is a description of the invention and is not intended to limit the scope of the invention. Other variations and modifications of the above-described embodiment shall be apparent to those skilled in the art and are intended to fall within the scope of the invention as defined in the following claims.
, Claims:1. A real-time data integration and communication system, comprising:
a) a Real-time Data Integration Module (RTDIM) (101) configured to aggregate, process, and deliver data in real-time from multiple sources;
b) a Data Integration Module (DIM) (102) configured to store, retrieve, and integrate historical data with real-time data streams provided by the RTDIM;
c) an Application Layer (103) connected to the RTDIM and DIM, the application layer being configured to execute processing logic based on the integrated data and communicate with an Operational Environment (104) for system interactions;
d) a User Interface (UI) (105) connected to the application layer, the UI enabling real-time monitoring and interaction by displaying processed data and receiving user inputs; and
e) a Communication Interface (CI) (106) configured to transmit and receive data between the system and external devices or networks, supporting remote operations and communication.
2. The system of claim 1, wherein the RTDIM (101) continuously updates the application layer with new data, ensuring real-time decision-making without latency.
3. The system of claim 1, wherein the DIM (102) maintains a synchronized flow of historical data with real-time streams to facilitate batch processing and comprehensive data analysis.
4. The system of claim 1, wherein the Application Layer (103) is further configured to:
a) interact with the Operational Environment (104) to execute operational tasks based on processed data, and
b) trigger events in the operational environment in response to predefined conditions detected in the data.
5. The system of claim 1, wherein the Operational Environment (104) comprises physical devices, sensors, or external systems that provide input data or respond to output signals generated by the application layer.
6. The system of claim 1, wherein the User Interface (UI) (105) includes:
a) real-time dashboards for visualizing data trends and alerts, and
b) interactive components that allow users to input commands or modify system configurations.
7. The system of claim 1, wherein the Communication Interface (CI) (106) supports multiple communication protocols, including Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), and Transmission Control Protocol/Internet Protocol (TCP/IP), to facilitate data exchange between the system and external devices or cloud-based platforms.
8. The system of claim 1, wherein the CI (106) ensures encrypted data transmission, maintaining the security and privacy of data exchanges between the system and external devices.

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