A digital twin system for cell-based autonomous production consolidation in ERP-controlled discrete manufacturing

Abstract

A digital twin-based system for cell-based autonomous production consolidation in an enterprise resource planning (ERP) controlled discrete manufacturing environment, comprising: a module for generating a digital twin (101) configured to create and maintain a virtual representation of one or more physical production cells including the associated workstations, workflow structures, material dependencies and capacity constraints; a demand classification and cell grouping module (102) configured to receive production demand from an ERP system and classify the demand into cell-compatible demand clusters based on routing compatibility, product family attributes, and production cell capacities; a production consolidation engine (103) configured to generate consolidated planned orders at the cell level by aggregating the classified demand clusters and embedding cell-specific production intelligence into ERP planning objects; a real-time synchronization module (104) configured to collect operational data from manufacturing systems, including machine status, throughput rate, queue metrics and capacity utilization, and to update the digital twin models accordingly; a deviation detection module (105) configured to compare the real-time operating data with the parameters of the digital twin models to identify deviations that exceed predefined operating thresholds; and an autonomous recalibration engine (106) is configured such that, when a deviation is detected, the results of the material requirements planning at the level of the manufacturing cell are selectively regenerated and the production utilization is redistributed to one or more manufacturing plants (107).

Description

Technical field of expertise:

[0001] The present invention relates generally to the field of digital manufacturing systems and technologies for production control in enterprises. In particular, the invention relates to a computer-aided system and method for digital twin-driven autonomous production consolidation in ERP-controlled discrete manufacturing environments. The invention integrates digital twin technology, cell-based production architecture, and intelligent material requirements planning (MRP) stabilization mechanisms to improve production planning efficiency, reduce order fragmentation, and increase synchronization between enterprise planning systems and physical shop floor operations. Background of the invention;

[0002] Discrete manufacturing industries increasingly rely on enterprise resource planning (ERP) systems to manage production planning, inventory management, procurement, and order fulfillment across complex manufacturing networks. Modern ERP platforms typically include material requirements planning (MRP) engines that translate customer requirements into production orders, purchase requisitions, and production planning instructions. While such systems offer structured planning capabilities, they often suffer from several technical limitations when deployed in highly dynamic production environments.

[0003] One of the biggest challenges in ERP-driven discrete manufacturing is order fragmentation. Conventional MRP systems generate planned orders based on demand signals, bill of materials structures, and route data. However, because these calculations are often performed at the level of individual workstations or materials, the resulting production plans can produce a large number of small, fragmented orders spread across multiple workstations or plants. This fragmentation increases planning complexity, reduces production stability, and often leads to inefficient material flows throughout the production network.

[0004] Another limitation stems from the static nature of traditional routing and capacity planning models. In many ERP systems, route structures and capacity definitions are configured as relatively static parameters that do not dynamically reflect real-time shop floor conditions. As a result, MRP calculations are often based on outdated assumptions regarding machine availability, throughput rates, queueing conditions, and operational constraints. When deviations occur in real-world production environments, such as machine downtime, unexpected demand spikes, or resource shortages, the ERP system typically requires a complete system-wide MRP regeneration to recalculate production plans. This process is computationally intensive, time-consuming, and disrupts ongoing manufacturing processes.

[0005] Furthermore, conventional ERP architectures often lack mechanisms for cell-based production coordination. Modern manufacturing facilities increasingly organize production resources into production cells, where machine groups, operators, and processes are structured around product families or manufacturing sequences. While cell-based manufacturing improves shop floor efficiency, traditional ERP planning models are generally geared toward individual work centers rather than coordinated production cells. Consequently, ERP-generated plans may fail to leverage the inherent efficiency gains of cell-based manufacturing structures.

[0006] Another major technological gap lies in the limited integration between virtual production models and physical shop floor operations. Emerging Industry 4.0 concepts have introduced the idea of ​​digital twins, which are dynamic virtual representations of physical assets or systems. Although digital twin technologies have been widely explored in applications for equipment monitoring and predictive maintenance, their integration into enterprise-level production planning and MRP optimization remains relatively limited.

[0007] Without a dynamic representation of production cells and their real-time operating states, ERP systems struggle to adapt production planning decisions to constantly changing manufacturing conditions. This often leads to unstable production plans, excessive material movement between plants, and inefficient use of production capacity.

[0008] Accordingly, there is a need for an improved ERP-integrated production control system capable of dynamically modeling production cells, consolidating demand into stable cell-level production structures, and autonomously adapting production plans to operational conditions in real time. The present invention addresses these technical challenges by introducing a digital twin-driven system for cell-based autonomous production consolidation. This system integrates virtual production models, adaptive demand grouping logic, and selective MRP recalibration mechanisms to improve production stability and computational efficiency in discrete manufacturing environments. Summary of the invention:

[0009] The present invention provides a digital twin-driven system for cell-based autonomous production consolidation in ERP-driven discrete manufacturing environments, designed to stabilize production planning, reduce order fragmentation, and improve synchronization between enterprise planning systems and real-time shop floor operations. The system introduces an intelligent production architecture in which digital twins of production cells are continuously maintained and used to control ERP-based material requirements planning and production order creation processes.

[0010] According to the invention, the system comprises a digital twin generation module configured to create and maintain a virtual representation of physical production cells. Each digital twin model contains detailed operational parameters describing the structure and behavior of a production cell, including associated work centers, route sequences, bill of materials dependencies, machine capacities, throughput rates, and operational constraints. These digital twin models are dynamically synchronized with actual shop floor conditions through continuous data acquisition from manufacturing execution systems, sensors, and operational monitoring platforms.

[0011] The system also includes a demand classification and cell grouping module configured to analyze incoming sales orders, forecast demand signals, and analyze production requirements received through the ERP system. This module applies adaptive cell grouping logic that classifies demand by product family, route compatibility, and processing capabilities at the cell level. By grouping demand by compatibility with production cells rather than individual work centers, the system generates consolidated production structures at the cell level, thereby reducing the fragmentation typically associated with traditional MRP outputs.

[0012] A production consolidation engine then generates consolidated planned orders aligned with specific production cells. This engine embeds cell-level intelligence into ERP-generated planning documents, including planned orders, production orders, and intercompany transfer requests. This cell-oriented planning structure enables the coordinated execution of production planning decisions across multiple work centers within the same production cell, thereby improving production flow and minimizing unnecessary material movements.

[0013] To maintain operational accuracy, the invention includes a real-time synchronization module that continuously collects operational data from the shop floor. This data can include information on machine status, equipment utilization, throughput metrics, queue states, and production cycle times. The collected operational data is compared with the parameters stored in the digital twin models to identify deviations between planned and actual production conditions.

[0014] A deviation detection module analyzes the synchronized data and identifies operational deviations that exceed predefined thresholds. Such deviations can include machine failures, overload conditions, bottlenecks, or unexpected fluctuations in throughput rates. When such conditions are identified, the system activates an autonomous recalibration engine that selectively regenerates the affected parts of the ERP planning structure.

[0015] Unlike traditional ERP systems that require a complete MRP regeneration, the recalibration engine operates selectively at the production cell level, allowing the system to regenerate only the planning segments affected by the detected deviation. The recalibration engine can dynamically regroup demand to alternative production cells, redistribute production loads between plants, and adjust planned orders at the cell level, while preserving unaffected portions of the existing ERP plan.

[0016] Through this architecture, the invention significantly improves the stability of the production plan, computing efficiency, and responsiveness to real-time manufacturing conditions. By integrating digital twin technology with ERP-based production planning and cell-based manufacturing structures, the system enables a new form of autonomous production consolidation that can support highly dynamic Industry 4.0 manufacturing environments. The invention therefore offers a measurable technical improvement in ERP-integrated discrete production control systems, particularly in multi-plant production networks where synchronized material flow and adaptive production planning are crucial for operational efficiency. Brief description of the drawing Fig. Figure 1 shows a block diagram of the system according to the invention. Detailed description of the invention

[0017] The present invention relates to a digital twin system designed to improve production planning and consolidation processes in ERP-driven discrete manufacturing environments. Modern manufacturing companies operate within highly complex production networks consisting of multiple work centers, product families, route structures, material flows, and interdependent production constraints. Conventional ERP-based material requirements planning (MRP) systems perform production planning by processing sales demand, bill of materials data, route information, and inventory levels to generate planned orders and production schedules. However, traditional MRP systems typically operate at the level of individual materials and work centers and are unable to effectively consolidate production activities into stable production structures.As a result, such systems often generate fragmented production plans, leading to inefficient resource utilization, excessive order generation, and unstable manufacturing processes.

[0018] The present invention introduces a novel architecture that integrates digital twin technology with ERP production planning to enable autonomous consolidation of production activities at the manufacturing cell level. A manufacturing cell represents a group of coordinated work centers, machines, or manufacturing processes that jointly perform a defined sequence of production operations for a specific product family or manufacturing stage. By structuring production planning around manufacturing cells instead of isolated work centers, the invention enables a more stable and operationally efficient consolidation of production demand.

[0019] According to the invention, the system comprises a digital twin generation module configured to create and manage digital representations of physical production cells within the manufacturing environment. Each digital twin model contains detailed structural and operational information describing the associated production cell. This information includes workstation identifiers, route sequences, machine capabilities, process durations, material dependencies from the bill of materials, production capacity constraints, and permissible operating thresholds. The digital twin generation module extracts relevant configuration data from the ERP system and combines it with operational data obtained from manufacturing execution systems or industrial monitoring platforms to create an accurate virtual model of each production cell.

[0020] The digital twin models maintained by the system provide a continuously updated representation of the operational status and capacity characteristics of the physical production environment. These models are not merely static representations but are dynamically updated with real-time operational data collected from the shop floor. The digital twin creation module continuously synchronizes its internal parameters with physical production systems, ensuring that the digital models accurately reflect machine availability, throughput, queue states, and capacity utilization across the entire manufacturing facility.

[0021] The system also includes a demand classification and cell grouping module configured to process incoming production demand received via the ERP system. Demand inputs can originate from customer orders, forecast signals, internal production requirements, or intercompany transfer requests. Instead of processing these demand signals independently for each material or work cell, the demand classification module evaluates the compatibility of each demand request with available production cells.

[0022] The classification process considers various parameters, including product family classification, route compatibility, required manufacturing operations, resource availability, and production constraints associated with each cell. Using these parameters, the module groups incoming demand requests into cell-based demand clusters. Each cluster represents a set of demand orders that can be efficiently processed within the same production cell based on that cell's operational capabilities.

[0023] Once the demand clusters are formed, a production consolidation engine generates consolidated planned orders corresponding to the identified production cells. The consolidation engine analyzes the grouped demand clusters and determines an optimized production plan that distributes demand quantities across specific production cells, taking into account capacity constraints and material availability conditions. Instead of generating numerous small and fragmented planned orders, the system creates consolidated orders at the cell level that represent the aggregated production demand for the entire cell.

[0024] The consolidation engine embeds cell-specific intelligence directly into the ERP planning objects generated by the system. This information can include identifiers for the target production cell, cell-level route sequences, capacity allocation parameters, and operating rules. The cell-oriented planning structure ensures that the generated planned orders and production orders align with the operational structure of the manufacturing plant.

[0025] The system also supports coordination between multiple production sites within a distributed production network. If several plants have production cells capable of processing the same product family, the consolidation engine can distribute production demand across these plants based on available capacity, production cost considerations, or delivery time constraints. In such scenarios, the system generates corresponding intra-group transfer documents or cross-plant production orders to ensure that material flows remain synchronized throughout the network.

[0026] A real-time synchronization module plays a crucial role in maintaining the operational accuracy of digital twin models and the production planning system. The synchronization module continuously collects operational data from shop floor monitoring systems, industrial sensors, manufacturing execution systems, and machine control platforms. The collected data includes information on machine status, operating speeds, throughput rates, queue lengths, cycle times, and capacity utilization metrics for each work center or production cell.

[0027] The synchronization module processes the collected data and updates the corresponding parameters in the digital twin models managed by the system. This continuous synchronization ensures that the digital twin representation accurately reflects the current operating conditions of the physical manufacturing environment. As a result, the production planning decisions generated by the system remain aligned with real-time manufacturing capacities.

[0028] To ensure robust production control, the invention further includes a deviation detection module configured to analyze differences between the parameters of the digital twin and the operational data in real time. The deviation detection module evaluates deviations across multiple dimensions, including machine availability, production throughput, queue buildup, capacity utilization, and operating cycle times. When the system detects deviations that exceed predefined thresholds, the module generates a deviation alarm indicating that the current production plan may no longer be optimal or feasible.

[0029] Examples of such deviations include machine breakdowns, unexpected maintenance events, operator shortages, interruptions in material supply, or sudden increases in customer demand. These events can cause bottlenecks within specific production cells or lead to unbalanced capacity utilization across the entire manufacturing network. In conventional ERP systems, resolving such disruptions often requires a complete MRP regeneration process across the entire enterprise planning structure.

[0030] The present invention avoids such computationally intensive operations by introducing an autonomous recalibration engine configured to perform selective recalculation of production plans at the level of individual production cells. When the variance detection module identifies a disturbance affecting a specific cell or group of cells, the recalibration engine isolates the affected planning segments and regenerates the corresponding MRP outputs without affecting the rest of the production plan.

[0031] During the recalibration process, the engine can assign demand clusters to alternative production cells, redistribute production loads between different production sites, adjust production start dates, or change capacity allocations to accommodate the detected operational changes. Because the recalibration process is selective and not global, the system significantly reduces computational effort while maintaining stable production operations.

[0032] The recalibration engine continues to interact with the ERP system to update the affected planning documents, including planned orders, production orders, purchase requisitions, and intercompany transfer documents. These updates ensure that the ERP system remains synchronized with the optimized production structure generated by the digital twin-driven planning mechanism.

[0033] By integrating digital twin modeling, adaptive demand grouping, production consolidation logic, real-time synchronization, variance detection, and autonomous recalibration functions, the present invention provides a comprehensive production planning architecture capable of stabilizing ERP-driven discrete manufacturing processes. The invention reduces the number of fragmented production orders generated by conventional MRP systems, improves resource utilization across production cells, increases responsiveness to operational disruptions, and reduces the computational load associated with large-scale planning recalculations.

[0034] The proposed system is particularly suitable for Industry 4.0 manufacturing environments, where digital connectivity between enterprise systems and shop floor equipment enables continuous monitoring and adaptive control of production processes. By embedding cell-level intelligence in ERP planning mechanisms and utilizing digital twins to maintain real-time production awareness, the invention represents a novel technical solution for achieving autonomous production consolidation in modern discrete manufacturing companies. Description of the numbers in the attached drawing 100 System 101 Module for creating a digital twin 102 Module for demand classification and cell grouping 103 Production Consolidation Engine 104 Real-time synchronization module 105 Deviation Detection Module 106 autonomous recalibration engine 107 or more manufacturing plants

Claims

[1] A digital twin-based system for cell-based autonomous production consolidation in an enterprise resource planning (ERP) controlled discrete manufacturing environment, comprising: a module for generating a digital twin (101) configured to create and maintain a virtual representation of one or more physical production cells including the associated workstations, workflow structures, material dependencies and capacity constraints; a demand classification and cell grouping module (102) configured to receive production demand from an ERP system and classify the demand into cell-compatible demand clusters based on routing compatibility, product family attributes, and production cell capacities; a production consolidation engine (103) configured to generate consolidated planned orders at the cell level by aggregating the classified demand clusters and embedding cell-specific production intelligence into ERP planning objects; a real-time synchronization module (104) configured to collect operational data from manufacturing systems, including machine status, throughput rate, queue metrics and capacity utilization, and to update the digital twin models accordingly; a deviation detection module (105) configured to compare the real-time operating data with the parameters of the digital twin models to identify deviations that exceed predefined operating thresholds; and an autonomous recalibration engine (106) is configured such that, when a deviation is detected, the results of the material requirements planning at the level of the manufacturing cell are selectively regenerated and the production utilization is redistributed to one or more manufacturing plants (107). [2] System according to claim 1, wherein the digital twin generation module creates digital models of manufacturing cells by integrating ERP configuration data including workflow structures, bill of materials relationships and workplace capacity definitions with real-time operating parameters obtained from manufacturing control systems. [3] System according to claim 1, wherein the production consolidation engine is further configured to embed production cell identifiers, cell-level routing parameters and capacity allocation attributes in planned orders, production orders and cross-company transfer documents generated within the ERP system. [4] System according to claim 1, wherein the real-time synchronization module continuously acquires operating data from industrial sensors, machine controls or manufacturing execution platforms to dynamically update the operating parameters of the digital twin models representing the production cells. [5] System according to claim 1, wherein the autonomous recalibration engine selectively regenerates affected segments of the material requirements planning results by redistributing demand clusters to alternative production cells or manufacturing plants, while maintaining unaffected ERP planning structures.

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