MLchartDataset catalogue

Patent · US12380418B2 · B2 · US

Adaptive additive manufacturing for value chain networks

(11) Publication number
US12380418B2
(21) Application number
17/683,145
(22) Filing date
2022-02-28
(30) Priority date
2020-12-18
(43) Publication date
2025-08-05
(45) Date of grant
2025-08-05
(51) IPC
B25J 9/16; B29C 64/386; B29C 64/393; B33Y 10/00; B33Y 50/00; B33Y 50/02; G02B 26/00; G02B 3/14; G05B 13/02; G05B 13/04; G05B 17/02; G05B 19/402; G05B 19/4099; G05D 1/00; G05D 1/221; G06F 113/10; G06F 30/27; G06N 20/00; G06N 20/20; G06Q 10/06; G06Q 10/0631; G06Q 10/0633; G06Q 10/0831; G06Q 10/0833; G06Q 10/087; G06Q 20/14; G06Q 30/0201; G06T 7/70; H04L 9/00; H04L 9/32; H04L 9/40
(52) CPC
  • G06Q Information and communication technology [ICT] specially adapted for administrative, commercial, financial, managerial or supervisory purposes; systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not otherwise provided for: 20/14, 10/06, 10/0631, 10/06311, 10/063114, 10/06313, 10/06316, 10/0633, 10/0635, 10/06395, 10/0831, 10/0833, 10/087, 10/10, 2220/00, 30/0201, 50/04
  • B22F Working metallic powder; manufacture of articles from metallic powder; making metallic powder; apparatus or devices specially adapted for metallic powder: 10/70, 10/85, 2998/00
  • B25J Manipulators; chambers provided with manipulation devices: 9/161, 9/163, 9/1653, 9/1661, 9/1671, 9/1682, 9/1697
  • B29C Shaping or joining of plastics; shaping of material in a plastic state, not otherwise provided for; after-treatment of the shaped products, e.g. repairing: 64/10, 64/357, 64/379, 64/386, 64/393
  • B33Y Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering: 10/00, 40/00, 50/00, 50/02
  • G02B Optical elements, systems or apparatus: 26/00, 3/14
  • G05B Control or regulating systems in general; functional elements of such systems; monitoring or testing arrangements for such systems or elements: 13/0265, 13/042, 17/02, 19/402, 19/4097, 19/4099, 19/41865, 2219/32015, 2219/32117, 2219/32254, 2219/32291, 2219/32365, 2219/33006, 2219/35134, 2219/36252, 2219/39146, 2219/39167, 2219/40113, 2219/49023
  • G05D Systems for controlling or regulating non-electric variables: 1/0027, 1/0221, 1/0297, 1/221, 1/6987
  • G06F Electric digital data processing: 2113/10, 30/27
  • G06N Computing arrangements based on specific computational models: 20/00, 20/10, 20/20, 3/006, 3/045, 3/0464, 3/084, 3/088, 3/09, 5/025
  • G06T Image data processing or generation, in general: 2207/20081, 7/70
  • H04L Transmission of digital information, e.g. telegraphic communication: 63/1441, 9/3239, 9/50
  • Y02P Climate change mitigation technologies in the production or processing of goods: 80/10, 80/40, 90/02, 90/30, 90/84
(73) Assignee
Strong Force VCN Portfolio 2019 LLC
(72) Inventors
Charles H. Cella; Brent BLIVEN; Kunal SHARMA; Teymour S. EL-TAHRY
(54) Title
Adaptive additive manufacturing for value chain networks
(57) Abstract

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

Full text
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Claims (14)

  1. An information technology system for a distributed manufacturing network, the information technology system comprising: an additive manufacturing management platform configured to manage one or more production processes and production workflows for a distributed manufacturing platform, including a set of distributed manufacturing network entities, through at least one of: design, modeling, printing, or supply chain stages, wherein the set of distributed manufacturing network entities include at least one of: an additive manufacturing unit, a manufacturing node, or another type of manufacturing unit; an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to train models of the one or more production processes and production workflows for each of the set of distributed manufacturing network entities and the distributed manufacturing platform; a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network, including a customer for a component, wherein the digital thread includes more than one instruction set for manufacturing the component, each instruction set is configured to facilitate manufacturing of the component using a distinct form of additive manufacturing equipment, or a hybrid combination of manufacturing equipment; a digital twin system configured to build manufacturing device digital twins for each of the set of distributed manufacturing network entities based on the trained models and an environment digital twin for the distributed manufacturing platform, wherein each manufacturing device digital twin is configured to include a capacity and a set of supported manufacturing capabilities, including at least one of: a manufacturing type, material selection, or a throughput, wherein the environment digital twin includes a set of existing one or more customers, and wherein each manufacturing device digital twin is configured to provide a substantially real-time representation of a corresponding one of the set of distributed manufacturing network entities through data from one or more sensors positioned in, on, or near the corresponding one of the set of distributed manufacturing network entities; and a control system configured to adjust the data and one or more parameters collected from the set of distributed manufacturing network entities in real time, wherein the artificial intelligence system generates a set of manufacturing options by executing simulations on the manufacturing device digital twins and the environment digital twin for predicting a set of impacts corresponding to the set of manufacturing options on the distributed manufacturing platform, including the more than one instruction set for manufacturing the component included in the digital thread, and wherein the additive manufacturing management platform schedules the manufacturing of the component based on the set of generated manufacturing options and their respective impacts on the distributed manufacturing platform and a set of existing one or more customer orders and manufactures the component based on at least one of the manufacturing schedules.
  2. The information technology system of claim 1, wherein scheduling the manufacturing of the component based on the generated set of manufacturing options and the corresponding respective impacts on the distributed manufacturing platform includes selecting at least one of the generated set of manufacturing options that minimizes waste production or maximizes material recapture and recycling.
  3. The information technology system of claim 1, wherein scheduling the manufacturing of the component based on the generated set of manufacturing options and their corresponding predicted impacts on the distributed manufacturing platform includes selecting at least one of the generated set of manufacturing options that: optimizes a material utilization, optimizes an energy utilization, or optimizes a labor utilization.
  4. The information technology system of claim 1, wherein scheduling the manufacturing of the component based on the generated set of manufacturing options and their respective impacts on the distributed manufacturing platform includes selecting at least one of the generated set of manufacturing options based on at least one of: a material cost, an energy cost, or a labor cost.
  5. A distributed manufacturing network comprising: an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities in a distributed manufacturing environment including at least one of: an additive manufacturing unit, a manufacturing node, or another type of manufacturing unit; an artificial intelligence system configured to train a set of machine-learned models using learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities; a digital twin system configured to build manufacturing digital twins of the set of distributed manufacturing network entities and an environment digital twin based on the distributed manufacturing environment and the trained set of machine-learned models, wherein each manufacturing digital twin is configured to provide a substantially real-time representation of a corresponding distributed manufacturing network entity through data from one or more sensors positioned in, on, or near the corresponding distributed manufacturing network entity; a control system configured to adjust the data and one or more parameters collected from the set of distributed manufacturing network entities in real time; wherein each manufacturing digital twin is configured to include a set of supported manufacturing capabilities, including at least one of: a manufacturing type, a material selection, a material utilization, a throughput, or an energy utilization, and wherein the artificial intelligence system is configured to generate an order fulfillment option by executing a simulation using the manufacturing digital twins and the environment digital twin, wherein the order fulfillment option includes an impact on the distributed manufacturing environment; a distributed ledger configured to store details of a set of customer orders, wherein each of a subset of the set of customer orders includes a corresponding digital thread including more than one instruction set to facilitate manufacturing of a component of a corresponding customer order using a distinct form of additive manufacturing equipment, or a hybrid combination of manufacturing equipment; and a job management system configured to receive a new customer order, wherein the new customer order includes at least one digital thread, wherein the artificial intelligence system generates a set of order fulfillment options by executing a set of simulations, the set of simulations including simulations of alternative scheduling sequences based on the more than one instruction set, across the set of distributed manufacturing network entities and the distributed manufacturing environment, and wherein the additive manufacturing management platform schedules manufacturing of a component of the new customer order based on the new customer order and the generated set of order fulfillment options and manufactures the component of the new customer order based on at least one of the manufacturing schedules.
  6. The distributed manufacturing network of claim 5 wherein the new customer order includes digital threads that encode information related to a complete lifecycle of a portion of the new customer order from at least one of: design, modeling, production, validation, use, maintenance, or through disposal.
  7. The distributed manufacturing network of claim 5, wherein the new customer order is at least one of a price contingency order or a timing contingency order.
  8. The distributed manufacturing network of claim 5, wherein scheduling manufacturing of the new customer order based on the generated set of order fulfillment options and their respective impacts on the distributed manufacturing environment includes selecting at least one of the generated set of order fulfillment options based on at least one of: a material cost, an energy cost, a labor cost, or a timeline.
  9. The distributed manufacturing network of claim 5, wherein scheduling the manufacturing of the component based on the generated set of order fulfillment options and their respective impacts on the distributed manufacturing environment includes selecting at least one of the generated set of order fulfillment options based on at least one of: a minimization of waste production, a maximization of material recapture, or a maximization of recycling.
  10. The distributed manufacturing network of claim 5, wherein scheduling the manufacturing of the component based on the set of generated order fulfillment options and their respective impacts on the distributed manufacturing environment includes selecting at least one of the generated set of order fulfillment options that: optimizes a material utilization, optimizes an energy utilization, or optimizes a labor utilization.
  11. A method for managing a distributed manufacturing network, the method comprising: receiving, by a processor executing an additive manufacturing management platform, production process data and production workflow data from a set of distributed manufacturing network entities including at least one of an additive manufacturing unit or a manufacturing node; training, by a processor executing an artificial intelligence system, machine-learned models for each of the set of distributed manufacturing network entities and the distributed manufacturing network, using a training set of parameters and data collected from the set of distributed manufacturing network entities and corresponding outcomes; creating a set of manufacturing digital twins, each of the set of manufacturing digital twins corresponding to one of the set of distributed manufacturing network entities, and an environment digital twin for the distributed manufacturing network using the machine learned models, wherein each of the set of manufacturing digital twins is configured to include a capacity and a set of supported manufacturing capabilities, including at least one of: a manufacturing type, a material selection, or a throughput, and wherein each of the set of manufacturing digital twins is configured to provide a substantially real-time representation of the corresponding one of the set of distributed manufacturing network entities through data from one or more sensors positioned in, on, or near the corresponding one of the set of distributed manufacturing network entities, receiving a customer manufacturing order, wherein the customer manufacturing order is part of a digital thread in a distributed ledger, wherein the digital thread includes more than one instruction set for manufacturing a component of the customer manufacturing order, each instruction set is configured to facilitate manufacturing of the component using a distinct form of additive manufacturing equipment, or a hybrid combination of manufacturing equipment; updating each of the set of manufacturing digital twins and the environment digital twin based on inventory and real-time data collected from the set of distributed manufacturing network entities; generating a set of manufacturing options by simulating different manufacturing strategies for fulfilling the customer manufacturing order using the set of manufacturing digital twins and the environment digital twin, including simulating manufacturing strategies based on the more than one instruction set of the digital thread; coordinating manufacture of the component of the customer manufacturing order across the distributed manufacturing network; scheduling the manufacture of the component of the customer manufacturing order based on the set of generated manufacturing options and their respective impacts on the distributed manufacturing network and a set of existing customer orders; adjusting the data and one or more parameters collected from the set of distributed manufacturing network entities in real time; and manufacturing the component of the customer manufacturing order based on the scheduled manufacturing of the component.
  12. The method of claim 11, wherein scheduling the manufacturing of the customer manufacturing order includes selecting a manufacturing option of the set of the generated manufacturing options that minimizes a waste production, maximizes a material recapture or maximizes a material recycling.
  13. The method of claim 11, wherein scheduling the manufacturing of the customer manufacturing order includes selecting a manufacturing option of the set of the generated manufacturing options based on at least one of: optimizing a material utilization, optimizing an energy utilization, or optimizing a labor utilization.
  14. The method of claim 11, wherein scheduling the manufacturing of the customer manufacturing order includes selecting a manufacturing option of the set of the generated manufacturing options based on at least one of: a material cost, an energy cost, or a labor cost.

Description

The present disclosure relates to information technology methods and systems for management of value chain network entities, including supply chain and demand management entities. The present disclosure also relates to the field of enterprise management platforms, more particularly involving data management, artificial intelligence, network connectivity and digital twins, additive manufacturing, robotics-as-a-service, and energy management.

Historically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were supplied mainly through a relatively linear fashion, in which manufacturers and other suppliers of finished goods, components, and other items handed off items to shipping companies, freight forwarders and the like, who delivered them to warehouses for temporary storage, to retailers, where customers purchased them, or directly to customer locations. Manufacturers and retailers undertook various sales and marketing activities to encourage and meet demand by customers, including designing products, positioning them on shelves and in advertising, setting prices, and the like.

Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order.

Citations (94)

  • US20030081019A1
  • US20080133383A1
  • US20210256580A1
  • KR101522110B1
  • US20140365258A1
  • JP2014021657A
  • US20140223583A1
  • US20150154321A1
  • US20160375676A1
  • US20200184278A1
  • US20190087529A1
  • US10611085B1
  • US20170075944A1
  • US9956727B2
  • US20180339448A1
  • US20180304370A1
  • US20190176387A1
  • US20170259502A1
  • US20170285585A1
  • US20210157312A1
  • US20190041835A1
  • US20170334141A1
  • US20180136633A1
  • US20190286384A1
  • US20180069899A1
  • US20180024517A1
  • US20180089563A1
  • US20180094953A1
  • US20180114368A1
  • US20190339671A1
  • US20200030979A1
  • US20180329658A1
  • US20180341248A1
  • US20200133235A1
  • US20200293822A1
  • US20190025800A1
  • US20190049929A1
  • US20190087598A1
  • US20190102667A1
  • US20200306978A1
  • US20200295942A1
  • US20190138662A1
  • US20190317935A1
  • US20200112545A1
  • US20190258747A1
  • US10520922B2
  • US20190303636A1
  • US20210236251A1
  • US11150633B2
  • US20190366644A1
  • US20200027585A1
  • US20220391845A1
  • US20200073850A1
  • US20200103857A1
  • US20210287459A1
  • US20200125078A1
  • US20200156321A1
  • US20200174713A1
  • US20220036404A1
  • US20220061958A1
  • WO2020142499A1
  • US20200272129A1
  • US20200306927A1
  • US20200341452A1
  • US20200341432A1
  • US20200370504A1
  • US20190340843A1
  • US20220269259A1
  • US20210016505A1
  • US20210064812A1
  • US20210096827A1
  • US20210178697A1
  • US20230044533A1
  • US20230041863A1
  • US20230101500A1
  • US20210326974A1
  • US20230158637A1
  • US20230131125A1
  • US20210356950A1
  • US20220410483A1
  • US20220004678A1
  • US20220058478A1
  • US20220334563A1
  • US20220100171A1
  • US11295513B1
  • US20220161497A1
  • US20220172258A1
  • US20220171898A1
  • US20220183703A1
  • US20220197522A1
  • US20240131591A1
  • US20220292617A1
  • US20220318752A1
  • US20220413989A1
Record as JSON 
{
  "publication_number": "US12380418B2",
  "country": "US",
  "kind": "B2",
  "title": "Adaptive additive manufacturing for value chain networks",
  "abstract": "An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.",
  "claims": [
    "1. An information technology system for a distributed manufacturing network, the information technology system comprising: an additive manufacturing management platform configured to manage one or more production processes and production workflows for a distributed manufacturing platform, including a set of distributed manufacturing network entities, through at least one of: design, modeling, printing, or supply chain stages, wherein the set of distributed manufacturing network entities include at least one of: an additive manufacturing unit, a manufacturing node, or another type of manufacturing unit; an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities to train models of the one or more production processes and production workflows for each of the set of distributed manufacturing network entities and the distributed manufacturing platform; a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network, including a customer for a component, wherein the digital thread includes more than one instruction set for manufacturing the component, each instruction set is configured to facilitate manufacturing of the component using a distinct form of additive manufacturing equipment, or a hybrid combination of manufacturing equipment; a digital twin system configured to build manufacturing device digital twins for each of the set of distributed manufacturing network entities based on the trained models and an environment digital twin for the distributed manufacturing platform, wherein each manufacturing device digital twin is configured to include a capacity and a set of supported manufacturing capabilities, including at least one of: a manufacturing type, material selection, or a throughput, wherein the environment digital twin includes a set of existing one or more customers, and wherein each manufacturing device digital twin is configured to provide a substantially real-time representation of a corresponding one of the set of distributed manufacturing network entities through data from one or more sensors positioned in, on, or near the corresponding one of the set of distributed manufacturing network entities; and a control system configured to adjust the data and one or more parameters collected from the set of distributed manufacturing network entities in real time, wherein the artificial intelligence system generates a set of manufacturing options by executing simulations on the manufacturing device digital twins and the environment digital twin for predicting a set of impacts corresponding to the set of manufacturing options on the distributed manufacturing platform, including the more than one instruction set for manufacturing the component included in the digital thread, and wherein the additive manufacturing management platform schedules the manufacturing of the component based on the set of generated manufacturing options and their respective impacts on the distributed manufacturing platform and a set of existing one or more customer orders and manufactures the component based on at least one of the manufacturing schedules.",
    "2. The information technology system of claim 1, wherein scheduling the manufacturing of the component based on the generated set of manufacturing options and the corresponding respective impacts on the distributed manufacturing platform includes selecting at least one of the generated set of manufacturing options that minimizes waste production or maximizes material recapture and recycling.",
    "3. The information technology system of claim 1, wherein scheduling the manufacturing of the component based on the generated set of manufacturing options and their corresponding predicted impacts on the distributed manufacturing platform includes selecting at least one of the generated set of manufacturing options that: optimizes a material utilization, optimizes an energy utilization, or optimizes a labor utilization.",
    "4. The information technology system of claim 1, wherein scheduling the manufacturing of the component based on the generated set of manufacturing options and their respective impacts on the distributed manufacturing platform includes selecting at least one of the generated set of manufacturing options based on at least one of: a material cost, an energy cost, or a labor cost.",
    "5. A distributed manufacturing network comprising: an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities in a distributed manufacturing environment including at least one of: an additive manufacturing unit, a manufacturing node, or another type of manufacturing unit; an artificial intelligence system configured to train a set of machine-learned models using learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities; a digital twin system configured to build manufacturing digital twins of the set of distributed manufacturing network entities and an environment digital twin based on the distributed manufacturing environment and the trained set of machine-learned models, wherein each manufacturing digital twin is configured to provide a substantially real-time representation of a corresponding distributed manufacturing network entity through data from one or more sensors positioned in, on, or near the corresponding distributed manufacturing network entity; a control system configured to adjust the data and one or more parameters collected from the set of distributed manufacturing network entities in real time; wherein each manufacturing digital twin is configured to include a set of supported manufacturing capabilities, including at least one of: a manufacturing type, a material selection, a material utilization, a throughput, or an energy utilization, and wherein the artificial intelligence system is configured to generate an order fulfillment option by executing a simulation using the manufacturing digital twins and the environment digital twin, wherein the order fulfillment option includes an impact on the distributed manufacturing environment; a distributed ledger configured to store details of a set of customer orders, wherein each of a subset of the set of customer orders includes a corresponding digital thread including more than one instruction set to facilitate manufacturing of a component of a corresponding customer order using a distinct form of additive manufacturing equipment, or a hybrid combination of manufacturing equipment; and a job management system configured to receive a new customer order, wherein the new customer order includes at least one digital thread, wherein the artificial intelligence system generates a set of order fulfillment options by executing a set of simulations, the set of simulations including simulations of alternative scheduling sequences based on the more than one instruction set, across the set of distributed manufacturing network entities and the distributed manufacturing environment, and wherein the additive manufacturing management platform schedules manufacturing of a component of the new customer order based on the new customer order and the generated set of order fulfillment options and manufactures the component of the new customer order based on at least one of the manufacturing schedules.",
    "6. The distributed manufacturing network of claim 5 wherein the new customer order includes digital threads that encode information related to a complete lifecycle of a portion of the new customer order from at least one of: design, modeling, production, validation, use, maintenance, or through disposal.",
    "7. The distributed manufacturing network of claim 5, wherein the new customer order is at least one of a price contingency order or a timing contingency order.",
    "8. The distributed manufacturing network of claim 5, wherein scheduling manufacturing of the new customer order based on the generated set of order fulfillment options and their respective impacts on the distributed manufacturing environment includes selecting at least one of the generated set of order fulfillment options based on at least one of: a material cost, an energy cost, a labor cost, or a timeline.",
    "9. The distributed manufacturing network of claim 5, wherein scheduling the manufacturing of the component based on the generated set of order fulfillment options and their respective impacts on the distributed manufacturing environment includes selecting at least one of the generated set of order fulfillment options based on at least one of: a minimization of waste production, a maximization of material recapture, or a maximization of recycling.",
    "10. The distributed manufacturing network of claim 5, wherein scheduling the manufacturing of the component based on the set of generated order fulfillment options and their respective impacts on the distributed manufacturing environment includes selecting at least one of the generated set of order fulfillment options that: optimizes a material utilization, optimizes an energy utilization, or optimizes a labor utilization.",
    "11. A method for managing a distributed manufacturing network, the method comprising: receiving, by a processor executing an additive manufacturing management platform, production process data and production workflow data from a set of distributed manufacturing network entities including at least one of an additive manufacturing unit or a manufacturing node; training, by a processor executing an artificial intelligence system, machine-learned models for each of the set of distributed manufacturing network entities and the distributed manufacturing network, using a training set of parameters and data collected from the set of distributed manufacturing network entities and corresponding outcomes; creating a set of manufacturing digital twins, each of the set of manufacturing digital twins corresponding to one of the set of distributed manufacturing network entities, and an environment digital twin for the distributed manufacturing network using the machine learned models, wherein each of the set of manufacturing digital twins is configured to include a capacity and a set of supported manufacturing capabilities, including at least one of: a manufacturing type, a material selection, or a throughput, and wherein each of the set of manufacturing digital twins is configured to provide a substantially real-time representation of the corresponding one of the set of distributed manufacturing network entities through data from one or more sensors positioned in, on, or near the corresponding one of the set of distributed manufacturing network entities, receiving a customer manufacturing order, wherein the customer manufacturing order is part of a digital thread in a distributed ledger, wherein the digital thread includes more than one instruction set for manufacturing a component of the customer manufacturing order, each instruction set is configured to facilitate manufacturing of the component using a distinct form of additive manufacturing equipment, or a hybrid combination of manufacturing equipment; updating each of the set of manufacturing digital twins and the environment digital twin based on inventory and real-time data collected from the set of distributed manufacturing network entities; generating a set of manufacturing options by simulating different manufacturing strategies for fulfilling the customer manufacturing order using the set of manufacturing digital twins and the environment digital twin, including simulating manufacturing strategies based on the more than one instruction set of the digital thread; coordinating manufacture of the component of the customer manufacturing order across the distributed manufacturing network; scheduling the manufacture of the component of the customer manufacturing order based on the set of generated manufacturing options and their respective impacts on the distributed manufacturing network and a set of existing customer orders; adjusting the data and one or more parameters collected from the set of distributed manufacturing network entities in real time; and manufacturing the component of the customer manufacturing order based on the scheduled manufacturing of the component.",
    "12. The method of claim 11, wherein scheduling the manufacturing of the customer manufacturing order includes selecting a manufacturing option of the set of the generated manufacturing options that minimizes a waste production, maximizes a material recapture or maximizes a material recycling.",
    "13. The method of claim 11, wherein scheduling the manufacturing of the customer manufacturing order includes selecting a manufacturing option of the set of the generated manufacturing options based on at least one of: optimizing a material utilization, optimizing an energy utilization, or optimizing a labor utilization.",
    "14. The method of claim 11, wherein scheduling the manufacturing of the customer manufacturing order includes selecting a manufacturing option of the set of the generated manufacturing options based on at least one of: a material cost, an energy cost, or a labor cost."
  ],
  "description_excerpt": "The present disclosure relates to information technology methods and systems for management of value chain network entities, including supply chain and demand management entities. The present disclosure also relates to the field of enterprise management platforms, more particularly involving data management, artificial intelligence, network connectivity and digital twins, additive manufacturing, robotics-as-a-service, and energy management.\n\nHistorically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were supplied mainly through a relatively linear fashion, in which manufacturers and other suppliers of finished goods, components, and other items handed off items to shipping companies, freight forwarders and the like, who delivered them to warehouses for temporary storage, to retailers, where customers purchased them, or directly to customer locations. Manufacturers and retailers undertook various sales and marketing activities to encourage and meet demand by customers, including designing products, positioning them on shelves and in advertising, setting prices, and the like.\n\nOrders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order.",
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  "assignees": [
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  "inventors": [
    "Charles H. Cella",
    "Brent BLIVEN",
    "Kunal SHARMA",
    "Teymour S. EL-TAHRY"
  ],
  "filing_date": "2022-02-28",
  "publication_date": "2025-08-05",
  "grant_date": "2025-08-05",
  "priority_date": "2020-12-18",
  "application_number": "US-202217683145-A",
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}

Record 132 of 8,000 in Patents full text (MLC-0201). Request the full dataset.