Automated multi-level inventory allocation system using machine learning-based criticality assessment

Abstract

An automated multi-level inventory allocation system (100) using machine learning-based criticality scoring, comprising: a) a demand forecasting and pattern recognition module configured to predict demand for items across multiple supply chain levels using historical and real-time data; b) a criticality scoring engine operatively coupled to the forecasting module, the engine configured to assign each inventory item a dynamic criticality score based on one or more parameters, including urgency, usage history, operational impact, and supply constraints, using machine learning algorithms;c) a multi-stage allocation optimizer configured to allocate inventory among a plurality of distribution nodes based on criticality ratings, forecasted demand, and logistical constraints using optimization techniques; d) an inventory visibility and synchronization interface configured to integrate with external systems and enable real-time tracking of inventory status across all stages; e) an exception and disruption management module configured to detect anomalies in supply or demand and trigger automatic or manual corrective actions; f) a self-learning feedback module configured to retrain forecasting and evaluation models based on actual performance data;andg) a compliance and policy management module configured to validate that inventory decisions comply with predefined business rules and regulatory requirements;h) whereby the system dynamically allocates inventory in real time to optimize availability, responsiveness and resilience in a multi-tier supply chain environment.;

Description

[0001] The present invention relates to the field of automated inventory management systems, particularly to intelligent resource allocation in multi-tiered supply chains. It involves the integration of machine learning algorithms for assessing item criticality and dynamically prioritizing distribution. This system is particularly suitable for sectors requiring high reliability and optimized inventory flow, such as healthcare, manufacturing, and retail logistics.

[0002] Modern supply chains, especially those operating across multiple tiers, face significant challenges in efficiently managing inventory due to dynamic demand patterns, supply uncertainties, and the lack of real-time prioritization mechanisms. Traditional inventory allocation systems often rely on static rules or past consumption trends, which fail to capture the criticality of items under changing conditions such as emergencies, supply chain disruptions, or fluctuating customer demands. As a result, vital items can be understocked or delivered late, leading to operational inefficiencies and service disruptions.

[0003] In multi-tiered supply networks, inventory decisions made at one node often impact other nodes, exacerbating shortages or creating overstocks. Existing systems are rarely able to adapt in real time or learn from historical patterns to make intelligent decisions. They also lack the ability to evaluate items based on their importance in a given context, such as patient-relevant devices in healthcare or high-demand components in manufacturing. This discrepancy leads to misallocation of resources, higher costs, and poorer system responsiveness.

[0004] To address these deficiencies, the proposed invention introduces an automated, machine-learning-based inventory allocation system that incorporates dynamic criticality assessment. By leveraging predictive analytics and intelligent prioritization, the system continuously evaluates the importance of inventory items at different levels and reallocates inventory to meet demand in real time with minimal loss. This approach aims to maximize the availability of critical items, reduce delays, and ensure the resilience of complex supply chains.

[0005] One objective of this disclosure is to enable intelligent and dynamic inventory allocation using machine learning-based criticality assessment.

[0006] Another objective of this disclosure is to improve the accuracy of demand forecasting through advanced predictive analytics.

[0007] Another objective of this disclosure is to ensure high availability of critical items across all levels of the supply chain.

[0008] Another objective of this disclosure is to reduce stockouts, excess inventory and related operational inefficiencies.

[0009] Another objective of this disclosure is to ensure consistent inventory transparency and synchronization across all systems.

[0010] Another objective of this disclosure is to improve supply chain resilience by detecting and managing disruptions in real time.

[0011] Another goal of this disclosure is the continuous improvement of system performance through self-learning feedback loops.

[0012] Another objective of this disclosure is to ensure compliance with business rules, contracts and regulatory requirements.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to an automated multi-level inventory allocation system that uses machine learning to optimize inventory distribution in complex supply chains. It eliminates manual decision-making by enabling data-driven, intelligent allocation based on dynamic parameters. The system ensures continuous efficiency and responsiveness in inventory operations.

[0015] Another embodiment of the present invention is a demand forecasting and pattern recognition module, which forms the backbone of the system by predicting future inventory requirements based on historical data and real-time inputs. This enables forward-looking planning and reduces the risk of overstocking or stockouts. Machine learning models enable high accuracy in demand forecasting.

[0016] Another embodiment of the present invention is the Criticality Scoring module, which evaluates each inventory item by assigning a score based on urgency, impact, consumption trends, and supply risk. These scores are dynamically updated and used to prioritize item movements, ensuring that critical items are always available when and where they are needed.

[0017] Another embodiment of the present invention is the Multi-Tier Allocation Optimizer, which uses optimization algorithms to distribute inventory among the various nodes of the supply chain. Criticality values, forecasted demand, logistics, and constraints are considered for efficient allocation. This leads to cost savings and improved supply security.

[0018] Another embodiment of the present invention is the inventory visibility and synchronization interface, which enables real-time tracking across all levels by integrating with ERP and warehouse systems. It provides a centralized dashboard for monitoring inventory levels, outstanding orders, and supply chain health. This transparency helps stakeholders make informed decisions.

[0019] Another embodiment of the present invention is the exception and disruption management module, which detects anomalies such as demand spikes, supplier delays, or logistical failures using AI-driven alert systems. It triggers automated or assisted corrective actions such as reallocations or emergency reordering. This increases the resilience of the existing network.

[0020] Another embodiment of the present invention is the self-learning feedback module, which continuously improves prediction and scoring models based on real-world performance. It analyzes past decisions and results to refine future predictions and assignments. The system adapts to changes, thus becoming increasingly intelligent over time.

[0021] Another embodiment of the present invention is the Compliance and Policy Management module, which ensures that all inventory decisions are consistent with legal, organizational, and contractual guidelines. It prevents violations of expiration dates, region-specific restrictions, or inventory policies.

[0022] The present invention relates to an automated multi-stage inventory allocation system (100) that uses machine learning to intelligently predict demand, assess item criticality, and allocate inventory in complex supply chains. It comprises seven key modules: a demand forecasting module for predicting inventory levels, a criticality assessment module for prioritizing items, and a multi-stage allocation optimizer for efficiently distributing inventory. A visibility interface ensures real-time tracking, while the exception management module dynamically handles disruptions. The self-learning module continuously improves system performance, and the compliance module ensures that all actions comply with regulatory and organizational standards. Together, these modules form a robust, data-driven inventory management ecosystem. Demand forecasting and pattern recognition module:

[0023] This module uses machine learning algorithms such as time series analysis, regression models, and deep learning to predict future demand trends at all levels of the supply chain. It considers historical consumption data, seasonal fluctuations, market dynamics, and real-time signals such as customer orders or delivery delays. The forecasted demand data forms the basis for subsequent modules and enables proactive planning instead of reactive inventory decisions. Criticality assessment module:

[0024] At the heart of the system is a criticality scoring module that evaluates each inventory item based on several weighted parameters, including item type, urgency, historical consumption criticality, failure impact, and downstream dependencies. Machine learning models, such as decision trees and random forests, are used to assign each SKU (stock-keeping unit) a dynamic criticality score, which determines the priority of allocation and replenishment across different nodes in the supply chain. Multi-stage allocation optimizer:

[0025] This module processes criticality assessments, real-time inventory, and transportation constraints to determine the optimal distribution of inventory among the various levels—suppliers, distribution centers, warehouses, and end users. Optimization algorithms (e.g., linear programming, genetic algorithms) are used to allocate inventory so that the most critical items are delivered first, taking cost and resource efficiency into account. Interface for inventory transparency and synchronization:

[0026] A unified dashboard interface enables real-time synchronization and visibility of inventory positions across all levels of the supply chain. This module integrates with existing ERP and warehouse systems via APIs to retrieve, update, and standardize inventory-related data. This visibility enables decision-makers to instantly monitor inventory levels, pending orders, and allocation decisions, promoting coordinated actions and accountability. Exception and incident management module:

[0027] This module automatically detects supply chain anomalies, such as delivery delays, stockouts, or demand spikes, using real-time alerts and anomaly detection algorithms. Upon detection of exceptions, it triggers automatic reallocations or reforecasts to minimize disruptions. It also supports manual interventions and scenario simulation tools so supply chain planners can intervene as needed. Self-learning feedback and continuous improvement module:

[0028] Every transaction, forecast, and allocation decision is fed back into the system to retrain and refine the model. This module leverages reinforcement learning and error analysis techniques to improve prediction accuracy, optimize scoring weights, and refine the overall allocation logic over time. It ensures that the system adapts to changing supply chain behavior and evolves for better future performance. Regulatory and Policy Compliance Management Module:

[0029] This module ensures that automated allocation complies with corporate policies, contractual obligations, and legal regulations. It encodes business rules, safety stock thresholds, and regional regulations, and provides rules-based validation before a distribution plan is executed. The module ensures ethical and legally compliant inventory flow, especially in regulated industries such as healthcare and pharmaceuticals.

[0030] The invention is explained again below with reference to the figure. It shows: Fig. : an illustration of an automated multi-stage inventory allocation system (100) using machine learning-based criticality assessment.

[0031] Fig.illustrates an automated multi-tiered inventory allocation system (100) that uses a machine learning-based criticality assessment. The automated multi-tiered inventory allocation system operates with a coordinated sequence of intelligent modules designed to optimize inventory management in complex supply chains. It begins with the demand prediction and pattern recognition module, which analyzes historical data, market trends, and real-time inputs to predict future demand with high accuracy. These predictions feed into the criticality scoring module, where each inventory item is assessed and, using machine learning algorithms, assigned a dynamic score based on urgency, consumption patterns, operational impact, and supply risk.The Multi-Tier Allocation Optimizer then processes these criticality assessments along with current inventory levels, transportation constraints, and demand forecasts to calculate the optimal distribution of inventory among suppliers, warehouses, and end-user locations using advanced optimization techniques. Meanwhile, the Inventory Visibility and Synchronization Interface integrates data from external ERP and WMS platforms to provide a unified, real-time view of inventory levels and movements. When anomalies such as sudden demand spikes, delays, or stockouts are detected, the Exception and Disruption Management module initiates automatic reallocation or alerts supply chain planners for corrective action. All operational data and results are fed into the self-learning Feedback module, which continuously refines the forecasting and allocation models through adaptive learning algorithms.Finally, all allocation actions are controlled by the Compliance and Policy Management module, which ensures compliance with business rules, regional regulations, safety stock thresholds, and contractual obligations, making the system not only intelligent and autonomous, but also reliable and compliant.

Claims

[1] Automated multi-level inventory allocation system (100) using machine learning-based criticality assessment, comprising: (a) a demand forecasting and pattern recognition module configured to predict the demand for items across multiple supply chain levels using historical and real-time data; (b) a criticality rating engine operatively coupled to the forecasting module, the engine configured to assign a dynamic criticality rating to each inventory item based on one or more parameters, including urgency, usage history, operational impacts and supply constraints, using machine learning algorithms; (c) a multi-stage allocation optimizer configured to allocate inventory among a plurality of distribution nodes based on criticality ratings, forecasted demand and logistical constraints using optimization techniques; (d) an inventory visibility and synchronisation interface configured to integrate with external systems and enable real-time tracking of inventory status across all stages; (e) an exception and fault management module configured to detect anomalies in supply or demand and trigger automatic or manual corrective actions; (f) a self-learning feedback module configured to retrain forecasting and evaluation models based on actual performance data; and g) a compliance and policy management module configured to validate that inventory decisions comply with predefined business rules and legal requirements; h) the system dynamically allocates inventory in real time to optimise availability, responsiveness and resilience in a multi-echelon supply chain environment. [2] The system (100) of claim 1, wherein the demand forecasting and pattern recognition module uses machine learning techniques including time series analysis, ARIMA, LSTM networks, or ensemble regression models. [3] The system (100) of claim 1, wherein the criticality assessment engine uses decision trees, random forests, or gradient boosting methods to calculate the criticality of items based on configurable weight parameters. [4] The system (100) of claim 1, wherein the allocation optimizer uses linear programming, genetic algorithms, or constraint satisfaction techniques to calculate optimal distribution routes and quantities. [5] The system (100) of claim 1, wherein the exception and disruption management module is configured to automatically reallocate inventory upon detection of anomalies such as stockouts, transportation delays, or unexpected demand spikes. [6] The system (100) of claim 1, wherein the inventory visibility and synchronization interface provides a dashboard with real-time data integration via APIs with ERP, WMS and logistics platforms. [7] The system (100) of claim 1, wherein the self-learning feedback engine uses reinforcement learning or gradient-based optimization to adjust the model parameters over time based on the prediction accuracy and the assignment results. [8] The system (100) of claim 1, wherein the regulatory and policy compliance management module includes customizable business rules related to safety stock levels, geographic restrictions, contract fulfillment, and product expiration restrictions.

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