System and method for coordinated manufacturing and logistics optimization

Abstract

ABSTRACT SYSTEM AND METHOD FOR COORDINATED MANUFACTURING AND LOGISTICS OPTIMIZATION A system and method for optimizing coordinated manufacturing and logistics is disclosed. The method comprises acquiring real-time inventory data from monitoring devices at multiple customer sites and processing the data to determine material demand. Customer sites are grouped based on proximity, and aggregated material demand is calculated for each group. A delivery threshold is determined for a group, and upon the aggregated material demand reaching the threshold, a consolidated dispatch order is generated, and production is coordinated based on the consolidated dispatch order. The system comprises a processor and memory storing instructions that cause the system to acquire real-time inventory data, process the data to determine material demand, group customer sites by proximity, calculate aggregated demand, determine delivery thresholds, and generate consolidated dispatch orders to coordinate production operations efficiently.

Description

FIELD

[0001] The present invention relates generally to manufacturing andlogistics systems, and more particularly to computer-implemented methodsand systems for industrial operations management.BACKGROUND

[0002] Manufacturing facilities face significant challenges inoptimizing their production efficiency and resource utilization. Thesechallenges are compounded by the complexity of managing operationsacross multiple dimensions of the industrial process. Traditional approachesto production planning often result in suboptimal outcomes that impact bothoperational costs and customer satisfaction.

[0003] One particular area of difficulty lies in inventory managementacross distributed locations. Current monitoring systems frequently provideinsufficient visibility into actual material consumption patterns, making itchallenging to maintain appropriate stock levels. This lack of visibilitycreates cascading effects throughout the manufacturing process, asproduction decisions must be made without adequate information aboutactual demand conditions.

[0004] The transportation and delivery aspects of industrial operationsadd another layer of complexity to these challenges. Existing logisticsapproaches often fail to achieve optimal resource utilization, resulting inincreased costs and reduced profitability. These inefficiencies in the logisticschain further complicate the already challenging task of coordinatingmanufacturing activities with market demands.

[0005] Manufacturing execution systems typically encounteradditional difficulties when attempting to adapt to these changingoperational requirements. The inability to respond effectively to dynamicconditions often leads to production inefficiencies and missed opportunitiesfor optimization. This inflexibility becomes particularly problematic whenmultiple operational variables must be considered simultaneously.

[0006] These individual challenges combine to create broader issues insupply chain coordination. The complexity of managing multipleinterconnected processes often results in suboptimal performance across theentire operational spectrum. Material demand forecasting remains apersistent underlying problem, as inaccurate demand predictions frequentlylead to either excess inventory carrying costs or stockout situations thatimpact customer service levels.

[0007] The cumulative effect of these interconnected operationalchallenges demonstrates that existing operational management systems oftenlack the capability to effectively address the complex interdependenciesbetween various industrial processes. This fundamental limitation results infragmented approaches that fail to achieve optimal overall systemperformance.

[0008] Accordingly, there exists a need for improved systems andmethods that can address these operational challenges and provide enhancedefficiency in industrial manufacturing and logistics operations.OBJECTS OF INVENTION

[0009] An object of the present invention is to provide a computerimplemented method and system that improves operational efficiency inmanufacturing and logistics environments.

[0010] Another object of the present invention is to provide enhancedcoordination between different operational processes to achieve betterresource utilization.

[0011] The other object of the present invention is to provide improvedvisibility into operational conditions across multiple locations to supportbetter decision-making.

[0012] The further object of the present invention is to provideautomated processing capabilities that reduce manual intervention andhuman error in industrial operations.

[0013] Yet another object of the present invention is to provideoptimized allocation of resources to minimize operational costs whilemaintaining service quality.

[0014] A still further object of the present invention is to providedynamic operational adjustments that respond effectively to changingconditions in real-time.

[0015] Another object of the present invention is to provide integrationcapabilities that enable seamless communication between differentoperational systems and components.

[0016] The additional object of the present invention is to providescalable solutions that can accommodate varying operational requirementsand growth demands.SUMMARY

[0017] A computer-implemented method for optimizing coordinatedmanufacturing and logistics is disclosed, the method comprising acquiringreal-time inventory data from a plurality of monitoring devices located atmultiple customer sites; processing the acquired real-time inventory data todetermine material demand; grouping the customer sites based on proximity;calculating an aggregated material demand for each group; determining adelivery threshold for a group; upon the aggregated material demandreaching the delivery threshold: generating a consolidated dispatch order forthe group; and coordinating production based on the consolidated dispatchorder.

[0018] In one embodiment, acquiring real-time inventory datacomprises continuously acquiring data via a secure communication interface.

[0019] In one embodiment, the customer sites are geographicallydistinct.

[0020] In one embodiment, processing the acquired real-time inventorydata comprises creating standardized inventory status reports for eachcustomer location.

[0021] In one embodiment, grouping the customer sites comprisesapplying a geographic information system (GIS) algorithm to location dataassociated with the customer sites to create dynamic clusters.

[0022] In one embodiment, determining the delivery thresholdcomprises calculating the threshold based on technical vehicle parametersincluding truck capacity and material density.

[0023] In one embodiment, coordinating production comprisestransmitting control instructions to a manufacturing execution system (MES)to coordinate a just-in-time production schedule corresponding to theaggregated material demand.

[0024] In one embodiment, the method further comprises calculatingan optimized multi-stop vehicle delivery route for servicing the customerlocations within the group, said calculation being based on the consolidateddispatch order.

[0025] In one embodiment, calculating the optimized multi-stopvehicle delivery route is further based on real-time traffic data.

[0026] In one embodiment, the plurality of monitoring devicesincludes at least one of a level sensor, a weight monitor, or a flow meter.

[0027] In one embodiment, processing the acquired real-time inventorydata comprises applying data validation algorithms to ensure data accuracy.

[0028] In one embodiment, the method further comprises applyingpredictive analytics to historical inventory data to forecast futureconsumption patterns, wherein the forecasted patterns are used in calculatingthe aggregated material demand.

[0029] In one embodiment, calculating the optimized multi-stopvehicle delivery route comprises applying a vehicle routing problem (VRP)algorithm to minimize vehicle travel time or distance.

[0030] In one embodiment, the method further comprises dynamicallyadjusting the optimized multi-stop vehicle delivery route based on real-timevehicle location data received from a GPS tracking system.

[0031] A system for optimizing coordinated manufacturing andlogistics is also disclosed, the system comprising at least one processor; anda memory storing instructions that, when executed by the at least oneprocessor, cause the system to: acquire real-time inventory data from aplurality of monitoring devices located at multiple customer sites; processthe acquired real-time inventory data to determine material demand; groupthe customer sites based on proximity; calculate an aggregated materialdemand for each group; determine a delivery threshold for a group; upon theaggregated material demand reaching the delivery threshold: generate aconsolidated dispatch order for the group; and coordinate production basedon the consolidated dispatch order.

[0032] In one embodiment, the instructions further cause the system toacquire real-time inventory data continuously via a secure communicationinterface.

[0033] In one embodiment, the instructions further cause the system todetermine the delivery threshold by applying a machine learning modeltrained on historical delivery performance data to improve truck utilization.

[0034] In one embodiment, the system comprises a plurality ofinterconnected software modules, including: a real-time inventorymonitoring module configured to acquire and process the inventory data; acustomer clustering and proximity analysis module configured to group thecustomer locations; a demand aggregation and threshold calculation moduleconfigured to calculate the aggregated material demand and determine thedelivery threshold; an automated dispatch and order generation moduleconfigured to generate the consolidated dispatch order; and a manufacturingintegration and production scheduling module configured to coordinateproduction.

[0035] In one embodiment, the secure communication interfaceimplements data encryption protocols for all communications between thesystem, the monitoring devices, and external systems.BRIEF DESCRIPTION OF DRAWING

[0036] The aspects and other features of the subject matter will bebetter understood with regard to the following description, appended claims,and accompanying figures. The use of the same reference number in differentfigures indicates similar or identical items.

[0037] FIG. 1 illustrates a block diagram of a system for optimizingcoordinated manufacturing and logistics, in accordance with an embodimentof the present disclosure.

[0038] FIG. 2 illustrates a flowchart diagram of a computerimplemented method for optimizing coordinated manufacturing andlogistics, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0039] Manufacturing and logistics operations face persistentchallenges in achieving optimal coordination between production planningand material distribution. Current systems typically operate with limitedvisibility into real-time customer inventory levels, resulting in reactive ratherthan proactive operational management. Manufacturing facilities often relyon predetermined production schedules that may not accurately reflect actualcustomer demand conditions at any given time.

[0040] Existing inventory monitoring systems at customer locationsfrequently operate in isolation, providing insufficient data integration withcentral manufacturing and logistics operations. This lack of real-timecommunication creates inefficiencies in production planning and suboptimalresource utilization across the supply chain. Customer sites may experiencestockout conditions while production facilities continue operating onoutdated demand forecasts.

[0041] Transportation and delivery operations commonly suffer fromfragmented approaches that handle individual customer requests separately,without consideration of geographic proximity or consolidated deliveryopportunities. This results in multiple small deliveries to nearby locationsrather than optimized consolidated shipments that could achieve bettereconomies of scale and reduced transportation costs.

[0042] The absence of coordinated planning between inventorymonitoring, demand aggregation, production scheduling, and logisticsmanagement creates operational inefficiencies that impact both coststructures and customer service levels. Manufacturing execution systemsoften lack the capability to dynamically adjust production schedules basedon real-time demand signals from multiple customer locationssimultaneously.

[0043] The present invention provides a coordinated manufacturingand logistics optimization system that addresses these operational challengesthrough integrated real-time monitoring and automated decision-makingcapabilities. The system acquires real-time inventory data from monitoringdevices located at multiple customer sites and processes this information todetermine material demand across the customer base.

[0044] The system, in accordance with the present invention, groupscustomer sites based on geographic proximity to create delivery clusters thatenable consolidated shipment planning. By calculating aggregated materialdemand for each customer group, the system determines optimal deliverythresholds that consider operational parameters such as vehicle capacity andtransportation efficiency.

[0045] When aggregated demand for a customer group reaches thepredetermined delivery threshold, the system automatically generatesconsolidated dispatch orders that trigger coordinated production activities.This approach synchronizes manufacturing operations with actual customerdemand conditions rather than relying on static production schedules.

[0046] The system coordinates production based on consolidateddispatch orders, enabling just-in-time manufacturing that aligns productionoutput with delivery requirements. This coordination reduces inventorycarrying costs while improving customer service through more responsivematerial replenishment.

[0047] The invention integrates multiple operational functionsincluding inventory monitoring, demand aggregation, threshold calculation,dispatch order generation, and production coordination into a unified systemthat optimizes resource utilization across the entire manufacturing andlogistics value chain. The system provides automated decision-makingcapabilities that respond to real-time operational conditions whilemaintaining coordination between geographically distributed customer sitesand centralized manufacturing facilities.

[0048] Through this integrated approach, the invention enablesmanufacturing and logistics operations to achieve improved efficiency,reduced operational costs, and enhanced customer service while maintainingoperational flexibility to adapt to changing demand conditions.

[0049] The computer-implemented invention disclosed hereinpossesses technical character within the meaning of Article 52(1) EPC as itrelates to inventions in the field of technology. The invention is directed totechnical processes involving the control and coordination of manufacturingsystems and logistics operations through computer-implemented methodsthat provide further technical effects.

[0050] The technical effects achieved by the present invention include,but are not limited to:

[0051] - Technical improvement in manufacturing system coordinationthrough automated real-time control

[0052] - Technical optimization of logistics operations throughcomputer-controlled routing and scheduling

[0053] - Technical enhancement of resource utilization inmanufacturing processes

[0054] - Technical coordination between geographically distributedsystems through secure data communication interfaces

[0055] - Technical improvements to production scheduling accuracythrough automated demand calculation

[0056] - Technical optimization of delivery route planning throughalgorithmic computation

[0057] These technical effects go beyond the normal physicalinteractions between computer programs and the hardware on which theyrun, thereby conferring technical character to the claimed computerimplemented inventions.

[0058] The objective technical problem solved by the present inventionrelates to the technical challenge of coordinating multiple technical systemsin manufacturing and logistics environments. Existing technical systemssuffer from insufficient coordination between manufacturing executionsystems and logistics management systems, resulting in technicalinefficiencies in resource utilization and suboptimal performance ofindustrial control systems.

[0059] The invention provides a technical solution to this technicalproblem through computer-implemented methods that enable technicalcoordination between manufacturing systems and logistics systems in realtime, thereby achieving technical improvements in overall systemperformance and resource utilization efficiency.

[0060] Referring to FIG. 1, there is shown a system 100 for optimizingcoordinated manufacturing and logistics operations. The system 100comprises a plurality of monitoring devices 102 configured to acquire realtime inventory data from multiple customer sites. For example, when acustomer's material storage tank reaches a predetermined level, themonitoring devices 102 detect this condition and transmit the inventorystatus to the system 100. The monitoring devices 102 may include, but arenot limited to, level sensors, weight monitors, flow meters, optical sensors,proximity sensors, or any combination thereof, without limitation. Inalternative embodiments, the monitoring devices 102 may comprise wirelesssensors, wired sensors, Internet of Things (IoT) devices, or any other dataacquisition devices known in the art.

[0061] The monitoring devices 102 are operatively connected to asecure communication interface 104 that facilitates data transmissionbetween the customer sites and the central system 100. When multiplecustomer sites have varying inventory levels, the secure communicationinterface 104 collects this data and transmits it to the central processor foranalysis. The secure communication interface 104 may comprise wirelesscommunication protocols such as Wi-Fi, cellular networks, satellitecommunication, Bluetooth, Zigbee, LoRaWAN, or any other wirelesscommunication technology. Alternatively, the secure communicationinterface 104 may utilize wired communication methods including Ethernet,fiber optic cables, serial communication, or any combination of wired andwireless technologies, without limitation.

[0062] The system 100 further comprises a Geographic InformationSystem (GIS) 106 configured to process location data associated withcustomer sites. The GIS 106 receives geographic coordinates of customerlocations and determines which sites are located within reasonable distanceof each other for delivery route planning. For instance, if three customer sitesare located within a five-mile radius, the GIS 106 identifies these locationsas candidates for grouped delivery service. In alternative embodiments, theGIS 106 may be replaced by or supplemented with distance calculationalgorithms, mapping software, GPS-based positioning systems, or any otherlocation-based analysis tools known in the art, without limitation.

[0063] A central processor 108 is operatively connected to receive datafrom the monitoring devices 102 via the secure communication interface104. When inventory data from multiple customer sites is received, theprocessor 108 analyzes the information to determine current materialconsumption patterns and future demand requirements. The processor 108may comprise a single processing unit, multiple processing units, distributedprocessing systems, cloud-based processing resources, edge computingdevices, microprocessors, microcontrollers, application-specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs), or anycombination thereof, without limitation. The processor 108 is configured toexecute instructions stored in a memory 110.

[0064] The memory 110 stores executable instructions and operationaldata, enabling the system 100 to maintain historical records of customerconsumption patterns and delivery schedules. The memory 110 maycomprise volatile memory such as RAM, non-volatile memory such asROM, flash memory, solid-state drives, hard disk drives, optical storagedevices, magnetic storage devices, or any combination of storagetechnologies, without limitation. In alternative embodiments, the memory110 may be distributed across multiple storage devices, implemented incloud storage systems, or comprise hybrid storage architectures.

[0065] The system 100 includes a Real-Time Inventory Monitoringmodule 112 configured to process inventory data received from themonitoring devices 102. The module 112 converts raw sensor readings intostandardized inventory status reports that can be processed uniformlyregardless of the type of monitoring device used at each customer site. Forexample, data from a level sensor measuring liquid materials is normalizedto the same format as data from a weight monitor measuring solid materials.The module 112 may implement data validation algorithms to ensure dataaccuracy. In alternative embodiments, the module 112 may incorporateartificial intelligence algorithms for sensor data validation and errorcorrection using machine learning models trained on normal versusabnormal sensor readings, predictive maintenance capabilities formonitoring devices based on data quality degradation patterns, or data fusionalgorithms that combine multiple sensor inputs for more accurate inventoryestimates, without limitation. In further alternative embodiments, the RealTime Inventory Monitoring module 112 may comprise softwarecomponents, hardware components, firmware implementations, or anycombination thereof, without limitation.

[0066] A Customer Clustering & Proximity Analysis module 114 isconfigured to group customer sites based on geographic proximity using datafrom the GIS 106. The module 114 creates dynamic clusters by identifyingcustomer locations that can be served efficiently in a single delivery route.For instance, when five customer sites are located within a geographic regionthat can be serviced by one delivery vehicle in a single trip, the module 114groups these sites into a cluster for consolidated delivery planning. Themodule 114 may implement various clustering algorithms including, but notlimited to, k-means clustering, hierarchical clustering, density-basedclustering, or any other clustering methodology known in the art, withoutlimitation.

[0067] The system 100 further comprises a Demand Aggregation &Threshold Calculation module 116 configured to calculate aggregatedmaterial demand for each customer group. The module 116 determinesdelivery thresholds based on various parameters which may include, but arenot limited to, vehicle capacity, material density, transportation costs,customer priorities, seasonal variations, or any other relevant operationalfactors, without limitation. In alternative embodiments, the module 116 mayimplement machine learning algorithms, artificial intelligence techniques,statistical models, or predictive analytics to optimize threshold calculations.

[0068] An Automated Dispatch & Order Generation module 118 isconfigured to generate consolidated dispatch orders when aggregateddemand reaches predetermined thresholds. The module 118 creates triggersignals that initiate subsequent processing steps and may implement variousoptimization algorithms to determine optimal dispatch timing and quantities,without limitation.

[0069] A Manufacturing Integration & Scheduling module 120receives consolidated dispatch orders from the module 118 and coordinatesproduction activities. The module 120 may interface with ManufacturingExecution Systems (MES) 122 to implement Just-In-Time (JIT) productionschedules. In alternative embodiments, the module 120 may interface withEnterprise Resource Planning (ERP) systems, Production Planning Systems(PPS), or any other manufacturing control systems known in the art, withoutlimitation.

[0070] The system 100 optionally includes a Vehicle RouteOptimization module 124 configured to calculate optimized multi-stopdelivery routes. The module 124 may receive real-time traffic data 126 fromexternal traffic monitoring systems and implement Vehicle Routing Problem(VRP) algorithms to minimize travel time, distance, fuel consumption, orany combination of optimization objectives, without limitation.

[0071] Real-time vehicle location data may be provided by deliveryvehicles 128 equipped with GPS tracking systems 130. The GPS trackingsystems 130 enable dynamic route adjustments when delivery vehiclesencounter unexpected delays or traffic conditions. For example, if a deliveryvehicle is delayed at one customer location, the system can automaticallyadjust the route to optimize the remaining stops. The GPS tracking systems130 may comprise satellite-based positioning systems, cellular-basedpositioning, Wi-Fi positioning, or any other location determinationtechnology known in the art, without limitation.

[0072] The secure communication interface 104 implements dataencryption protocols to protect sensitive customer and operationalinformation during transmission between system components. The interface104 may implement various data encryption protocols including, but notlimited to, SSL / TLS encryption, AES encryption, RSA encryption, or anyother cryptographic security measures to protect data integrity andconfidentiality during transmission, without limitation.

[0073] The modules 112, 114, 116, 118, 120, and 124 operate incoordination to transform individual customer inventory requirements intocoordinated manufacturing and delivery operations. When inventory levelsat multiple customer sites indicate material needs, the system processes thisinformation to create efficient delivery clusters, calculate optimal productionquantities, and schedule coordinated manufacturing and logistics activities.The modules may be implemented as separate software components,integrated software packages, hardware circuits, firmware implementations,or any combination thereof. In alternative embodiments, the functionality ofmultiple modules may be combined into fewer modules, or individualmodules may be subdivided into additional specialized components, withoutdeparting from the scope of the invention.

[0074] The system 100 may operate in various deploymentconfigurations including, but not limited to, on-premises installations, cloudbased implementations, hybrid cloud architectures, edge computingdeployments, or distributed computing environments, without limitation.The system 100 accommodates varying operational requirements and can bescaled to support different numbers of customer sites, monitoring devices,and processing demands based on the specific needs of the manufacturingand logistics operation.

[0075] The specific configuration shown in Figure 1 represents oneembodiment of the invention, and numerous modifications and alternativeembodiments may be implemented without departing from the spirit andscope of the present invention. The interconnections between componentsmay be implemented using various communication protocols, data formats,and system architectures as appropriate for specific applications andoperational requirements.

[0076] Where the invention incorporates artificial intelligence ormachine learning components, these are implemented as technical solutionsadapted for specific technical applications in manufacturing and logisticscontrol systems. The AI algorithms are not claimed as abstract mathematicalmethods, but rather as computer-implemented methods that are technicallyadapted for solving specific technical problems in industrial controlenvironments.

[0077] The machine learning models disclosed herein are trained usingtechnical data from manufacturing processes and logistics operations, withthe specific technical purpose of improving the technical performance ofindustrial control systems. The technical implementation details include:

[0078] - Specific technical parameters for neural network architecturesadapted for industrial data processing

[0079] - Technical training methodologies using historical technicaldata from manufacturing systems

[0080] - Technical validation procedures for ensuring reliableoperation in industrial environments

[0081] - Technical integration interfaces with manufacturing executionsystems and logistics control systems

[0082] The technical character of these AI implementations isestablished through their adaptation to specific technical applications andtheir provision of technical effects in the control of manufacturing andlogistics systems.

[0083] Referring to FIG. 2, there is shown a method flow diagram fora computer-implemented method 200 for optimizing coordinatedmanufacturing and logistics operations. The method 200 provides asystematic approach for coordinating manufacturing activities with logisticsoperations based on real-time customer demand conditions.

[0084] At step 202, the method 200 includes acquiring real-timeinventory data from a plurality of monitoring devices located at multiplecustomer sites. The monitoring devices continuously monitor material levelsat customer locations and transmit current inventory status information to thecentral system. For example, when a customer's storage tank reaches apredetermined level, the monitoring device detects this condition andtransmits the inventory data to initiate the coordination process. Inalternative embodiments, acquiring real-time inventory data may comprisecontinuously acquiring data via a secure communication interface thatimplements encryption protocols for data protection during transmission,without limitation. In further alternative embodiments, the customer sitesmay be geographically distinct locations that span different regions, cities,or service areas. The plurality of monitoring devices may include at least oneof a level sensor, a weight monitor, or a flow meter, or any combination ofsensor technologies suitable for measuring material quantities, withoutlimitation.

[0085] At step 204, the method 200 includes processing the acquiredreal-time inventory data to determine material demand. The system analyzesthe inventory information from multiple customer sites to assess currentmaterial requirements and consumption patterns. For instance, if inventorydata indicates that multiple customers have reduced material levels, thesystem calculates the material demand needed to replenish these locations.In alternative embodiments, processing the acquired real-time inventory datamay comprise creating standardized inventory status reports for eachcustomer location to normalize data from different types of monitoringdevices, without limitation. In further alternative embodiments, processingmay comprise applying data validation algorithms to ensure data accuracyby identifying and correcting erroneous sensor readings or transmissionerrors. The method may also comprise applying predictive analytics tohistorical inventory data to forecast future consumption patterns, wherein theforecasted patterns are used in calculating material demand, withoutlimitation.

[0086] At step 206, the method 200 includes grouping the customersites based on proximity. The system analyzes the geographic locations ofcustomer sites to identify clusters of customers that can be served efficientlythrough consolidated delivery operations. For example, customer siteslocated within a predetermined geographic radius are grouped together toenable single-vehicle delivery routes that service multiple locations in onetrip. In alternative embodiments, grouping the customer sites may compriseapplying a geographic information system (GIS) algorithm to location dataassociated with the customer sites to create dynamic clusters that adapt basedon operational conditions, without limitation.

[0087] At step 208, the method 200 includes calculating an aggregatedmaterial demand for each group. The system combines the individualmaterial requirements of all customers within each geographic cluster todetermine the total material demand for that group. For instance, if threecustomers in a cluster require 100, 150, and 200 units respectively, thesystem calculates an aggregated demand of 450 units for that cluster. Thisaggregation enables the system to evaluate whether consolidated deliveryoperations are justified for each customer group.

[0088] At step 210, the method 200 includes determining a deliverythreshold for a group. The system establishes criteria for triggeringconsolidated delivery operations based on operational parameters andefficiency considerations. The delivery threshold represents the minimumaggregated demand level that justifies initiating a consolidated delivery to acustomer group. In alternative embodiments, determining the deliverythreshold may comprise calculating the threshold based on technical vehicleparameters including truck capacity and material density to optimizetransportation efficiency, without limitation.

[0089] At step 212, the method 200 includes determining whether theaggregated material demand has reached the delivery threshold. The systemcontinuously monitors the aggregated demand for each customer group andcompares it against the predetermined delivery threshold. This decision pointdetermines whether sufficient demand exists to proceed with consolidateddelivery operations or whether additional demand accumulation is required.

[0090] When the aggregated material demand reaches the deliverythreshold, the method proceeds to step 214, where the method 200 includesgenerating a consolidated dispatch order for the group. The system creates asingle dispatch order that encompasses the material requirements for allcustomers within the cluster, rather than processing individual orders foreach customer separately. This consolidated approach enables more efficientproduction planning and logistics coordination.

[0091] At step 216, the method 200 includes coordinating productionbased on the consolidated dispatch order. The system schedulesmanufacturing activities to produce the required materials in quantities andtiming that align with the consolidated delivery requirements. For example,upon receiving a consolidated order for 450 units, the system schedulesproduction to manufacture this quantity and coordinates the timing withplanned delivery operations. In alternative embodiments, coordinatingproduction may comprise transmitting control instructions to amanufacturing execution system (MES) to coordinate a just-in-timeproduction schedule corresponding to the aggregated material demand,without limitation.

[0092] In alternative embodiments, the method 200 may furthercomprise calculating an optimized multi-stop vehicle delivery route forservicing the customer locations within the group, said calculation beingbased on the consolidated dispatch order. The route optimization determinesthe most efficient sequence for visiting all customers within a cluster tominimize travel time and transportation costs. In further alternativeembodiments, calculating the optimized multi-stop vehicle delivery routemay be further based on real-time traffic data to adjust routes according tocurrent road conditions, without limitation. The route calculation maycomprise applying a vehicle routing problem (VRP) algorithm to minimizevehicle travel time or distance, or any other optimization objective suitablefor the specific operational requirements.

[0093] In additional alternative embodiments, the method 200 mayfurther comprise dynamically adjusting the optimized multi-stop vehicledelivery route based on real-time vehicle location data received from a GPStracking system. This capability enables the system to respond to unexpecteddelays or changing conditions during delivery operations by modifyingroutes in real-time to maintain optimal efficiency.

[0094] The method 200 provides a systematic approach that transformsindividual customer inventory requirements into coordinated manufacturingand logistics operations, enabling improved operational efficiency andresource utilization across the entire supply chain. The method steps may beimplemented using various computational technologies and may be adaptedto different operational scales and requirements without departing from thescope of the invention.

[0095] The present invention provides a non-obvious technicalcontribution to the prior art in the field of computer-implementedmanufacturing and logistics control systems. The technical novelty residesin the specific combination of technical features that provide technicalsolutions to previously unsolved technical problems in industrialcoordination systems.

[0096] The technical advantages achieved by the claimed inventioninclude:

[0097] - Technical improvement in system coordination efficiencythrough novel computer-implemented control methods

[0098] - Technical enhancement of resource utilization throughautomated optimization algorithms adapted for industrial applications

[0099] - Technical advancement in real-time industrial control throughimproved data processing and decision-making systems

[0100] - Technical innovation in the integration of manufacturingexecution systems with logistics control systems

[0101] These technical contributions represent non-obvious solutionsto technical problems that would not be apparent to a person skilled in theart based on the prior art available in the field of manufacturing and logisticscontrol systems.

[0102] The technical scope of protection sought encompasses thespecific technical means disclosed for achieving the technical effects inmanufacturing and logistics coordination. The claims are directed totechnical methods and systems that provide concrete technical solutionsrather than abstract ideas or non-technical business methods.

[0103] The technical subject matter claimed possesses technicalcharacter and provides technical contributions that establish both novelty andinventive step within the meaning of Articles 54 and 56 EPC respectively.

Claims

1. A computer-implemented method for optimizing coordinated manufacturing and logistics, the method comprising: acquiring real-time inventory data from a plurality of monitoring devices located at multiple customer sites; processing the acquired real-time inventory data to determine material demand; grouping the customer sites based on proximity; calculating an aggregated material demand for each group; determining a delivery threshold for a group; upon the aggregated material demand reaching the delivery threshold: generating a consolidated dispatch order for the group; and coordinating production based on the consolidated dispatch order.

2. The method of claim 1, wherein acquiring real-time inventory data comprises continuously acquiring data via a secure communication interface.

3. The method of claim 1, wherein the customer sites are geographically distinct.

4. The method of claim 1, wherein processing the acquired real-time inventory data comprises creating standardized inventory status reports for each customer location.

5. The method of claim 1, wherein grouping the customer sites comprises applying a geographic information system (GIS) algorithm to location data associated with the customer sites to create dynamic clusters.

6. The method of claim 1, wherein determining the delivery threshold comprises calculating the threshold based on technical vehicle parameters including truck capacity and material density.

7. The method of claim 1, wherein coordinating production comprises transmitting control instructions to a manufacturing execution system (MES) to coordinate a just-in-time production schedule corresponding to the aggregated material demand.

8. The method of claim 1, further comprising calculating an optimized multi-stop vehicle delivery route for servicing the customer locations within the group, said calculation being based on the consolidated dispatch order.

9. The method of claim 8, wherein calculating the optimized multi-stop vehicle delivery route is further based on real-time traffic data. 10.The method of claim 1, wherein the plurality of monitoring devices includes at least one of a level sensor, a weight monitor, or a flow meter. 11.The method of claim 4, wherein processing the acquired real-time inventory data comprises applying data validation algorithms to ensure data accuracy.12.The method of claim 1, further comprising applying predictive analytics to historical inventory data to forecast future consumption patterns, wherein the forecasted patterns are used in calculating the aggregated material demand. 13.The method of claim 8, wherein calculating the optimized multi-stop vehicle delivery route comprises applying a vehicle routing problem (VRP) algorithm to minimize vehicle travel time or distance. 14.The method of claim 9, further comprising dynamically adjusting the optimized multi-stop vehicle delivery route based on real-time vehicle location data received from a GPS tracking system. 15.A system for optimizing coordinated manufacturing and logistics, the system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to: acquire real-time inventory data from a plurality of monitoring devices located at multiple customer sites; process the acquired real-time inventory data to determine material demand; group the customer sites based on proximity; calculate an aggregated material demand for each group; determine a delivery threshold for a group; upon the aggregated material demand reaching the delivery threshold: generate a consolidated dispatch order for the group; and coordinate production based on the consolidated dispatch order. 16.The system of claim 15, wherein the instructions further cause the system to acquire real-time inventory data continuously via a secure communication interface. 17.The system of claim 15, wherein the instructions further cause the system to determine the delivery threshold by applying a machine learning model trained on historical delivery performance data to improve truck utilization. 18.The system of claim 15, wherein the system comprises a plurality of interconnected software modules, including: a real-time inventory monitoring module configured to acquire and process the inventory data; a customer clustering and proximity analysis module configured to group the customer locations; a demand aggregation and threshold calculation module configured to calculate the aggregated material demand and determine the delivery threshold; an automated dispatch and order generation module configured to generate the consolidated dispatch order; and a manufacturing integration and production scheduling module configured to coordinate production. 19.The system of claim 16, wherein the secure communication interface implements data encryption protocols for all communications between the system, the monitoring devices, and external systems.