Warehouse management system

Abstract

This disclosure describes a computer-implemented method for efficiently managing warehouse operations by dynamically controlling tasks across multiple facilities. Upon receiving data related to a customer order, the system processes task sequences based on order-specific criteria such as delivery deadlines, product requirements, and inventory availability. A data processing module extracts order parameters and, through an orchestration module, adjusts the task sequence in real-time, addressing inventory levels, resource constraints, and operational conditions. The method leverages predictive analytics, historical data, and integration with external systems to optimize task assignment, ensuring efficient fulfillment. Additionally, the system can modify tasks based on customer preferences and environmental impact metrics, reallocating resources as needed to maintain operational continuity. This adaptable approach enhances warehouse flexibility, enabling tasks to be customized for unique orders, cross-docking processes, and specific customer requirements while minimizing delays and optimizing resource use.

Description

Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1TITLE WAREHOUSE MANAGEMENT SYSTEMTECHNICAL FIELD

[0001] The present disclosure relates to warehouse management systems (WMS), particularly a cloud-based WMS capable of dynamic task orchestration, real-time inventory management, multi-level component tracking, integration of light manufacturing processes, and artificial intelligence (Al)-based task optimization for improved warehouse efficiency.BACKGROUND

[0002] Traditional warehouse management systems face several limitations, including their inability to handle complex, custom, or dynamic workflows, lack dynamic task management, lack of Al-driven optimization, inefficient inventory tracking, lack scalability, and challenges in integrating value-added services like light manufacturing. Furthermore, conventional warehouse management systems fall short in supporting custom processes or unique business needs, especially in industries requiring multi-level component assembly tracking and rigorous traceability of serialized or lot-controlled items (e.g., aerospace, automotive, and electronics sectors). These shortcomings lead to inefficiencies in inventory management, task allocation, and order fulfillment, especially when operating across multiple warehouse locations.SUMMARY

[0003] The present disclosure describes various embodiments of a warehouse management system to dynamically generate and manage tasks based on real-time warehouse conditions, order specifics, and inventory availability. In one embodiment, the warehouse management system integrates advanced features such as Al-driven optimization, multi-level component tracking, and support for light manufacturing processes such as cable cutting, assembly, and custom labeling. In one embodiment, the warehouse management system ensures seamless scalability across multiple warehouse locations, allowing for centralized control and improved operational efficiency. In one embodiment, the warehouse management system employs cloud-based scalability enabling both on-premise and cloud-based operations with centralized control over multiple warehouses and global scalability.

[0004] In a first embodiment, the present disclosure describes a computer-implemented method for controlling operations within one or more warehouse facilities. An input data module receives an inbound data packet containing data parameters related to a customerAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1order of physical goods stored in one or more warehouse facilities. A data processing module processes the inbound data packet to manage order fulfillment. The data processing module extracts data parameters regarding the physical goods specified in the customer order. The data processing module analyzes the extracted data parameters for determining a sequenced set of physical tasks required for fulfilling the customer order, with each task allocating specific warehouse resources for operations. The data processing module directs the sequenced set of tasks to specific warehouse facilities configured to execute and monitor task progress through operational data feedback. An orchestration module in communication with the data processing module orchestrates the execution of tasks in response to operational data from the warehouse facilities, for fulfilling the customer order by dynamically reallocating resources among tasks based on operational changes in the operational data.

[0005] In conjunction with one or more embodiments of the first embodiment, the data processing module communicates data with an inventory module. The inventory module monitors inventory levels of the physical goods at one or more warehouse facilities. The inventory module optimizes storage locations of the physical goods and enhances pick-path efficiency. The inventory module traces serialized components through stages of assembly, storage, and shipment.

[0006] In conjunction with one or more embodiments of the first embodiment, the inventory module adjusts the sequenced set of tasks based on real-time inventory levels at the one or more warehouse facilities, adapting tasks to match current stock availability.

[0007] In conjunction with one or more embodiments of the first embodiment, the data processing module generates the sequenced set of tasks based on product-specific characteristics including at least one of assembly, labeling, or packaging requirements tailored to each product in the customer order.

[0008] In conjunction with one or more embodiments of the first embodiment, the data processing module integrates data from external systems to dynamically adjust the sequenced set of tasks, fulfilling the customer order accurately and efficiently.

[0009] In conjunction with one or more embodiments of the first embodiment, the data processing module applies predictive analytics based on historical operational data for proactively generating or adjusting the sequenced set of tasks to improve efficiency and prevent stock-outs.

[0010] In conjunction with one or more embodiments of the first embodiment, the data processing module incorporates cross-docking processes into the sequenced set of tasks toAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1directly transfer inbound goods to outbound shipping without intermediate storage, optimizing speed and reducing handling costs.

[0011] In conjunction with one or more embodiments of the first embodiment, the data processing module creates the sequenced set of tasks for unique or custom orders that require different fulfillment paths, including coordination with multiple suppliers to provide a tailored and efficient customer order processing.

[0012] In conjunction with one or more embodiments of the first embodiment, the data processing module generates the sequenced set of tasks based on customizable criteria, including urgency, delivery windows, or product perishability, for dynamically adjusting task execution to meet varying priorities.

[0013] In conjunction with one or more embodiments of the first embodiment, the data processing module creates the sequenced set of tasks based on supplier availability, lead times, delivery routes, or supplier performance data, for optimizing supplier selection for timely and cost-effective order fulfillment.

[0014] In conjunction with one or more embodiments of the first embodiment, the orchestration module adjusts or modifies the sequenced set of tasks based on the operational data received from each allocated warehouse facility.

[0015] In conjunction with one or more embodiments of the first embodiment, the orchestration module prioritizes tasks in the sequenced set of tasks based on customer order-related criteria, including delivery deadlines, product perishability, or order value, to optimize fulfillment efficiency.

[0016] In conjunction with one or more embodiments of the first embodiment, the orchestration module allocates tasks in the sequenced set of tasks to specific warehouse facilities based on geographic proximity to a delivery location or customer, for minimizing transportation time and costs.

[0017] In conjunction with one or more embodiments of the first embodiment, the orchestration module modifies tasks in the sequenced set of tasks based on availability of workforce, equipment, or other operational resources at the warehouse facility, for optimizing capacity and reducing delays.

[0018] In conjunction with one or more embodiments of the first embodiment, the orchestration module adjusts or triggers tasks in the sequenced set of tasks based onAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1specific events, including stock replenishment, equipment malfunctions, or updates to customer orders.

[0019] In conjunction with one or more embodiments of the first embodiment, the orchestration module customizes the sequenced set of tasks based on customer-specific preferences or requirements, including at least one of packaging, branding, or handling instructions provided in the inbound data packet

[0020] In conjunction with one or more embodiments of the first embodiment, the orchestration module detects failure or delay of a task in the sequenced set of tasks at a warehouse facility, and reallocating affected tasks to other warehouse facilities with available capacity or resources for maintaining operational efficiency.

[0021] In conjunction with one or more embodiments of the first embodiment, the orchestration module optimizes the sequenced set of tasks based on energy consumption metrics, transportation distances, or equipment efficiency, for reducing overall environmental footprint of warehouse operations.

[0022] In conjunction with one or more embodiments of the first embodiment, the orchestration module re-prioritizes tasks in the sequenced set of tasks based on detected delays or bottlenecks at one or more warehouse facilities to complete critical tasks first and meet customer deadlines.

[0023] In conjunction with one or more embodiments of the first embodiment, the orchestration module optimizes the sequenced set of tasks based on geographical location of warehouse facilities relative to delivery destinations or supply chain nodes, for minimizing transportation distances and lead times.

[0024] In conjunction with one or more embodiments of the first embodiment, the orchestration module allocates tasks in the sequenced set of tasks to one or more warehouse facilities based on inventory levels, workforce availability, transportation costs, or energy efficiency.

[0025] In conjunction with one or more embodiments of the first embodiment, the orchestration module retrieves operational data from multiple external systems, including Warehouse Management Systems (WMS), Supply Chain Management (SCM) systems, or Enterprise Resource Planning (ERP) systems, for informing task generation andorchestration.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1

[0026] In conjunction with one or more embodiments of the first embodiment, the orchestration module applies forecasting data based on historical operational performance to adjust the sequenced set of tasks based on predictive bottlenecks or resource constraints.

[0027] In conjunction with one or more embodiments of the first embodiment, the orchestration module adjusts the sequenced set of tasks based on operational data related to equipment efficiency, including at least one of machinery performance metrics or downtime rates, for assigning tasks to the most capable and available equipment.

[0028] In conjunction with one or more embodiments of the first embodiment, the orchestration module dynamically reallocates resources including workforce and machinery based on real-time operational data, for continuously optimizing the sequenced set of tasks to match available resources at each warehouse facility.

[0029] In conjunction with one or more embodiments of the first embodiment, a data output module communicates with the orchestration module automatically generates and transmits notifications to customers regarding order status updates or delays based on operational data from warehouse facilities.

[0030] In a second embodiment, the present disclosure describes a system for managing operations across one or more warehouse facilities. An input data module receives inbound data packets containing parameters associated with a customer order for physical goods stored at one or more warehouse facilities. A data processing module processor and memory executes instructions. The data processing module extracts data parameters from the inbound data packets related to the physical goods specified in the customer order. The data processing module analyzes the extracted data to generate a sequenced set of tasks needed to fulfill the customer order in which the tasks are allocated to one or more warehouse facilities. The data processing module transmits the sequenced set of tasks to the warehouse facilities. A plurality of communication interfaces located at each warehouse facility in which the communication interfaces receive the sequenced set of tasks from the data processing unit and send operational data, including status updates, back to the data processing unit. An orchestration module receives the operational data and adjusts the sequenced set of tasks in response to conditions at the warehouse facilities. A storage system for dynamic storage and retrieval of goods, in communication with the orchestration module and the data processing unit, enables optimization of goods location and retrieval paths within each warehouse facility based on the sequenced set of tasks. A tracking system includes sensor arrays and inventory tagging mechanisms, such as RFID or barcode systems, to track serialized components as they move through stages of assembly,Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1storage, and shipment, and to communicate tracked data to the data processing unit.

[0031] In conjunction with one or more embodiments of the second embodiment, a cloud-based control module supports centralized operations across multiple warehouse facilities, enabling remote management, real-time data synchronization, and operational oversight from a single access point.

[0032] In conjunction with one or more embodiments of the second embodiment, the data processing module includes a predictive analytics engine analyzes historical data and forecast demand, enabling the orchestration module to proactively adjust the sequenced set of tasks to prevent stockouts and optimize resource allocation.

[0033] In conjunction with one or more embodiments of the second embodiment, the orchestration module communicates with a workforce management system, assigns tasks to available personnel based on workload, proximity to task locations, and efficiency factors.

[0034] In conjunction with one or more embodiments of the second embodiment, the input data module receives data from external supply chain systems, including Supplier Management Systems and Logistics Systems, providing the data processing module with supply availability and lead time information to adjust task sequencing.

[0035] In conjunction with one or more embodiments of the second embodiment, the storage system with modular storage units can be rearranged dynamically based on realtime inventory demands and repositioned by an automated system to reduce retrieval time for frequently ordered goods.

[0036] In conjunction with one or more embodiments of the second embodiment, the tracking system further comprises an energy management module to monitor energy consumption metrics across robotic and mechanical systems within each warehouse, enabling the orchestration module to optimize task sequencing to minimize energy use.

[0037] In conjunction with one or more embodiments of the second embodiment, a customer communication module generates automated notifications for customers, providing real-time updates on order fulfillment status based on data received from the warehouse facilities.

[0038] In conjunction with one or more embodiments of the second embodiment, a geographical mapping module within the data processing module determines optimal warehouse facility assignments based on customer delivery locations, minimizingAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1transportation time and logistics costs.

[0039] In conjunction with one or more embodiments of the second embodiment, a machine learning module is embedded in the data processing module. The machine learning module continuously refines task sequencing based on real-time performance data, historical data, and evolving inventory patterns.

[0040] In conjunction with one or more embodiments of the second embodiment, a global load-balancing system dynamically reallocates orders among warehouse facilities based on real-time data from the orchestration module, optimizing warehouse utilization across multiple locations.

[0041] In conjunction with one or more embodiments of the second embodiment, a custom order management system communicates with the data processing module. The custom order management system receives customer-specific preferences such as branding and handling instructions and relays this information to the orchestration module.

[0042] In conjunction with one or more embodiments of the second embodiment, the data processing module includes an order prioritization engine to determine task urgency based on customer delivery deadlines, order value, or perishability of goods, enabling the orchestration module to allocate resources accordingly.

[0043] In conjunction with one or more embodiments of the second embodiment, an integration layer interfaces with third-party enterprise resource planning (ERP) systems, enabling the data processing module to access supplier data, order fulfillment metrics, and transportation schedules, to optimize the generated sequenced set of tasks.

[0044] In conjunction with one or more embodiments of the second embodiment, the data processing module includes a route optimization module to determine optimal transportation routes for goods leaving each warehouse facility, based on real-time traffic data and logistics schedules.

[0045] In conjunction with one or more embodiments of the second embodiment, a human-machine collaboration interface enables warehouse personnel to override an automated sequenced set of tasks. The interface is connected to the orchestration module to update the sequenced set of tasks in real time.

[0046] In conjunction with one or more embodiments of the second embodiment, the data processing module includes a stock-balancing module to synchronize inventory levelsAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1across multiple warehouse facilities, triggering stock transfers as needed based on real-time demand and inventory conditions.

[0047] In conjunction with one or more embodiments of the second embodiment, an automated load-planning module optimizes product placement on transport vehicles for outbound shipments, minimizing handling and maximizing space efficiency.

[0048] In a third embodiment, the present disclosure describes a non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a warehouse management system, cause the warehouse management system to manage operations across one or more warehouse facilities. The instructions cause an input data module to receive inbound data packets containing parameters associated with a customer order of physical goods stored at one or more warehouse facilities, extract data parameters from the inbound data packets related to the physical goods specified in the customer order, and analyze the extracted data to determine a sequenced set of tasks required to fulfill the customer order, in which each task is allocated to one or more of the warehouse facilities. The sequenced set of tasks is transmitted to allocated warehouse facilities for execution. Operational data is received from each warehouse facility regarding task status, inventory levels, and order fulfillment progress. The instructions cause an orchestration module to adjust the sequenced set of tasks based on the operational data received from the warehouse facilities to improve order fulfillment accuracy and efficiency.

[0049] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors of the data processing module to communicate data with an inventory module to monitor inventory levels of the physical goods across the warehouse facilities, optimize storage location of physical goods for efficient picking paths, and trace serialized components through stages of assembly, storage, and shipment.

[0050] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to adjust the sequenced set of tasks based on real-time inventory levels at the warehouse facilities, adapting tasks to current stock availability.

[0051] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to generate a sequenced set of tasks based on product-specific characteristics, including at least one of assembly, labeling, or packagingAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1requirements tailored to each item in the customer order.

[0052] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to integrate data from external systems to dynamically adjust the sequenced set of tasks to achieve accurate and efficient order fulfillment.

[0053] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to apply predictive analytics based on historical operational data to proactively generate or adjust the sequenced set of tasks to improve efficiency and prevent stock shortages.

[0054] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to incorporate cross-docking processes into the sequenced set of tasks to facilitate direct transfer of inbound goods to outbound shipments, minimizing handling time and storage costs.

[0055] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to create the sequenced set of tasks to manage unique or custom orders requiring different fulfillment paths, including coordination with multiple suppliers for tailored order processing.

[0056] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to generate the sequenced set of tasks based on customizable criteria, including urgency, delivery timeframes, or product perishability, to prioritize task execution according to customer and order requirements.

[0057] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to create the sequenced set of tasks based on supplier availability, lead times, delivery routes, and supplier performance data to optimize supplier selection for timely and cost-effective order fulfillment.

[0058] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to adjust the sequenced set of tasks based on operational data received from allocated warehouse facilities to modify tasks dynamically.

[0059] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to prioritize tasks in the sequenced set of tasks based on criteria such as delivery deadlines, product perishability, or order value toAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1optimize order fulfillment efficiency.

[0060] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to allocate tasks in the sequenced set of tasks to specific warehouse facilities based on their geographic proximity to the customer or delivery location, to minimize transportation time and costs.

[0061] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to modify tasks in the sequenced set of tasks based on available workforce, equipment, or other operational resources at each warehouse facility to maximize efficiency and minimize delays.

[0062] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to trigger task adjustments based on specific events, such as stock replenishment, equipment malfunctions, or updates to customer orders.

[0063] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to customize the sequenced set of tasks based on customer-specific requirements, including packaging, branding, or handling preferences provided in the inbound data packet.

[0064] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to detect task failures or delays at a warehouse facility and reallocating affected tasks to other facilities with available resources to maintain operational efficiency.

[0065] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to optimize the sequenced set of tasks based on energy consumption metrics, transportation distances, or equipment efficiency to reduce environmental impact of warehouse operations.

[0066] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to re-prioritize tasks in the sequenced set of tasks based on detected delays or bottlenecks at one or more warehouse facilities to prioritize critical tasks and meet customer deadlines.

[0067] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to optimize the sequenced set of tasks basedAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1on geographical locations of warehouse facilities relative to delivery points or supply chain nodes to reduce lead times.

[0068] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to allocate tasks in the sequenced set of tasks to warehouse facilities based on factors including inventory levels, workforce availability, transportation costs, or energy efficiency.

[0069] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to retrieve operational data from external systems, including Warehouse Management Systems (WMS), Supply Chain Management (SCM) systems, or Enterprise Resource Planning (ERP) systems, to inform task generation and orchestration.

[0070] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to apply forecasting data based on historical operational performance to adjust the sequenced set of tasks in response to predictive bottlenecks or resource constraints.

[0071] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to adjust the sequenced set of tasks based on operational data concerning equipment performance metrics, including machinery efficiency or downtime rates, to allocate tasks to the most capable and available equipment.

[0072] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to dynamically reallocate resources such as workforce and machinery based on real-time operational data, continuously optimizing the sequenced set of tasks to match available resources at each warehouse facility.

[0073] In conjunction with one or more embodiments of the third embodiment, the instructions cause the one or more processors to automatically generate and transmit notifications to customers, based order status updates or delays, based on operational data received from warehouse facilities.

[0074] In various embodiments, the present disclosure provides a system, method, and computer-readable medium that emphasize specific technical steps, real-time data integration, and automated adjustments, showing clear improvements in physical warehouse operations. In various embodiments, the system, method, and computer-readable medium demonstrate substantial technical interactions between software modules and real-worldAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1warehouse processes, to address specific logistical issues through a specialized, technically integrated system. This integration of technical components to manage a physical environment provides a clearly described practical application. The system emphasizes specific technical components and interactions within the system that achieve warehouse management goals, ensuring that the invention is rooted in a concrete technological infrastructure to address real-world logistical problems. This system includes components, such as hardware (communication interfaces, tracking systems, and modular storage), specific processing units (predictive analytics engines, machine learning modules), and the operational interplay between components in a warehouse management environment. The specification establishes how these components work together to improve the efficiency, adaptability, and customer response capabilities of the overall system.BRIEF DESCRIPTION OF THE DRAWINGS

[0075] FIG. 1 illustrates an embodiment of a warehouse management system architecture.

[0076] FIG. 2 illustrates an embodiment of an order processing flow within the warehouse management system architecture depicted in FIG. 1.

[0077] FIG. 3 depicts an embodiment of an order processing flow for handling orders that require custom cable operations.

[0078] FIG. 4 illustrates a multi-level component assembly and delivery workflow, detailing steps for assembling Finished Goods #1 and #2.

[0079] FIG. 5 shows a workflow with task dependencies where certain prerequisites must be completed before proceeding to the next stage.

[0080] FIG. 6 displays an inventory structure with attributes like Unique Identifier, Type, Parent Inventory Identifier, and additional inventory attributes.

[0081] FIG. 7 is an embodiment of a computer-implemented method for controlling operations within one or more warehouse facilities.

[0082] FIG. 8 depicts an embodiment of a processor-based computing system suitable for use as a platform for executing certain routines of the presently disclosed warehouse management system.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1DESCRIPTION

[0083] A warehouse management system is described that introduces dynamic task orchestration, cloud-based scalability, Al-based inventory optimization, and the integration of light manufacturing into the workflow. These operate alone or in combination to handle complex, custom, or dynamic workflows, provide dynamic task management, provide Al-driven optimization, efficient inventory tracking, scalability, and integrates value-added services like light manufacturing. Furthermore, the warehouse management system supports custom processes or unique business needs, especially in industries requiring multi-level component assembly tracking and rigorous traceability of serialized or lot-controlled items (e.g., aerospace, automotive, and electronics sectors). The warehouse management system provide efficient inventory management, task allocation, and order fulfillment, especially when operating across multiple warehouse locations.

[0084] FIG. 1 illustrates an embodiment of a warehouse management system architecture 100. The warehouse management system architecture 100 includes various components and functions, all working to optimize warehouse operations and meet diverse fulfillment requirements. The various components and functions of the warehouse management system as described in the document, emphasizing dynamic task orchestration, cloud-based scalability, inventory traceability, and the integration of light manufacturing with warehouse operations.

[0085] In various embodiments, the warehouse management system architecture 100 provides a cloud-based warehouse management system 102 to dynamically generate and manage tasks based on real-time warehouse conditions, order specifics, and inventory availability. In various embodiments, the cloud-based warehouse management system 102 integrates features such as Al-driven optimization, multi-level component tracking, and support for light manufacturing processes such as cable cutting, assembly, and custom labeling. The cloud-based warehouse management system 102 provides seamless scalability across multiple warehouse locations, allowing for centralized control and improved operational efficiency. The cloud-based warehouse management system 102 adapts the picking, packing, and shipping processes based on specific customer requirements, including value-added services and tailored shipping preferences.

[0086] Several components of one embodiment of the cloud-based warehouse management system 102 may include a dynamic task orchestration module to generate tasks based on real-time conditions, inventory, and order specifications. In another embodiment, the Al-based optimization engine to analyze historical data and current warehouse conditions to optimize task prioritization, warehouse layout, and resourceAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1allocation. In another embodiment, the cloud-based warehouse management system 102 may include light manufacturing integration to allow for the fulfillment of custom orders through processes like cable cutting, stripping, and assembly. In another embodiment, the cloud-based warehouse management system 102 may include multi-level component tracking to provide traceability of serialized components throughout multiple stages of assembly and inventory management. These and other components of the cloud-based warehouse management system 102 will be described in greater detail below.

[0087] T urning back to FIG. 1 , in one embodiment, the warehouse management system architecture 100 operates on a cloud infrastructure, supporting both on-premise and cloudbased operations. The warehouse management system architecture 100 coordinates warehouse activities across multiple locations by dynamically generating and managing tasks. The system includes an Al optimization engine to continuously improve warehouse operations based on real-time feedback and historical performance data.

[0088] In various embodiments, the warehouse management system 102 includes an input data module 106, a data processing module 108, an orchestration module 110, and an inventory module 112, where each of these modules can communicate with each other directly or indirectly. The data processing module 108, an orchestration module 110, and an inventory module 112 can communicate directly or indirectly with a warehouse facility 118. More specifically, the data processing module 108, orchestration module 110, and inventory module 112 can communicate directly or indirectly with a warehouse control system 120 and warehouse operations 122. In another embodiment, the warehouse management system 102 may include a predictive analytics I cross-docking integration module 114. In another embodiment, the warehouse management system 102 may include a data output module 116. These structural elements work together to create a dynamic and responsive warehouse management system 102 that can adapt to changes in real-time, ensuring optimized task flow and efficient customer order fulfillment.

[0089] In various embodiments, the input data module 106 receives and processes inbound data packets from customer orders generated by an order management system 104. The input data module 106 includes an order input interface 105 to receive the customer orders either manually or through integration with the order management system 104 or external systems 126, such as an enterprise resource planning (ERP) system-based warehouse management solution. The input data module 106 stores customer order information, including data parameters related to the customer order, specifications, product details, and quantities in a database 107.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1

[0090] The external system 126, such as an ERP system, may be a type of software used by organizations to manage and integrate the core functions of their business. An ERP system centralizes and automates processes across different departments such as finance, human resources, procurement, supply chain, manufacturing, and customer relationship management (CRM), allowing for streamlined operations and real-time data access. An ERP system may include various features. Centralized data integrates various business processes into a single system, providing a unified database for all departments. Automation of routine business tasks like order processing, payroll, inventory tracking, and accounting. Real-time data access provides real-time information across departments, improving decision-making and visibility into business operations. Customization, often modular, allows companies to implement only the specific functions they need. Integration with other software and external platforms, like supply chain management tools, e-commerce platforms, and financial systems, and in one embodiment, the warehouse management system 102. Common ERP providers include SAP, Oracle ERP Cloud, Microsoft Dynamics 365, Infor, and NetSuite. ERP can improve efficiency, reduce operational costs, and foster collaboration across departments.

[0091] The inbound data packets include data about physical goods stored in one or more warehouse facilities 118, 130, 132. The inbound data packets contain data parameters related to the customer order of physical goods stored in one or more warehouse facilities 118, 130, 132. The input data module 106 is capable of handling large data packets with various data parameters and ensuring smooth communication with other system modules such as the data processing module 108, orchestration module 110, inventory module 112, among others. The input data module 110 converts sales orders into formats compatible with both the warehouse management system 102 and third-party systems such as, for example, the external system 126 (e.g., ERP systems).

[0092] In various embodiments, the data processing module 108 analyzes each inbound data packet containing customer order information, data parameters related to the customer order, specifications, product details, and quantities to identify product requirements, special handling needs, and deadlines. The data processing module 108 includes a parser to extract key details from the customer order (e.g., product type, quantities, customization), processes, and sequences one or more tasks necessary for order fulfillment. A criteria engine evaluates customer order conditions and any special requirements that influence task creation.

[0093] In one embodiment, the data processing module 108 determines the optimal sequenced set of tasks for fulfilling customer orders. In one embodiment, the dataAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1processing module 108 performs order fulfillment criteria analysis and considers factors such as delivery deadlines, product specifications, and inventory levels. In one embodiment, the data processing module 108 provides data integration and is capable of connecting with external systems, like ERP and SCM systems, to gather additional insights and adjust task sequences dynamically.

[0094] More particularly, in various embodiments the data processing module 108 processes the inbound data packet for the fulfillment of the customer order. Accordingly, the data processing module 108 may extract one or more data parameters associated with the physical goods specified in the customer order. The data processing module 108 may analyze the extracted data parameters to determine a sequenced set of tasks for fulfilling the customer order based on a set of criteria. Each task allocates one or more operations to one or more of the warehouse facilities. The data processing module 108 may push the sequenced set of tasks to the one or more allocated warehouse facilities 118, 130, 132. Each warehouse facility 118, 130, 132 can communicate operational data associated with each task.

[0095] In various embodiments, the data processing module 108 may communicate data directly or indirectly with an inventory module 112. The inventory module 112 may perform a method to monitor an inventory level of physical goods of the one or more warehouse facilities 118, 130, 132, optimize storage location and pick-path efficiency, and trace serialized components through assembly, storage, and shipment stages. The inventory module 112 provides real-time inventory levels for products and components required for fulfilling the customer order. Inventory tracking sensors collect real-time data on product availability and stock levels within the warehouse facility 118, 130, 132. An inventory database 111 continuously updates and stores current stock levels. An inventory-task correlation engine matches inventory availability with customer order requirements.

[0096] In various embodiments, the data processing module 108 generates a customized sequenced set of tasks tailored to each product's specific characteristics, such as assembly, labeling, or packaging requirements, for customer orders. The data processing module 108 integrates data from external systems to dynamically adjust these tasks, ensuring accurate and efficient order fulfillment. By applying predictive analytics based on historical operational data, the data processing module 108 proactively generates or adjusts tasks to improve efficiency and prevent stock-outs.

[0097] In various embodiments, for unique or custom orders, the data processing module 108 creates a sequenced set of tasks that require different fulfillment paths,Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1coordinating with multiple suppliers to provide a tailored and efficient customer ordering process. The data processing module 108 also generates task sequences based on customizable criteria, including urgency, delivery windows, and product perishability, allowing for dynamic adjustment to meet varying priorities.

[0098] Additionally, in various embodiments, the data processing module 108 creates task sequences based on supplier availability, considering factors such as lead times, delivery routes, and supplier performance data, optimizing supplier selection for timely and cost-effective order fulfillment.

[0099] In various embodiments, the orchestration module 110 includes an orchestration engine to adjust and orchestrate the task sequence dynamically based on changes in order specifications, inventory levels, and real-time warehouse conditions. A task adjustment module to modify task sequences when there are changes in order details or inventory availability. An orchestration controller ensures task execution is synchronized with real-time conditions, optimizing the workflow. A task prioritization engine prioritizes tasks based on urgency, stock availability, and warehouse conditions. A real-time decision engine continuously evaluates and optimizes task sequencing and orchestration based on live data. A data integration module integrates inventory data stored in the inventory database 111, operational monitoring, and order processing systems. Optimization algorithms evaluate real-time data to dynamically optimize the task flow and ensure timely order fulfillment.

[0100] In various embodiments, the orchestration module 110 manages and optimizes task execution across warehouse facilities 118, 130, 132. In one embodiment, the orchestration module 110 can communicate data directly or indirectly with the data processing module 108 to orchestrate the sequenced set of tasks based on the operational data received from the one more warehouse facility 118, 130, 132 to fulfill the customer order. The orchestration module 110 provides resource allocation by assigning tasks based on proximity to delivery location, warehouse capabilities, or availability of workforce and equipment. The orchestration module 110 dynamically re-prioritizes tasks in response to real-time conditions, such as delays, bottlenecks, or equipment issues. The orchestration module 110 also provides environmental optimization by minimizing the environmental footprint by considering transportation distances, energy consumption, and equipment efficiency.

[0101] The orchestration module 110 generates and manages tasks in real-time based on various factors such as order details, inventory availability, and warehouse conditions. Unlike traditional warehouse management systems, which rely on predefined workflows, theAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1dynamic task orchestration module 110 can adjust task sequences dynamically, allowing for greater flexibility and responsiveness in handling complex workflows. For example, if a specific item is out of stock, the dynamic task orchestration module 110 can prioritize other tasks while awaiting inventory replenishment, to minimize downtime. The system tracks task progress using various states, including “Task Available,” “Task Assigned,” and “Task Complete,” ensuring efficient task execution.

[0102] The implementation of the warehouse management system 102 provides several benefits including increased operational efficiency through dynamic task orchestration and automation of complex workflows. Unlike traditional warehouse management systems, which rely on predefined, rigid workflows that cannot adapt to changing warehouse conditions or customized orders, the orchestration module 110 provides an orchestration process that adjusts task sequences dynamically independent of predefined workflows, allowing for greater flexibility and responsiveness in handling complex workflows in handling orders. The dynamic task orchestration by the orchestration module 110 allows tasks to be generated and adjusted in real-time based on the specific requirements of each order and the current state of the warehouse facility 118, 130, 132. For example, when an order is received by the input date module 106, the orchestration module 110 evaluates the inventory, checks for custom requirements (e.g., cable cutting or assembly), and creates tasks dynamically to meet those needs. This task management system is flexible enough to handle unexpected changes, such as inventory shortages or last-minute customer changes, and allows the system to reconfigure workflows on the fly.

[0103] In various embodiments, the orchestration module 110 adjusts the sequenced set of tasks based on operational data from warehouse facilities 118, 130, 132. The orchestration module 110 prioritizes tasks using criteria like delivery deadlines, product perishability, or order value to enhance fulfillment efficiency. Tasks are allocated to specific warehouse facilities 118, 130, 1332 based on their proximity to delivery locations, minimizing transportation time and costs.

[0104] In various embodiments, the orchestration module 110 modifies tasks according to the availability of workforce, equipment, or other resources to optimize capacity and reduce delays. The orchestration module 110 can adjust or trigger tasks based on events such as stock replenishment, equipment malfunctions, or updates to customer orders.Customization of task sequences is possible based on customer-specific preferences, including special packaging or unique handling instructions.

[0105] If task failures or delays occur, in various embodiments, the orchestration moduleAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1110 reallocates tasks to other facilities with available capacity to maintain efficiency. The orchestration module 110 optimizes task sequences based on energy consumption, transportation distances, or equipment efficiency to reduce the environmental footprint. The module re-prioritizes tasks when delays or bottlenecks are detected to ensure critical tasks meet customer deadlines.

[0106] In various embodiments, task sequences are optimized based on the geographical location of warehouse facilities 118, 130, 132 relative to delivery destinations, minimizing transportation distances and lead times. Tasks are allocated based on criteria like inventory levels, workforce availability, transportation costs, and energy efficiency for comprehensive optimization.

[0107] In various embodiments, the orchestration module 110 retrieves data from external systems 126, such as Warehouse Management Systems, Supply Chain Management systems, or Enterprise Resource Planning systems, to inform task orchestration. The orchestration module 110 uses forecasting data to adjust tasks in anticipation of bottlenecks or resource constraints. Task sequences are adjusted based on equipment efficiency metrics to assign tasks to the most capable equipment.

[0108] In various embodiments, the orchestration module 110 dynamically reallocates resources, including workforce and machinery, based on real-time data, continuously optimizing task sequences to match available resources at each warehouse facility.

[0109] Additionally, in various embodiments, the orchestration module 110 provides dynamic task orchestration. The orchestration module 110 orchestrates tasks dynamically, adapting to real-time conditions and the specific requirements of each order, unlike traditional systems that rely on fixed workflows. When a customer order is created in the order management system 104, it is automatically transferred to the data input module 106. The warehouse management system 102 then transforms the sales order into a warehouse order, translating any custom requirements into specific warehouse tasks.

[0110] In various embodiments, the warehouse management system 102 generates a sequenced set of tasks based on the order's details, such as picking items, packing, or performing light manufacturing tasks like cable cutting and labeling. As warehouse conditions change, such as inventory availability or workforce capacity, the orchestration module 110 adjusts tasks in real-time, rerouting or prioritizing operations to ensure timely order completion.

[0111] For example, if an order requires custom cable cutting, the data processingAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1module 108 generates a cutting task and integrates it into the overall pick-pack-ship workflow. If there’s an inventory shortage, the orchestration module 110 automatically adjusts the process to resolve the issue efficiently.

[0112] In various aspects, the inventory module 112 monitors stock levels, manages pick-path optimization, and ensures effective storage. In monitoring inventory levels, the inventory module 112 tracks stock levels in real-time and adjusts task sequences accordingly. To optimize efficiency, the inventory module 112, adjusts storage locations and picking paths to streamline order processing. The inventory module 112 manages serialized components, tracking them through storage, assembly, and shipping to provide traceability.

[0113] In various embodiments, the data processing module 108 may communicate data directly or indirectly with the inventory module 112. The inventory module 112 may perform a method to monitor an inventory level of physical goods of the one or more warehouse facilities, optimize storage location and pick-path efficiency, and trace serialized components through assembly, storage, and shipment stages. In various embodiments, the inventory module 112 adjusts the sequenced set of tasks based on real-time inventory levels at the one or more warehouse facilities to ensure that tasks are adapted to current stock availability.

[0114] In various embodiments, the inventory module 112 is responsible for several critical warehouse functions, including stock level monitoring, pick-path optimization, and storage efficiency. By tracking stock levels in real-time, the inventory module 112 can dynamically adjust task sequences to maintain efficient operations. This includes optimizing storage locations and pick paths, streamlining the order fulfillment process. Additionally, the inventory module 112 manages serialized components, ensuring complete traceability through storage, assembly, and shipping. The inventory module 112 provides enhanced flexibility for custom orders and light manufacturing while strengthening inventory management and traceability.

[0115] In various embodiments, the warehouse management system 102 supports the assembly of components into finished goods with full traceability, critical for industries demanding high accountability. This traceability feature enables effective management of complex inventory requirements like serialized and lot-controlled items, ensuring compliance with regulatory and quality standards. By managing inventory exceptions (e.g., damaged goods or backorders), the inventory module 112 minimizes delays and enhances stock accuracy, reducing instances of stockouts, overstocking, and inventory shrinkage.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1

[0116] Additionally, in various embodiments, the inventory module 112 tracks multi-level assembly of components, linking them to finished goods with unique identifiers and attributes like part number and quantity. Multi-level component tracking provides detailed tracking of serialized components throughout the entire assembly process in the warehouse operations 122. Each component is assigned a unique identifier and tracked through multiple stages, including picking, assembly, and shipping. The warehouse management system 102 supports multi-stage assembly, ensuring that all components are accurately tracked and linked to their final products. For instance, if a product consists of several sub-components (e.g., wires, fasteners), the system tracks each component’s status, location, and quantity through the assembly stages. The tracking system is particularly useful for industries with stringent traceability requirements, such as automotive or aerospace.

[0117] Inventory types are defined within the warehouse management system 102: Actual (ACT) represents sellable inventory, Work in Progress (WIP) denotes partially transformed items, Component (COMP) is used for parts integrated into other items, and Ship Complete (SHC) indicates inventory that has been shipped. For instance, finished products are sent to storage if they are completed before their scheduled ship date. The inventory module 112 tracks each inventory component down to the serial or lot number, which is advantageous for industries like automotive and aerospace manufacturing.

[0118] Moreover, in various embodiments, the warehouse management system 102 offers partial assembly options when full inventory is unavailable, allowing flexibility in deciding whether to ship partial orders or await complete stock. In cases where items are unavailable at the primary warehouse 118, inventory can be sourced from alternate warehouse facilities 130, 132. The inventory module 112 assigns each item in the inventory a unique identifier and links to its assembled product via a parent inventory identifier.

[0119] In various embodiments, Al-driven features of the inventory module 112 will further enhance efficiency by analyzing historical order patterns, seasonal trends, and current inventory levels to optimize inventory placement, reduce picking times, and prioritize tasks based on urgency and warehouse conditions. This Al integration is projected to decrease labor costs, expedite order processing, and improve overall warehouse operations. The inventory module 112 also includes a cycle count feature, providing real-time monitoring of inventory levels, identifying discrepancies, and enabling corrective actions.

[0120] In various embodiments, the data output module 116 oversees customer communication by providing timely order updates and notifying customers of any delays. It generates and sends real-time status updates to customers based on order progress andAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1any detected issues. In one embodiment, the data output module 116 interacts with the orchestration module 110 to automatically create and send notifications about order status or delays, utilizing data from the warehouse facilities 118, 130, and 132.

[0121] In addition, the predictive analytics and cross-docking integration module 114 uses historical data to improve task planning and reduce storage costs. This module applies predictive models to forecast and adjust tasks proactively, to help prevent bottlenecks.Cross-docking further supports this goal by enabling the direct transfer of inbound goods to outbound shipments, minimizing handling time and associated costs.

[0122] The warehouse management system 102 interfaces with a warehouse control system 120 via a user interface I dashboard 119 to coordinate a range of warehouse operations 122, including both single and multi-level component assembly with detailed traceability for serialized or lot-controlled items. A real-time monitoring dashboard displays key metrics such as task status, inventory levels, and operational bottlenecks. An alert system notifies users of any critical changes or disruptions in the task sequence or inventory status.

[0123] The warehouse control system 120 monitors and adjusts each stage of assembly, from components to finished goods, supporting industries that require strict accountability, such as aerospace and automotive, by ensuring regulatory compliance and quality control. The warehouse control system 120 includes a warehouse operational condition monitoring system to monitor and adjust for real-time conditions in the warehouse 118, such as equipment status, workforce availability, and processing capacity. The warehouse control system 120 includes a warehouse sensor network to monitor factors such as equipment functionality, worker availability, and processing load. An operational data hub collects and analyzes warehouse conditions in real-time to optimize task allocation.

[0124] The warehouse control system 120 tracks material positions throughout warehouse operations 122. Various sensors, such as loT sensors, barcode scanners, and RFID tags / readers, monitor material locations, component statuses, and inventory levels in real-time, providing data back to the warehouse control system 120 for precise material tracking. For instance, RFID readers and barcode scanners help manage inventory and item positioning within the warehouse facility 118 or other warehouse facilities 130, 132.

[0125] Furthermore, the warehouse control system 120 manages multi-level assembly tracking, capturing the entire assembly process for each component, especially serialized or lot-controlled items. The warehouse control system 120 logs each step of the assemblyAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1process in the warehouse operations 122, ensuring comprehensive traceability of all materials used. Once a product is fully assembled, the warehouse control system 120 records the finished product’s serial number and its associated parts. For example, if parts A and B are assembled into component C, and component C is further incorporated with other parts to complete the final product, the WCS records each assembly stage. This feature is essential in industries like aerospace and automotive, where traceability and regulatory compliance are critical.

[0126] The warehouse management system 102 integrates light manufacturing processes into the warehouse operations 122 workflows via the light manufacturing integration module 134, enabling the execution of value-added services such as cable cutting, stripping, printing, and assembly. These processes are dynamically orchestrated alongside standard warehouse tasks, allowing for seamless custom order fulfillment. For example, if a customer orders custom-cut cables, the system generates tasks for cutting, labeling, and packaging the cables according to the customer’s specifications.

[0127] In some configurations, the light manufacturing integration module 134 adds value-added services, such as product customization and light manufacturing, directly into the warehouse operations 122 workflow. By automating these tasks — such as cable cutting, wire twisting, labeling, and printing — the system reduces manual processes, decreases errors, and increases throughput, leading to labor cost savings.

[0128] The integration of light manufacturing capabilities enables services like cable cutting to custom lengths, custom packaging, and wire twisting, streamlining processes that would otherwise require separate systems. The warehouse management system 102 handles orders requiring specific cable lengths, printed labels, and twisted wires to meet custom specifications. It creates tasks for these value-added services, incorporating them into the overall warehouse operations 122 workflow. Once customizations are complete, the warehouse management system 102 directs the warehouse team or automation systems to package and label products according to customer specifications. For example, if a customer requests a specific cable length with custom labeling, the system creates a cutting task, assigns labeling instructions to machines, and moves the finished product to packaging for shipping.

[0129] In various embodiments, warehouse operations 122 include custom labeling and shipping integration. This allows for the customization of shipping documents, labels, and manifests based on customer requirements. The warehouse management system 102 integrates with external shipping services, such as the transportation management systemAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1124, ensuring customers receive accurate shipping confirmations and advanced shipping notifications (ASNs). The transportation management system 124 also manages dock door assignments and shipping schedules, optimizing operations based on real-time conditions.

[0130] Through such seamless integration, the warehouse management system 102 eliminates the need for external systems or manual intervention, enhancing efficiency and responsiveness for customized orders.

[0131] The Al optimization engine 136 also supports advanced data analytics with cloud integration 128, creating a comprehensive platform for improved forecasting, decisionmaking, and proactive warehouse optimization. This engine provides real-time insights into inventory levels, order statuses, and overall warehouse operations, enabling quick, informed decisions and enhancing operational transparency. It prioritizes tasks based on factors such as order urgency, available resources, and warehouse conditions, ultimately lowering labor costs, speeding up order processing, and optimizing efficiency across warehouse operations.

[0132] Additionally, the Al-powered inventory optimization component in warehouse management system 102 leverages collected data to optimize tasks such as slotting inventory, establishing efficient picking paths, and managing order prioritization. The system gathers data on inventory levels, order patterns, warehouse layout, and operational metrics, which the Al then processes to determine ideal product placement (slotting) and recommend optimized picking routes for staff or robotic systems. Real-time Al-driven adjustments to scheduling and inventory movement ensure peak operational efficiency. For instance, if certain products are frequently ordered together, the Al will suggest positioning them close to each other, reducing picking and packing time.

[0133] In various embodiments, the warehouse management system’s 102 cloud-based scalability feature enables centralized control and coordination across multiple warehouses, providing a global view of inventory, tasks, and manufacturing processes. By operating through a cloud infrastructure, such as AWS, Microsoft Azure, or Google Cloud, the system offers seamless scaling across facilities 118, 130, and 132. Cloud-based centralization allows for inventory balancing, reducing delays and enhancing communication between warehouses. With cross-warehouse optimization, inventory transfers are managed efficiently to maintain balanced stock levels, preventing overstocking or stockouts. This centralized control facilitates real-time stock adjustments across locations, ensuring smooth and prompt fulfillment. For example, if one facility (e.g., warehouse 118) is low on stock for a specific item, the warehouse management system can instantly arrange for inventory transfers fromAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1facilities 130 or 132 to prevent order delays.

[0134] In various embodiments, within the cloud-based environment, the warehouse management system 102 coordinates with the orchestration module 110 to dynamically create and manage tasks based on real-time conditions, order details, and available inventory. The inventory module 112 provides detailed traceability for serialized components, multi-stage assembly processes, and real-time updates. The light manufacturing module 134 is seamlessly integrated into warehouse operations 122, enabling custom order fulfillment options such as cable cutting, stripping, labeling, and assembly as part of the workflow. The Al optimization engine 136, powered by advanced machine learning algorithms, optimizes warehouse layouts, task prioritization, and inventory placement using both historical data and real-time insights, delivering a streamlined, scalable, and globally coordinated warehouse management solution.

[0135] FIG. 2 illustrates an embodiment of an order processing flow 200 within the warehouse management system architecture 100 depicted in FIG. 1. As shown in FIG. 2 and FIG. 1, the input data module 106 receives 202 an order from the order management system 104. In response, the data processing module 108 generates 204 a sequenced set of tasks tailored to current inventory levels and operational conditions.

[0136] In various embodiments, the inventory module 112 actively manages 206 stock levels, tracking serialized components with RFID readers and inventory management systems to ensure real-time updates. The orchestration module 110, connected 210 to the warehouse facility 118, dynamically adjusts 208 task priorities and sequences by processing feedback from loT sensors and worker performance metrics. This flexibility allows for immediate responses to changes in the state of the assembly or manufacturing processes 212 within the warehouse facility 118, as well as other cloud-connected warehouse facilities 130, 132. The system coordinates multi-level assembly tasks and light manufacturing processes, adjusting based on specific customer order requirements and tracking 214 serialized components through each stage of assembly or manufacturing.

[0137] Real-time feedback loops inform the orchestration module 110, which makes on-the-fly adjustments 204 to the sequenced task set, ensuring optimal workflow. Leveraging the cloud computing infrastructure 128, the system provides scalable management across multiple warehouse facilities 130, 132, with synchronized task and inventory data available in real-time across all locations. Once assembly and manufacturing processes 212 are complete, the system coordinates fulfillment and shipping 216, managing packing, shipping, and delivery preferences through an integrated shipping module.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1

[0138] Together, this system integrates both hardware and software components to create a scalable, efficient, and responsive warehouse management system 102, enabling seamless operations and real-time adaptability across multiple facilities.

[0139] FIG. 3 depicts an embodiment of an order processing flow 300 for handling orders that require custom cable operations. Referring to FIG. 3 in conjunction with FIG. 1, the warehouse management system 102 orchestrates tasks dynamically based on real-time conditions and order-specific requirements, departing from the fixed workflows of traditional systems. When a customer order is created 302 within the order management system 104 (e.g., sales system), it is automatically forwarded to the warehouse management system 102, which translates 304 the sales order into a warehouse order and converts any custom requirements into specific warehouse tasks.

[0140] In various embodiments, the inventory module 112 offers real-time stock updates and tracks serialized components needed to fulfill the order, managing inventory through various I / O devices such as RFID scanners, loT sensors, and mobile handhelds. These devices capture real-time data on inventory status, item locations, environmental conditions, and user commands.

[0141] The data processing module 108 dynamically generates 306 a sequenced set of tasks according to order details, such as item picking, packing, or light manufacturing tasks (e.g., cable cutting, labeling). Task generation 306 includes phases for task creation 308, synchronization 310, availability 312, assignment 314, and completion 316, including picking 318 required components and monitoring inventory status 328.

[0142] As warehouse conditions shift (e.g., inventory availability, workforce levels), the orchestration module 110 adjusts tasks dynamically, rerouting or prioritizing them to ensure timely order completion. For orders involving custom cable cutting, the data processing module 108 generates a cutting task 344, which is seamlessly integrated into the overall workflow. In case of inventory shortages, the orchestration module 110 adapts the process to resolve the issue.

[0143] The system also supports integrated light manufacturing 320 within the warehouse management process, eliminating the need for separate systems to handle tasks like cutting 344, stripping 332, twisting 334, labeling 348, or assembling. When customization is required (e.g., specific cable lengths or labeling), tasks for these value-added services 330 are generated and incorporated into the workflow. Workers or automated machines carry out customization tasks 330, including cable cutting 344, wireAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1stripping 332, twisting 334, printing or coloring 336, dipping 338, multi-level component assembly 340, and other modifications as per customer requirements. Once custom izations are complete, the system directs packaging 322 and labeling 354 according to customer preferences, applying labels and readying the product for shipment.

[0144] Additionally, the warehouse management system 102 includes dock door management 350, coordinating inbound and outbound trailers and integrating with shipping 326 services to support customer-specified or optimized shipping options. Real-time vehicle location (AVL), automated dock door operations 356, and shipping confirmations 358 are also supported.

[0145] The system allows for comprehensive tracking of multi-level component assemblies 340, ensuring detailed monitoring of each assembled part from warehouse facility to final assembly. It tracks individual components, including serial and lot numbers, as they progress through the assembly process. For example, in the assembly of parts A and B into component C, the system logs all component data, enabling easy traceability for quality control or recalls if a component proves defective.

[0146] Operating on a cloud-based architecture, the warehouse management system 102 centrally controls multiple facilities 118, 130, 132 with real-time updates and scalability across global operations. The cloud platform facilitates inter-warehouse inventory transfers to balance stock and meet order demands efficiently. This centralized control improves visibility and coordination, as illustrated when a facility 118 running low on a product triggers an automated stock transfer from another facility 130, 132.

[0147] The system also includes an Al-driven inventory optimization engine that enhances warehouse operations by dynamically slotting 360 inventory, orchestrating tasks 362, optimizing picking paths, and prioritizing orders. Through data collection and analysis of inventory levels, past order trends, warehouse layouts, and operational performance, the Al component maximizes efficiency in product placement (slotting), recommends optimized picking paths, and schedules tasks in real time for peak warehouse performance. For instance, historical order data may suggest placing frequently co-ordered items closer together, reducing picking times.

[0148] FIG. 4 illustrates a multi-level component assembly and delivery workflow 400, detailing steps for assembling Finished Goods #1 and #2. The process includes tasks such as “Pick,” “Dye,” “Stripe,” “Twist,” “Assembly,” “Storage,” “Pack,” “Load,” and “Ship.” Components like wires 404, 406, 408, cabinets 410, and fasteners 412 are selected fromAttorney Docket No. 241770PCT - P / 101108 / WQ / SEC / 1inventory 402 and processed through multi-level assembly to produce the final goods.

[0149] The wires 404, 406, 408 parameters include Part Identification (e.g., Wire 1, Wire 2, Wire 3), Part Number, Quantity, and Attributes such as: Vendor, Receipt Date, Reel Size, and Reel Material (plastic / wood). The cabinet 410 parameters include Part Identification (e.g., Cabinet), Part Number, Quantity, and Attributes such as: Receipt Date and Serial Number. The screws 412 parameters include Part Identification (e.g., Screws), Part Number, Quantity, and Attributes such as: Receipt Date and Lot Number. Each part is tagged for inventory and location tracking.

[0150] The assembly begins by picking the first and second wires 404, 406 from inventory 402 and transferred to the dye task 414 location. The third wire 408 is selected and transferred to the stripe task 416 location. The first and second wires 404, 406 are transferred from the dye task 414 location to the stripe task 416 location. From the stripe task 416 location all three wires 404, 406, 408 are transferred to the twist task 418 location, where the wires 404, 406, 408 are twisted to produce finished good #1 420.

[0151] Next, the cabinet 410 and screws 412 are picked from inventory 402 and transferred to the assembly task 422 location along with finished good #1 420. Finished good #1 420, the cabinet 410, and the screws 412 are assembled into finished good #2424 at the assembly task 422 location. Finished good #2424 is then transferred to the storage task 426, the pack task 428, the load task 430, and the ship task 432, where finished good #2424 is shipped to the customer.

[0152] FIG. 5 shows a workflow 500 with task dependencies where certain prerequisites must be completed before proceeding to the next stage. For instance, picking wires 404 and 406 is required before starting the dye task 414. Each task follows a flow that includes task creation 502, synchronization 504, availability 506, assignment 508, and completion 510, triggering the next task upon completion.

[0153] With reference to FIG. 5 and FIG. 4, a new task state is created at the task creation stage 502. At the task sync stage 504, the system checks if any dependent tasks have been completed. For example, in FIG. 4, picking the first and second wires 404, 406 must be completed before the dye task 414 can be begin. The stripe task 416 cannot be completed until the inventory arrives. The three wires 404, 406, 408 must be stripped and transferred to the twist station before the twist task 418 becomes available. The end product from twisting the three wires 404, 406, 408 must be completed and transferred to the assembly station, as well as completion of picking the cabinet 410 and screws 412 fromAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1inventory before assembly can occur. At the task available stage 506 all material required to perform a task has arrived at the location to be performed. At the task assigned stage 508 an associate has confirmed receipt of the task and begins work. The associate catalogs any pertinent information about the task performed such as quantity picked, serial numbers, type and size of packaging material used, etc. At the task complete stage 510 the associate informs the system that the task is complete triggering the next task to perform.

[0154] FIG. 6 displays an inventory structure 600 with attributes like Unique Identifier, Type, Parent Inventory Identifier, and additional inventory attributes. Types include Actual (ACT), Work in Progress (WIP), Component (COMP), and Ship Complete (SHC). Parent Inventory Identifiers link components to finished goods, ensuring full traceability across assembly stages. Completed items may be stored if finished before the target shipping date, ensuring efficient management and allocation of stock.

[0155] The Unique Identifier is a numerical sequence identifying an inventory and all of the attributes associated with it. ACT is an inventory that is in a sellable state. WIP inventory type is inventory that is transformed but not in its final state. COMP inventory type is inventory consumed as a component of another. SHC inventory type is inventory that is shipped and no longer operable. The Parent Inventory Identifier links components to a finished good, such as when three wires are twisted to form Finished Good #1. Additional inventory attributes include part number and quantity. Completed products are sent to storage if finished before the target ship date.

[0156] FIG. 7 is an embodiment of a computer-implemented method 700 for controlling operations within one or more warehouse facilities. In various embodiments, the computer-implemented method 700 may be executed within the warehouse management system architecture 100 depicted in FIG. 1 by the computing environment 800 depicted in FIG. 8. With reference to FIGS. 7 together with FIG. 1, an input data module 106 receives 702 an inbound data packet containing data parameters related to a customer order of physical goods stored in one or more warehouse facilities. A data processing module 108 processes 704 the inbound data packet to manage order fulfillment. The data processing module 108 extracts 706 data parameters regarding the physical goods specified in the customer order; analyzes 708 the extracted data parameters for determining a sequenced set of physical tasks required for fulfilling the customer order, with each task allocating specific warehouse resources for operations; and directs 710 the sequenced set of tasks to specific warehouse facilities 118, 130, 132 to execute and monitor task progress through operational data feedback. An orchestration module 110 to communicate with the data processing module 108, orchestrates 712 the execution of tasks in response to real-time operational data fromAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1the warehouse facilities 118, 130, 132, for fulfilling the customer order by dynamically reallocating resources among tasks based on real-time operational changes in the operational data.

[0157] In one embodiment, the data processing module 108 communicates data with an inventory module 112 to monitor inventory levels of the physical goods at one or more warehouse facilities; optimize storage locations of the physical goods and enhancing pickpath efficiency; and trace serialized components through stages of assembly, storage, and shipment. In another embodiment, the inventory module 112 adjusts the sequenced set of tasks based on real-time inventory levels at the one or more warehouse facilities, adapting tasks to match current stock availability.

[0158] In one embodiment, the data processing module 108 generates the sequenced set of tasks based on product-specific characteristics including at least one of assembly, labeling, or packaging requirements tailored to each product in the customer order.

[0159] In one embodiment, the data processing module 108 integrates data from external systems to dynamically adjust the sequenced set of tasks, fulfilling the customer order accurately and efficiently.

[0160] In one embodiment, the data processing module 108 applies predictive analytics based on historical operational data for proactively generating or adjusting the sequenced set of tasks to improve efficiency and prevent stock-outs.

[0161] In one embodiment, the data processing module 108 incorporates cross-docking processes into the sequenced set of tasks to directly transfer inbound goods to outbound shipping without intermediate storage, optimizing speed and reducing handling costs.

[0162] In one embodiment, the data processing module 108 creates the sequenced set of tasks for unique or custom orders that require different fulfillment paths, including coordination with multiple suppliers to provide a tailored and efficient customer order processing.

[0163] In one embodiment, the data processing module 108 generates the sequenced set of tasks based on customizable criteria, including urgency, delivery windows, or product perishability, for dynamically adjusting task execution to meet varying priorities.

[0164] In one embodiment, the data processing module 108 creates the sequenced set of tasks based on supplier availability, lead times, delivery routes, or supplier performanceAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1data, for optimizing supplier selection for timely and cost-effective order fulfillment.

[0165] In one embodiment, the orchestration module 110 adjusts or modifies the sequenced set of tasks based on the operational data received from each allocated warehouse facility.

[0166] In one embodiment, the orchestration module 110 prioritizes tasks in the sequenced set of tasks based on customer order-related criteria, including delivery deadlines, product perishability, or order value, to optimize fulfillment efficiency.

[0167] In one embodiment, the orchestration module 110 allocates tasks in the sequenced set of tasks to specific warehouse facilities based on geographic proximity to a delivery location or customer, for minimizing transportation time and costs.

[0168] In one embodiment, the orchestration module 110 modifies tasks in the sequenced set of tasks based on availability of workforce, equipment, or other operational resources at the warehouse facility, for optimizing capacity and reducing delays.

[0169] In one embodiment, the orchestration module 110 adjusts or triggers tasks in the sequenced set of tasks based on specific events, including stock replenishment, equipment malfunctions, or updates to customer orders.

[0170] In one embodiment, the orchestration module 110 customizes the sequenced set of tasks based on customer-specific preferences or requirements, including at least one of packaging, branding, or handling instructions provided in the inbound data packet.

[0171] In one embodiment, the orchestration module 110 detects failure or delay of a task in the sequenced set of tasks at a warehouse facility, and reallocating affected tasks to other warehouse facilities with available capacity or resources for maintaining operational efficiency.

[0172] In one embodiment, the orchestration module 110 optimizes the sequenced set of tasks based on energy consumption metrics, transportation distances, or equipment efficiency, for reducing overall environmental footprint of warehouse operations.

[0173] In one embodiment, the orchestration module 110 re-prioritizes tasks in the sequenced set of tasks based on detected delays or bottlenecks at one or more warehouse facilities to complete critical tasks first and meet customer deadlines.

[0174] In one embodiment, the orchestration module 110 optimizes the sequenced setAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1of tasks based on geographical location of warehouse facilities relative to delivery destinations or supply chain nodes, for minimizing transportation distances and lead times.

[0175] In one embodiment, the orchestration module 110 allocates tasks in the sequenced set of tasks to one or more warehouse facilities based on inventory levels, workforce availability, transportation costs, or energy efficiency.

[0176] In one embodiment, the orchestration module 110 retrieves operational data from multiple external systems, including Warehouse Management Systems (WMS), Supply Chain Management (SCM) systems, or Enterprise Resource Planning (ERP) systems, for informing task generation and orchestration.

[0177] In one embodiment, the orchestration module 110 applies forecasting data based on historical operational performance to adjust the sequenced set of tasks based on predictive bottlenecks or resource constraints.

[0178] In one embodiment, the orchestration module 110 adjusts the sequenced set of tasks based on operational data related to equipment efficiency, including at least one of machinery performance metrics or downtime rates, for assigning tasks to the most capable and available equipment.

[0179] In one embodiment, the orchestration module 110 dynamically reallocates resources including workforce and machinery based on real-time operational data, for continuously optimizing the sequenced set of tasks to match available resources at each warehouse facility.

[0180] In one embodiment, a data output module 116 to communicate with the orchestration module 110 automatically generates and transmits notifications to customers regarding order status updates or delays based on operational data from warehouse facilities.

[0181] The disclosure now turns to FIG. 8 which illustrates an embodiment of a computing hardware environment 800 for advanced warehouse management. The computing hardware environment 800 is optimized for implementing the complex operations for warehouse management as described above in connection with FIGS. 1-7, integrating both a robust, modular hardware setup and a detailed processor-based computing system.

[0182] With reference to FIG. 8, the components of the system are in communication with each other using a system bus 805. The computing system 800 can include aAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1processing unit (CPU or processor) 810 and a system bus 805 that may couple various system components including the system memory 815, such as read only memory (ROM) 820 and random-access memory (RAM) 825, to the processor 810. The computing system 800 can include a cache 812 of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 810.

[0183] The computing system 800 can copy data from the memory 815, ROM 820, RAM 825, and / or storage device 830 to the cache 812 for quick access by the processor 810. In this way, the cache 812 can provide a performance boost that avoids processor delays while waiting for data. These and other modules can control the processor 810 to perform various actions. Other system memory 815 may be available for use as well. The memory 815 can include multiple different types of memory with different performance characteristics. The processor 810 can include any general-purpose processor and a hardware module or software module, such as module 1 832, module 2834, up to module n 836 (where n is an integer greater than 2) stored in the storage device 830, to control the processor 810 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 810 may essentially be a completely self-contained computing system, containing multiple cores or processors, a system bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

[0184] To enable user interaction with the computing system 800, an input device 845 can represent any number of input mechanisms, such as a microphone for speech, a touch-protected screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 835 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing system 800. The communications interface 840 can govern and manage the user input and system output. There may be no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

[0185] The storage device 830 can be a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memory, read only memory, and hybrids thereof.

[0186] As discussed above, the storage device 830 can include the software modulesAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1832, 834, 836 for controlling the processor 810. Other hardware or software modules are contemplated. The storage device 830 can be connected to the system bus 805. In some embodiments, a hardware module that performs a particular function can include a software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 810, system bus 805, output device 835, and so forth, to carry out the function. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

[0187] With reference to both FIG. 1 and FIG. 8, the disclosed warehouse management system 102 operates within a sophisticated, processor-based computing environment, exemplified by the computing system 800, designed to orchestrate, optimize, and secure warehouse operations with real-time precision. Central to the warehouse management system 102 is a high-performance processor 810, which coordinates essential functions across memory, storage, data inputs, and external networks via a system bus 805. Equipped with a dedicated cache 812, the processor 810 ensures rapid access and retrieval of critical data, minimizing latency and providing the computational capacity to process complex instructions related to task orchestration, inventory management, and order fulfillment including continuous Al-driven adjustments in task management, inventory tracking, and order fulfillment.

[0188] The computing system 800 includes a multi-tiered memory architecture where the system memory 815 includes both read-only memory 820 (ROM) and random-access memory 825 (RAM). One aspect of the computing system 800 is its multi-layered storage infrastructure, anchored by the storage device 830. This storage device 830 includes various forms of non-volatile memory, such as solid-state drives (SSDs) and magnetic storage, housing critical data and software modules 832, 834, 836 necessary for controlling the processor 810 and executing the warehouse management routines such as the modules 106, 108, 110, 112, 114, 116, described in FIG. 1, for example. The storage device 830 stores complex software algorithms and machine learning models that analyze historical data and real-time conditions, generating optimized task sequences for each warehouse facility.

[0189] This configuration supports high-speed processing for tasks and data updates essential for dynamic warehouse operations. While the ROM 820 provides core operational protocols and configurations, the high-speed RAM 825 enables the system to handle realtime updates to inventory levels, task queues, and predictive analytics. Furthermore, aAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1storage device 830, including SSDs and magnetic storage, houses critical data and software modules necessary for executing warehouse management functions. This storage system retains historical records, operational data, and machine learning models, enabling complex task sequencing and optimized inventory management.

[0190] Artificial intelligence (Al) and machine learning (ML) modules 850 embedded within the computing system 800 enable advanced predictive analytics, transforming historical and real-time data into actionable insights. High-performance Tensor Processing Units (TPUs) and Graphical Processing Unites (GPUs), and Central Processing Units (CPUs) support continuous training of Al I ML models that enhance slotting optimization, pick-path efficiency, and demand forecasting. Al-driven algorithms analyze real-time operational data to anticipate potential stockouts, bottlenecks, or equipment failures, allowing the system to adjust task allocations proactively. These Al I ML models 850 are housed on cloud servers 852, enabling parallel processing and providing adaptive, scalable insights across warehouse facilities.

[0191] Leveraging TPUs, GPUs, and CPUs, the warehouse management system 102 continuously trains the ML models 850 that improve slotting optimization, pick-path efficiency, and demand forecasting, while factoring in complex variables like seasonality, lead times, and product perishability. Al-driven algorithms analyze real-time operational data to anticipate stockouts, bottlenecks, or equipment failures, to enable the system to proactively adjust task allocations and order priorities. These Al models are housed on cloud servers, where large datasets can be processed in parallel, providing scalable, adaptive insights to each warehouse facility.

[0192] To monitor and interact with the warehouse management system 102, warehouse facilities employ a suite of data collection and input / output (I / O) devices. Input devices 845, including barcode scanners, RFID readers, loT sensors, touchscreens, and handheld devices, capture real-time data on inventory status, item locations, and environmental conditions. This data feeds into the processor 810 for seamless integration into the operational database, enabling precise, on-the-fly adjustments. Output devices, such as digital displays, wearable alerts, and screens, provide real-time feedback to personnel on inventory status and task priorities, ensuring that staff remain informed of critical updates and operational changes. The range of input and output devices, including the input device 845 and the output device 835, which enable warehouse personnel to interact directly with the warehouse management system 102. These devices 835, 845 provide real-time feedback and facilitate task monitoring, allowing operators to stay informed of task progress, inventory levels, and any system updates. Input devices 845, such as touchscreens andAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1handheld scanners, capture real-time data from personnel and transmit it to the processor 810, while output devices 835 display prioritized task sequences, updates on order status, and notifications for operational adjustments.

[0193] To manage the complexities of warehouse orchestration by the orchestration module 119, the computing system 800 integrates a communications interface 840, which handles the flow of data between the processor 810 and other components, as well as external networks. The communications interface 840 supports and facilitates high-speed, bidirectional communication between the processor 810 and other system components such as other warehouse facilities 130, 132 and cloud-based resources, supporting data flows critical to real-time decision-making. This interface integrates with external systems 126 such as ERP, WMS, and SCM platforms, providing the system with comprehensive, up-to-date information for operational adjustments. For example, operational data from loT sensors and RFID tags across the warehouse facilities 118, 130, 132 can be transmitted to the processor 810 in real time, updating the warehouse management system 102 on stock levels, item locations, and environmental conditions, to enable precise and timely adjustments to task sequences.

[0194] In various embodiments, the computing environment 800 is extended and supported by a scalable cloud-based infrastructure 854, which provides scalable storage, processing power, and data analytics. Cloud integration enables the warehouse management system 102 to store large volumes of historical data and compute-intensive ML models 850 remotely, ensuring each facility has access to centralized intelligence and enabling real-time processing for multiple locations simultaneously. This cloud framework, managed through virtual machines and containerized applications, allows for continuous software updates, enhanced disaster recovery, and rapid resource allocation based on system demand.

[0195] The scalable cloud-based infrastructure 854 also serves as the primary hub for inter-facility communication, linking regional warehouses to a central command system that monitors and directs tasks across the network. When additional processing capacity is required, the system can dynamically allocate cloud resources, ensuring that computational workloads related to predictive analytics, complex task sequencing, and resource optimization are handled efficiently. Additionally, data redundancy protocols within the cloud architecture safeguard operational data, ensuring recovery from hardware failure or data loss scenarios.

[0196] The processor-based computing system 800 operates within a cloud-enabled,Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1modular hardware environment designed for scalability and high availability. This broader infrastructure includes additional Al-optimized processing units, such as TPUs and GPUs, which are capable of handling the intensive computational requirements of machine learning algorithms used for demand forecasting, slotting optimization, and real-time task adjustments. The modular cloud architecture ensures that each facility can access centralized data and processing power, dynamically adjusting workflows based on system demands. It also allows the orchestration module to allocate resources optimally, whether from central servers or distributed warehouse-specific processors, depending on task priorities and operational constraints.

[0197] The scalable cloud-based infrastructure 854 provides flexible storage, processing, and analytics capabilities across distributed facilities. Cloud integration supports storage of historical data, remote processing of compute-intensive machine learning models, and real-time data access for multiple warehouses. Managed through containerized applications and virtual machines, the cloud framework enables continuous software updates, enhanced disaster recovery, and dynamic resource allocation to accommodate varying operational demands. Cloud-based inter-facility communication ensures synchronized, efficient operations, with additional processing capacity allocated as needed.

[0198] Energy efficiency is also prioritized within the warehouse management system's 102 design, with power management protocols integrated across hardware components to reduce the system’s environmental impact. The distributed power architecture, supported by uninterruptible power supplies (UPS) and backup generators, ensures reliable operation and minimizes downtime, allowing the system to maintain continuity and efficiency during power disruptions.

[0199] The warehouse management system’s 102 security protocols ensure data integrity, privacy, and compliance with regulatory standards. These include end-to-end encryption, multi-factor authentication, role-based access control, and real-time monitoring via firewalls and intrusion detection systems. An Al-based anomaly detection component monitors access patterns and data consistency, flagging unusual activities. Additionally, encrypted logging and audit trails facilitate transparency and compliance with regulations such as GDPR for data privacy, while data sovereignty protocols ensure that sensitive information meets regional compliance standards.

[0200] This computing environment 800 represents an end-to-end, adaptive warehouse management platform that integrates Al-driven analytics, real-time data capture, robust security measures, and scalable cloud resources. The processor 810, memory modules,Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1storage devices, and I / O components collectively enable data-driven decision-making, adjusting tasks in real time to respond to changing demands. Data flows seamlessly from cloud storage and I / O devices to the central processor, dynamically orchestrating resources to provide real-time updates to warehouse staff, maintain efficiency, and optimize performance across facilities. This robust, secure, and compliant system is designed to streamline operations, improve responsiveness, and support scalable growth in diverse logistics environments.

[0201] Altogether, the computing system 800 represents an end-to-end, adaptive warehouse management environment that combines Al-driven analytics, real-time data collection, secure data management, and scalable cloud resources to deliver exceptional operational efficiency. The processor 810, memory modules, storage devices, and I / O components work in concert to enable continuous, data-driven decision-making, adjusting warehouse tasks to meet evolving demands with precision. As data flows from the cloud 854 and various I / O devices to the central processor 810, the computing system 800 dynamically orchestrates resources, providing real-time updates to staff and ensuring optimal performance across warehouse facilities. This high-performance, secure, and compliant environment thus allows for streamlined operations, enhanced responsiveness to market demands, and scalable growth across diverse logistical challenges.

[0202] In summary, this integrated computing environment 800 offers a comprehensive solution for real-time warehouse management, combining high-performance processing, advanced memory configurations, scalable cloud-based resources, and robust communication interfaces. It enables efficient and adaptive control over the entire fulfillment process, from inventory monitoring and task allocation to predictive analytics and order prioritization, ultimately facilitating efficient and responsive warehouse operations that meet complex and changing logistical demands. Together, these elements establish a high-performance environment that supports continuous adaptation, precise task orchestration, and predictive optimization, fully realizing the functionalities described in the appended claims.

[0203] The presently disclosed warehouse management system 102 harnesses advanced artificial intelligence (Al) technologies, integrating sophisticated machine learning models with cutting-edge computational infrastructure to solve complex problems, enhance decision-making, and automate tasks across multiple warehouse facilities. Designed with flexibility, scalability, and high-performance in mind, the system offers organizations a comprehensive Al solution tailored to their needs.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1

[0204] At its core, the warehouse management system 102 utilizes a scalable, cloudbased architecture that ensures high availability, security, and performance even under the most demanding conditions. A distributed data processing pipeline allows the system to efficiently ingest, cleanse, and transform large volumes of structured and unstructured data in real-time, facilitating quick, intelligent insights.

[0205] The system warehouse management system 102 leverages state-of-the-art deep learning models such as convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequential data, and transformer models for natural language processing (NLP). These models are dynamically selected and optimized based on input data, ensuring the best possible outcomes for tasks like predictive analytics, image recognition, and language understanding.

[0206] Through reinforcement learning, the Al system continually learns and adapts in real-time, optimizing its performance over time. This feature enables autonomous decisionmaking in dynamic environments like robotics, autonomous vehicles, and operational workflows, where the system adjusts based on changing data or conditions.

[0207] In addition to cloud infrastructure, the warehouse management system 102 includes edge computing capabilities, enabling real-time Al processing on local devices or servers. This reduces latency and allows critical applications — such as loT networks, autonomous systems, and real-time monitoring — to function seamlessly without relying on constant cloud communication.

[0208] Optimized for high-performance computing environments, the warehouse management system 102 utilizes GPUs and TPUs to accelerate machine learning tasks, enabling the rapid processing of large datasets. This feature is especially beneficial for applications requiring intense computational power, such as video analytics, financial modeling, and real-time data processing.

[0209] Built on a microservices framework, the Al system offers scalability and flexibility. Each Al function is deployed as an independent service, allowing organizations to scale and adjust components as necessary without affecting the entire system. This modularity ensures seamless updates and improvements over time.

[0210] The Al system integrates with real-time data streaming platforms like Apache Kafka and Apache Flink to process and analyze live data as it enters the system. This feature empowers businesses to make data-driven decisions in real-time, adapting quickly to new information and evolving market conditions.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1

[0211] Equipped with advanced NLP capabilities, the warehouse management system 102 comprehends, interprets, and generates human language. This enables applications like Al-driven customer service chatbots, sentiment analysis, and content generation, automating tasks that would otherwise require human input.

[0212] Built with security, the warehouse management system 102 features end-to-end encryption and adheres to the latest privacy regulations. It includes continuous monitoring for anomalous behavior, ensuring that sensitive data is securely processed and stored in compliance with industry standards.

[0213] The warehouse management system 102 can be deployed across a range of environments, including public or private cloud infrastructures (e.g., AWS, Azure, Google Cloud), hybrid cloud setups, or fully on-premise configurations. This flexibility allows organizations to select the best deployment strategy based on their operational, security, and scalability needs.

[0214] This Al based warehouse management system 102 represents a leap forward in intelligent technologies, combining adaptive learning, real-time data analytics, and advanced computing power to offer organizations unparalleled efficiency, accuracy, and insight. By seamlessly integrating Al into existing operations and adapting to diverse environments, this system empowers businesses to thrive in a rapidly evolving, data-driven world.

[0215] Within the context of this disclosure, the term "module" is used as a broad and flexible term to describe a component of the warehouse management system 102 that can be implemented using hardware, software, firmware, or a combination of these to perform one or more specific tasks or operations. A module may be implemented using various types of technology, including but not limited to:

[0216] 1. Hardware: A physical device or circuit that executes predefined functions. Hardware modules may include, but are not limited to, processors (e.g., central processing units (CPUs), digital signal processors (DSPs), graphical processing units (GPUs)), memory components (RAM, ROM, flash memory), network interfaces, power management systems, or specialized chips such as application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs). These components may be connected through buses, networks, or communication interfaces, providing the necessary infrastructure for high-speed data transfer and execution of operations.

[0217] 2. Software: A set of instructions stored in memory and executed by processing units. Software modules may include executable code, dynamic link libraries (DLLs),Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1software development kits (SDKs), virtual machine environments, or applications. These modules are responsible for implementing algorithms, data processing, decision-making logic, and user interface functionalities. Software modules can be written in various programming languages (e.g., C++, Python, Java) and can interact with other modules through well-defined APIs, middleware, or cloud-based services.

[0218] 3. Firmware: Low-level code stored in non-volatile memory (e.g., EEPROM, flash memory) that bridges the gap between hardware and higher-level software. Firmware modules provide the control logic necessary to configure, initialize, and manage hardware devices. Firmware can handle essential tasks such as device bootstrapping, protocol handling, and power management. Firmware updates may be delivered remotely, enabling post-deployment enhancements and security patches without requiring hardware modifications.

[0219] In addition to these core implementations, modules may include the following technical features:

[0220] Modules are capable of interacting with other modules via inter-module communication within the system through standard communication protocols such as InterProcess Communication (IPC), message passing, remote procedure calls (RPC), or data buses. This allows for distributed operations across different hardware or software environments, whether local or over a network. The use of APIs, middleware layers, or network protocols (e.g., REST, gRPC) facilitates seamless communication between modules regardless of their underlying implementation.

[0221] Modules are designed with modularity and scalability features via a plug-and-play architecture, enabling the system to dynamically add, remove, or modify modules as needed. This modularity allows the system to scale efficiently, either horizontally (by adding more modules for parallel processing) or vertically (by enhancing the capabilities of individual modules). This feature is particularly useful in distributed computing environments, such as cloud platforms or multi-core processors.

[0222] Modules can be designed to support multi-threading, parallel execution, or distributed computing architectures, where tasks are split across multiple hardware resources (e.g., multi-core processors, distributed nodes). Load balancing and task synchronization mechanisms ensure efficient resource utilization, minimizing execution time for complex operations.

[0223] Modules can integrate Al-driven components such as machine learning modelsAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1or neural networks to perform tasks like pattern recognition, decision-making, and predictive analytics. These Al modules can be pre-trained models or dynamically updated through continuous learning, depending on the application's requirements. Modules can leverage specialized Al hardware accelerators such as TPUs (Tensor Processing Units) or GPUs for high-performance processing.

[0224] For time-sensitive applications, modules may feature real-time processing capabilities, including low-latency processing, task prioritization, and event-driven architectures. Real-time operating systems (RTOS) or real-time task schedulers can be used within firmware or software modules to ensure that critical tasks are completed within specific time constraints.

[0225] Modules may incorporate security mechanisms such as encryption, authentication, and access control to protect data and ensure the integrity of operations. Secure hardware modules (e.g., Trusted Platform Modules (TPMs) or secure enclaves) may be used to store cryptographic keys and execute secure operations, while software- based modules may implement firewalls, intrusion detection systems (IDS), or secure communication protocols (e.g., TLS / SSL).

[0226] Modules may manage and store data using embedded databases, cloud storage services, or other data management and persistence mechanisms. Data synchronization across distributed systems may be supported through version control, replication strategies, and consistency models (e.g., eventual consistency, strong consistency).

[0227] Modules are adaptable for deployment in cloud environments (e.g., AWS, Google Cloud, Microsoft Azure) or edge computing frameworks. Cloud-based modules can dynamically scale according to demand, leveraging elastic resources, while edge modules perform low-latency processing closer to the data source, reducing dependency on centralized cloud systems.

[0228] In environments where power consumption is critical (e.g., loT devices or battery-operated systems), modules may include energy-efficient designs, such as power-aware algorithms, dynamic voltage scaling, sleep modes, or energy harvesting technologies.Hardware modules may implement low-power designs using specific semiconductor technologies optimized for minimal energy usage.

[0229] Each module is designed to function as an independent, reusable component within a larger system architecture, while maintaining compatibility with other modules. This modular approach allows for flexibility in system design, enabling easy upgrades,Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1extensions, and maintenance. Whether deployed on dedicated hardware, within virtualized environments, or across distributed networks, modules provide the foundational building blocks for the warehouse management system’s 102 comprehensive functionality.

[0230] In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like.However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

[0231] Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and / or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

[0232] Devices implementing methods according to these disclosures can comprise hardware, firmware and / or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

[0233] The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

[0234] Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations.Further and although some subject matter may have been described in language specific toAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1examples of structural features and / or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims.

Claims

Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1CLAIMSWhat is claimed is:

1. A computer-implemented method for controlling operations within one or more warehouse facilities, the method comprising:receiving, by an input data module, an inbound data packet containing data parameters related to a customer order of physical goods stored in one or more warehouse facilities;processing, by a data processing module, the inbound data packet to manage order fulfillment, wherein the data processing module is configured for:extracting data parameters regarding the physical goods specified in the customer order;analyzing the extracted data parameters for determining a sequenced set of physical tasks required for fulfilling the customer order, with each task allocating specific warehouse resources for operations; anddirecting the sequenced set of tasks to specific warehouse facilities configured to execute and monitor task progress through operational data feedback;orchestrating, by an orchestration module configured to communicate with the data processing module, the execution of tasks in response to operational data from the warehouse facilities, for fulfilling the customer order by dynamically reallocating resources among tasks based on operational changes in the operational data.2 The method of claim 1, wherein the data processing module is configured to communicate data with an inventory module, wherein the inventory module is configured for:monitoring inventory levels of the physical goods at one or more warehouse facilities; optimizing storage locations of the physical goods and enhancing pick-path efficiency; andtracing serialized components through stages of assembly, storage, and shipment.3 The method of claim 2, comprising adjusting, by the inventory module, the sequenced set of tasks based on real-time inventory levels at the one or more warehouse facilities, adapting tasks to match current stock availability.4 The method of claim 1, comprising generating, by the data processing module, the sequenced set of tasks based on product-specific characteristics including at least one ofAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1assembly, labeling, or packaging requirements tailored to each product in the customer order.

5. The method of claim 1, comprising applying, by the data processing module, predictive analytics based on historical operational data for proactively generating or adjusting the sequenced set of tasks to improve efficiency and prevent stock-outs.6 The method of claim 1, comprising adjusting or modifying, by the orchestration module, the sequenced set of tasks based on the operational data received from each allocated warehouse facility.7 The method of claim 1, comprising prioritizing, by the orchestration module, tasks in the sequenced set of tasks based on customer order-related criteria, including delivery deadlines, product perishability, or order value, to optimize fulfillment efficiency.8 A system for managing operations across one or more warehouse facilities, the system comprising:an input data module configured to receive inbound data packets containing parameters associated with a customer order for physical goods stored at one or more warehouse facilities;a data processing module comprising a processor and memory configured to execute instructions, wherein the data processing module is configured to:extract data parameters from the inbound data packets related to the physical goods specified in the customer order;analyze the extracted data to generate a sequenced set of tasks needed to fulfill the customer order, wherein the tasks are allocated to one or more warehouse facilities; and transmit the sequenced set of tasks to the warehouse facilities;a plurality of communication interfaces located at each warehouse facility, the communication interfaces configured to:receive the sequenced set of tasks from the data processing unit; andsend operational data, including status updates, back to the data processing unit; an orchestration module configured to receive the operational data and adjust the sequenced set of tasks in response to conditions at the warehouse facilities;a storage system configured for dynamic storage and retrieval of goods, in communication with the orchestration module and the data processing unit, enabling optimization of goods location and retrieval paths within each warehouse facility based on the sequenced set of tasks; andAttorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1a tracking system comprising sensor arrays and inventory tagging mechanisms, such as RFID or barcode systems, configured to track serialized components as they move through stages of assembly, storage, and shipment, and to communicate tracked data to the data processing unit.

9. The system of claim 8, comprising a cloud-based control module configured to support centralized operations across multiple warehouse facilities, enabling remote management, real-time data synchronization, and operational oversight from a single access point.

10. The system of claim 8, wherein the data processing module includes a predictive analytics engine configured to analyze historical data and forecast demand, enabling the orchestration module to proactively adjust the sequenced set of tasks to prevent stockouts and optimize resource allocation.

11. The system of claim 8, wherein the input data module is configured to receive data from external supply chain systems, including Supplier Management Systems and Logistics Systems, providing the data processing module with supply availability and lead time information to adjust task sequencing.

12. The system of claim 8, wherein the storage system is configured with modular storage units, which can be rearranged dynamically based on real-time inventory demands and are repositioned by an automated system to reduce retrieval time for frequently ordered goods.

13. The system of claim 8, comprising a machine learning module embedded in the data processing module, wherein the machine learning module continuously refines task sequencing based on real-time performance data, historical data, and evolving inventory patterns.

14. The system of claim 8, comprising an integration layer, configured to interface with third- party enterprise resource planning (ERP) systems, enabling the data processing module to access supplier data, order fulfillment metrics, and transportation schedules, to optimize the generated sequenced set of tasks.

15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a warehouse management system, cause the warehouse management system to manage operations across one or more warehouse facilities, the instructions comprising:Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 1receiving, by an input data module, inbound data packets containing parameters associated with a customer order of physical goods stored at one or more warehouse facilities;extracting data parameters from the inbound data packets related to the physical goods specified in the customer order;analyzing the extracted data to determine a sequenced set of tasks required to fulfill the customer order, each task allocated to one or more of the warehouse facilities;transmitting the sequenced set of tasks to allocated warehouse facilities for execution; andreceiving operational data from each warehouse facility regarding task status, inventory levels, and order fulfillment progress; andadjusting, by an orchestration module, the sequenced set of tasks based on the operational data received from the warehouse facilities to improve order fulfillment accuracy and efficiency.

16. The non-transitory computer-readable medium of claim 15, wherein the instructions comprise communicating data, by the data processing module, with an inventory module configured for:monitoring inventory levels of the physical goods across the warehouse facilities; optimizing storage location of physical goods for efficient picking paths; andtracing serialized components through stages of assembly, storage, and shipment.

17. The non-transitory computer-readable medium of claim 16, wherein the instructions comprise adjusting the sequenced set of tasks based on real-time inventory levels at the warehouse facilities, adapting tasks to current stock availability.

18. The non-transitory computer-readable medium of claim 15, wherein the instructions comprise generating the sequenced set of tasks based on product-specific characteristics, including at least one of assembly, labeling, or packaging requirements tailored to each item in the customer order.

19. The non-transitory computer-readable medium of claim 15, wherein the instructions comprise applying predictive analytics, based on historical operational data, to proactively generate or adjust the sequenced set of tasks to improve efficiency and prevent stock shortages.Attorney Docket No. 241770PCT - P / 101108 / WO / SEC / 120. The non-transitory computer-readable medium of claim 15, wherein the instructions comprise adjusting the sequenced set of tasks based on operational data received from allocated warehouse facilities to modify tasks dynamically and prioritizing tasks in the sequenced set of tasks based on criteria such as delivery deadlines, product perishability, or order value to optimize order fulfillment efficiency.

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