Description:Field of the Invention

The present disclosure relates to supply chain automation systems, particularly to artificial intelligence and deep learning-enabled predictive frameworks for dynamic operational decision-making in logistics and distribution networks.

Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Conventional supply chain systems operate through rule-based logic and static thresholds configured for linear forecasting and pre-programmed reactions. These systems are limited in their ability to respond dynamically to unstructured signals, nonlinear disruptions, and interdependent fluctuations inherent in modern global logistics. Traditional enterprise resource planning (ERP) platforms and supply chain management (SCM) tools rely heavily on historical averages, lagging indicators, and manual inputs, leading to inefficiencies in decision-making under uncertainty.
The complexity of contemporary supply chains, exacerbated by globalization, fluctuating demand cycles, extreme weather, and geopolitical instability, necessitates real-time responsiveness and predictive foresight. Legacy systems are unable to assimilate real-time data from diverse sources or adapt to cascading effects triggered by localized disruptions. Although some modern platforms incorporate basic forecasting algorithms or dashboards, they lack integrated predictive analytics and are often reactive rather than proactive.
Prior attempts to infuse intelligence into supply chains have focused on siloed automation or statistical regression models with limited accuracy in complex contexts. There remains a substantial technological gap in solutions that can not only forecast disruptions but also recommend corrective actions, evaluate alternatives, and improve over time through machine learning. There is a need for integrated, end-to-end systems that leverage AI and deep learning to model intricate supply chain dynamics, anticipate operational challenges, and provide actionable prescriptions with self-learning capability. The present invention addresses these challenges by introducing an intelligent supply chain decision system leveraging neural predictive frameworks.
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
The present disclosure relates to supply chain automation systems, particularly to artificial intelligence and deep learning-enabled predictive frameworks for dynamic operational decision-making in logistics and distribution networks.

The disclosed system enables smart supply chain management by utilizing artificial intelligence and deep learning to anticipate, evaluate, and mitigate logistics disruptions through predictive modeling and prescriptive orchestration. At its core, the system comprises a modular pipeline that begins with the acquisition of diverse structured and unstructured data from various sources including inventory databases, shipment records, vendor communications, weather alerts, and economic trend feeds. This data is processed by a preprocessing engine that standardizes and formats it for neural model consumption.
A neural prediction framework is trained on historical and real-time logistics data to forecast events such as demand spikes, supplier delays, or transport bottlenecks. This framework utilizes advanced deep learning models capable of capturing temporal trends, spatial dependencies, and contextual anomalies. Forecasted disruptions or inefficiencies are passed to a decision orchestration engine, which generates prescriptive actions such as rescheduling shipments, adjusting inventory allocations, recommending alternate suppliers, or rerouting transportation logistics.
These decisions are communicated to execution modules or human operators through ranked dashboards with annotated confidence metrics. An adaptive feedback loop monitors outcomes and discrepancies between predicted and actual events. Based on this variance, the system retrains the neural models to enhance forecasting accuracy and prescription efficacy. Additional components include simulation interfaces for testing hypothetical operational strategies and NLP modules for extracting insights from unstructured textual sources.
The system is deployable in cloud-native or hybrid environments, supporting distributed inference and global coordination. Its modularity allows integration with existing ERP systems, and its feedback-driven architecture supports continuous improvement, delivering intelligent, responsive, and efficient supply chain operations.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a method flow diagram illustrating the sequential processing of supply chain data from ingestion through AI-based forecasting, optimization, and adaptive feedback.
FIG. 2 is a block diagram showing the modular architecture of the system, including key submodules such as the neural prediction framework, optimization logic, and API-driven external data integration.
FIG. 3 is a sequence diagram capturing time-ordered interactions among modules during a live supply chain optimization cycle.
Detailed Description
The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The present disclosure relates to supply chain automation systems, particularly to artificial intelligence and deep learning-enabled predictive frameworks for dynamic operational decision-making in logistics and distribution networks.

Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The invention disclosed herein relates to a modular intelligent supply chain system designed to operate under dynamic conditions using deep learning and artificial intelligence. The system is architected as an end-to-end intelligent framework that supports predictive analytics and prescriptive action synthesis across global logistics networks. Each component operates in coordination to ingest, process, analyze, and act upon supply chain signals in real time.
The data acquisition module collects supply chain data from multiple sources including enterprise resource planning systems, inventory control software, transportation logs, vendor management platforms, and external feeds. These include structured sources such as tabular inventory levels or shipment manifests, and unstructured formats such as incident reports or supplier communications. The ingestion layer supports API integration, stream processing, and event-driven triggers for high-frequency data capture.
Collected data is routed to the preprocessing engine, which applies data normalization, duplicate removal, outlier correction, and semantic labeling. Natural language processing submodules are employed to extract key entities and intent from free-text data, enhancing the system’s capacity to interpret subjective or informal information such as vendor notices or field reports. The engine generates standardized feature vectors across time, location, and modality dimensions.
The feature vectors are input to the neural prediction framework, which is composed of multiple deep learning models tailored to various supply chain forecasting tasks. Convolutional neural networks are applied to detect patterns in spatial data, while recurrent networks and transformer encoders process temporal and contextual sequences. These models are trained to recognize early signals of disruptions, such as shipment delays, supplier bottlenecks, or inventory surges. Each prediction includes a confidence score and time horizon.
Predicted events are passed to the decision orchestration engine, which evaluates potential actions through optimization models. This engine considers constraints such as transport cost, warehouse capacity, vendor reliability, and delivery deadlines. It generates ranked action sets, including inventory redistribution, supplier switching, schedule adjustments, and logistics rerouting. Each recommendation is tagged with expected benefit, implementation cost, and risk index. Output is presented via dashboard or API for direct execution or managerial review.
After deployment of recommended actions, the performance feedback loop monitors execution outcomes. The system compares actual events against forecasted disruptions, calculates error metrics, and triggers model retraining when thresholds are breached. The retraining pipeline adjusts model weights using supervised or reinforcement learning, depending on the feedback structure. Over time, model performance improves as it adapts to evolving logistics conditions.
In one embodiment, the system is applied to a consumer electronics supply chain with multiple global distribution nodes. When semiconductor shortages are predicted using upstream vendor data and macroeconomic signals, the system preemptively recommends reallocating orders across higher-capacity suppliers and rescheduling deliveries to avoid factory idling. The feedback loop confirms mitigation success, improving future sensitivity to similar disruptions.
In another embodiment, a food logistics company utilizes the system to forecast perishability risks based on weather data and truck telemetry. The orchestration engine recommends alternate cold-chain carriers for certain routes during high-temperature weeks. Execution results validate the improvement in spoilage reduction, and the model adapts to seasonal variance.
In a third embodiment, the system simulates labor unrest impact by ingesting real-time news alerts and employee sentiment from warehouse chat logs. It predicts operational slowdowns and suggests accelerating critical deliveries before forecasted strike periods. The resulting foresight ensures service continuity while maintaining cost efficiency.
Across embodiments, the system continuously refines itself through closed-loop feedback, achieves high prediction accuracy through layered neural inference, and offers operational agility by synthesizing viable, context-aware logistics actions. Its architecture allows seamless integration with enterprise ecosystems and scalable deployment in cloud or hybrid infrastructures, supporting responsive, efficient, and intelligent supply chain optimization.
FIG. 1 depicts a method flow diagram that represents the chronological operations carried out by the disclosed system for intelligent supply chain optimization. The method initiates at the data ingestion phase, wherein structured and unstructured information including inventory logs, shipment records, and environmental signals are collected from internal enterprise sources and third-party vendors. The preprocessing stage standardizes, cleanses, and enriches this data. A natural language processing unit extracts entities and sentiments from unstructured reports, emails, and memos, enhancing the semantic resolution of incoming signals.
Once cleaned and transformed, the data is fed into a neural prediction framework that utilizes deep learning techniques such as convolutional neural networks for spatial recognition of supply imbalance clusters and long short-term memory models to capture temporal event patterns. The forecasted outputs, consisting of predicted delays, demand surges, or bottlenecks, are passed to a constraint-aware decision orchestration engine, which generates actionable prescriptions using business rule constraints and cost parameters. These recommendations are executed via logistics software or human decision dashboards. A performance feedback loop captures execution outcomes, compares them with predictions, and adjusts model parameters in subsequent iterations to increase long-term accuracy.
FIG. 2 shows a block diagram of the system's architecture, revealing the modular composition of its key operational units. The central Data Acquisition Module receives input from ERP systems, IoT sensors, and external APIs. It is directly connected to the Preprocessing Engine, which houses a semantic parsing submodule and outlier detection logic. Feature vectors derived here are passed into the Neural Prediction Framework, which is comprised of a model stack including spatial-temporal forecasting layers and attention mechanisms.
Alongside, a Decision Orchestration Engine integrates outputs from the predictor with logistics rules, supplier contracts, and cost models. Internally, it houses a Simulation Interface that allows pre-testing of hypothetical scenarios prior to execution. Outputs from the decision engine flow into the Execution Dashboard, enabling human-in-the-loop decision oversight or automated enforcement. Real-world results are fed back into a Performance Monitoring and Feedback Layer, which in turn influences subsequent model retraining and recommendation tuning.
FIG. 3 presents a sequence diagram showing the chronological interaction among primary system modules during an optimization cycle. The process is initiated by the Data Acquisition Module which queries both internal and third-party APIs. The Preprocessing Module then structures the acquired data and sends the processed vectors to the Neural Prediction Engine. Once predictions are made, the Decision Orchestration Engine initiates optimization logic and transmits a ranked set of recommended actions to the Execution Dashboard.
Upon implementation, the Monitoring Layer records the execution metrics such as success/failure rates, delivery delays, or inventory corrections. This data is routed to the Feedback Loop, which evaluates predictive error margins and initiates retraining routines if thresholds are exceeded. This coordinated exchange ensures the system remains responsive and accurate under changing operational and environmental conditions.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
Claims

I/We Claims

Claim 1.
A smart supply chain optimization system utilizing artificial intelligence and deep learning for predictive operations management, the system comprising:
a data acquisition module configured to retrieve and synchronize structured and unstructured supply chain data from multiple internal and external sources, including inventory systems, shipment logs, vendor databases, weather feeds, and economic indicators;
a preprocessing engine operatively coupled to said data acquisition module, said preprocessing engine being configured to normalize, clean, and semantically label input datasets for downstream neural modeling;
a neural prediction framework comprising one or more deep learning models trained on historical supply chain activity data, said framework being configured to forecast demand fluctuations, logistic bottlenecks, supplier delays, inventory imbalances, and fulfillment constraints;
a decision orchestration engine interfaced with said neural prediction framework, said engine being programmed to synthesize prescriptive operational actions including inventory realignment, vendor switching, transportation rerouting, and warehouse allocation optimization;
a performance feedback loop configured to monitor implementation outcomes of prescriptive actions and retrain said neural prediction framework based on observed variances from forecasted events;
wherein said system operates iteratively to minimize supply chain disruptions and improve forecasting precision by integrating real-time operational signals and historic contextual trends.
Claim 2.
The system of claim 1, wherein said preprocessing engine includes a natural language processing submodule for extracting features from unstructured logistics notes, vendor emails, and incident reports.
Claim 3.
The system of claim 1, wherein said neural prediction framework comprises a hybrid architecture integrating convolutional neural networks for pattern recognition and recurrent neural networks for temporal event modeling.
Claim 4.
The system of claim 1, wherein said decision orchestration engine includes a constraint-aware optimization submodule configured to respect regulatory limits, cost ceilings, and capacity constraints during prescriptive synthesis.
Claim 5.
The system of claim 1, wherein said performance feedback loop includes a reinforcement learning component configured to update model weights based on long-term supply chain efficiency metrics.
Claim 6.
The system of claim 1, wherein said data acquisition module integrates an API gateway to subscribe to third-party data feeds including weather anomalies, customs processing delays, and geopolitical disruptions.
Claim 7.
The system of claim 1, wherein said system further comprises a simulation interface configured to model alternative logistics decisions and estimate their downstream effect on order fulfillment, delivery latency, and inventory turnover ratios.
Claim 8.
The system of claim 1, wherein said decision orchestration engine outputs recommendations in a ranked format, each action annotated with expected efficiency gain, execution cost, and risk-adjusted priority index.
Claim 9.
The system of claim 1, wherein said neural prediction framework employs attention mechanisms to focus on time-sensitive supply chain variables with high predictive salience.
Claim 10.
The system of claim 1, wherein said system is deployable across cloud, edge, and on-premise environments, enabling distributed inference and synchronized decision propagation across global supply chain nodes.

/
DATED 07 July 2025 PALLAVI SINHA
IN/PA- 4068
AGENT FOR THE APPLICANT

Smart Supply Chains Using AI and Deep Learning Techniques: Optimizing Operations with Neural Predictive Systems

A smart supply chain system is disclosed using AI and deep learning for predictive and prescriptive logistics optimization. The system includes data ingestion, preprocessing, neural prediction, and decision orchestration modules. It forecasts disruptions and recommends real-time operational actions such as rerouting, inventory shifts, and vendor adjustments. A feedback loop monitors intervention outcomes and retrains the models to enhance future predictions. The invention enables dynamic, efficient, and adaptive supply chain decision-making under uncertainty.

, Claims:I/We Claims

Claim 1.
A smart supply chain optimization system utilizing artificial intelligence and deep learning for predictive operations management, the system comprising:
a data acquisition module configured to retrieve and synchronize structured and unstructured supply chain data from multiple internal and external sources, including inventory systems, shipment logs, vendor databases, weather feeds, and economic indicators;
a preprocessing engine operatively coupled to said data acquisition module, said preprocessing engine being configured to normalize, clean, and semantically label input datasets for downstream neural modeling;
a neural prediction framework comprising one or more deep learning models trained on historical supply chain activity data, said framework being configured to forecast demand fluctuations, logistic bottlenecks, supplier delays, inventory imbalances, and fulfillment constraints;
a decision orchestration engine interfaced with said neural prediction framework, said engine being programmed to synthesize prescriptive operational actions including inventory realignment, vendor switching, transportation rerouting, and warehouse allocation optimization;
a performance feedback loop configured to monitor implementation outcomes of prescriptive actions and retrain said neural prediction framework based on observed variances from forecasted events;
wherein said system operates iteratively to minimize supply chain disruptions and improve forecasting precision by integrating real-time operational signals and historic contextual trends.
Claim 2.
The system of claim 1, wherein said preprocessing engine includes a natural language processing submodule for extracting features from unstructured logistics notes, vendor emails, and incident reports.
Claim 3.
The system of claim 1, wherein said neural prediction framework comprises a hybrid architecture integrating convolutional neural networks for pattern recognition and recurrent neural networks for temporal event modeling.
Claim 4.
The system of claim 1, wherein said decision orchestration engine includes a constraint-aware optimization submodule configured to respect regulatory limits, cost ceilings, and capacity constraints during prescriptive synthesis.
Claim 5.
The system of claim 1, wherein said performance feedback loop includes a reinforcement learning component configured to update model weights based on long-term supply chain efficiency metrics.
Claim 6.
The system of claim 1, wherein said data acquisition module integrates an API gateway to subscribe to third-party data feeds including weather anomalies, customs processing delays, and geopolitical disruptions.
Claim 7.
The system of claim 1, wherein said system further comprises a simulation interface configured to model alternative logistics decisions and estimate their downstream effect on order fulfillment, delivery latency, and inventory turnover ratios.
Claim 8.
The system of claim 1, wherein said decision orchestration engine outputs recommendations in a ranked format, each action annotated with expected efficiency gain, execution cost, and risk-adjusted priority index.
Claim 9.
The system of claim 1, wherein said neural prediction framework employs attention mechanisms to focus on time-sensitive supply chain variables with high predictive salience.
Claim 10.
The system of claim 1, wherein said system is deployable across cloud, edge, and on-premise environments, enabling distributed inference and synchronized decision propagation across global supply chain nodes.

/
DATED 07 July 2025 PALLAVI SINHA
IN/PA- 4068
AGENT FOR THE APPLICANT

Smart Supply Chains Using AI and Deep Learning Techniques: Optimizing Operations with Neural Predictive Systems