US-12626215-B2 - Control tower and enterprise management platform with a machine learning/artificial intelligence managing sensor and the camera feeds into digital twin

US12626215B2US 12626215 B2US12626215 B2US 12626215B2US-12626215-B2

Abstract

An information technology generally including a set of monitoring facilities that are configured to monitor the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.

Inventors

Charles Howard Cella
Richard Spitz
Teymour S. EL-TAHRY
Joshua DOBROWITSKY
Jenna Parenti
Brent BLIVEN
Andrew Cardno

Assignees

STRONG FORCE VCN PORTFOLIO 2019, LLC

Dates

Publication Date: 20260512
Application Date: 20201204

Claims (12)

1 . A value chain system that provides recommendations for designing a logistics system comprising: a machine learning system, implemented by at least one processor, that trains a first machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the first machine-learned model based, at least in part, on a first training data set that includes features of logistics systems and corresponding outcomes; an artificial intelligence system, implemented by at least one processor, that receives a first request for a first logistics system design and determines a first logistics system design recommendation based on the first machine-learned model and a set of features included in the first request; and a digital twin system, implemented by at least one processor, configured to: generate a logistics environment digital twin of a logistics environment that incorporates the first logistics system design recommendation and one or more physical asset digital twins of physical assets; simulate a first logistics operation performance based on the logistics environment digital twin and the one or more physical asset digital twins to generate a simulation result that includes at least one simulated outcome corresponding to the first logistics system design recommendation; provide the simulation result of the first logistics operations performance simulation to the machine learning system to retrain the first machine-learned model, wherein the retraining results in a second machine-learned model; issue a logistics system design request to the artificial intelligence system for a second logistics system design recommendation based on a result of the first logistics operations performance simulation and the second machine-learned model; and update the logistics environment digital twin based on the second logistics system design recommendation, wherein the retraining includes training the second machine-learned model based on the first training data set and the result of the first logistics operations performance simulation.
2 . The value chain system of claim 1 , wherein the digital twin system outputs a graphical representation of the logistics environment digital twin to a display, whereby a user views the first logistics operations performance simulation via the display.
3 . The value chain system of claim 1 , wherein the artificial intelligence system receives the first request from a logistics design system, wherein the first request includes one or more logistics factors corresponding to a first proposed logistics solution of an organization.
4 . The value chain system of claim 3 , wherein the one or more logistics factors include at least one of a type of product corresponding to a proposed logistics solution, a feature of the type of product, a location of a manufacturing site, a location of a distribution facility, a location of a warehouse, a location of a customer base, a proposed expansion area of the organization, or a supply chain feature.
5 . The value chain system of claim 1 , wherein the artificial intelligence system determines the first logistics system design recommendation to minimize delay times.
6 . The value chain system of claim 1 , wherein the artificial intelligence system determines the first logistics system design recommendation to comply with regulatory requirements.
7 . The value chain system of claim 1 , wherein the second machine-learned model differs from the first machine-learned model by at least one of a number of inputs, a type of inputs, a weight of an input, a configuration of nodes, or a process flow.
8 . The value chain system of claim 1 , wherein the set of features included in the first request include at least one of an availability of space in at least one of a vehicle, a container, a package, a warehouse, a fulfillment center, or a shelf.
9 . The value chain system of claim 1 , wherein the logistics system includes at least one logistics process such as a pickup of goods, a delivery of goods, a transfer of goods onto hauling facilities, a loading of goods, an unloading of goods, a packing of goods, a picking of goods, a shipping of goods, or a driving of goods.
10 . A computer-implemented method of designing a logistics system, the method comprising: training a first machine-learned model on a first training data set, wherein the first training data set includes logistics features and outcomes corresponding to the logistics features, and wherein the first machine-learned model outputs a logistics design recommendation given a set of input features; receiving a first request for a logistics system design; determining, based, at least in part, on the first machine-learned model, a first logistics system design recommendation in response to the first request for a first logistics system design; generating a logistics environment digital twin that incorporates the first logistics system design recommendation; simulate a first logistics operation performance based on the logistics environment digital twin; training a second machine-learned model based on a result of the first logistics operations performance simulation based on the first logistics system design recommendation, and at least one of the first training data set, or the first machine-learned model; receiving a second request for a second logistics system design; and determining, based, at least in part, on the second machine-learned model, a second logistics system design recommendation in response to the second request for the second logistics system design.
11 . The method of claim 10 , further comprising displaying the first logistics operation performance simulation for observation by a user.
12 . The method of claim 10 , wherein the first logistics system design recommendation is structured to minimize delay times.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a bypass continuation of International Patent Applications Numbers PCT/US2020/059224 and PCT/US2020/059227, each filed on Nov. 5, 2020 and each entitled “CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM FOR VALUE CHAIN NETWORKS,” each of which claims the benefit of priority to the following U.S. Provisional Patent Applications: Ser. No. 62/931,193, filed Nov. 5, 2019, entitled “METHODS AND SYSTEMS OF VALUE CHAIN NETWORK MANAGEMENT PLATFORM;” Ser. No. 62/969,153, filed Feb. 3, 2020, entitled “METHODS AND SYSTEMS OF VALUE CHAIN NETWORK MANAGEMENT PLATFORM;” Ser. No. 63/016,976, filed Apr. 28, 2020, entitled “DIGITAL TWIN SYSTEMS AND METHODS FOR FACILITATING VALUE CHAIN NETWORKS AND LOGISTICS;” Ser. No. 63/054,606, filed Jul. 21, 2020, entitled “DIGITAL TWIN SYSTEMS AND METHODS FOR FACILITATING VALUE CHAIN NETWORKS AND LOGISTICS;” Ser. No. 63/069,533, filed Aug. 24, 2020, entitled “INFORMATION TECHNOLOGY SYSTEMS AND METHODS FOR VALUE CHAIN ARTIFICIAL INTELLIGENCE LEVERAGING DIGITAL TWINS;” and Ser. No. 63/087,292, filed Oct. 4, 2020, entitled “EXECUTIVE CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM FOR VALUE CHAIN NETWORK.” Each of the above applications is hereby incorporated by reference in its entirety as if fully set forth herein. FIELD 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. BACKGROUND Historically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were been 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. The product 130 was passed through the supply chain, being conveyed and stored via various hauling facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112 and delivery systems 114, such as trucks and other vehicles, trains, and the like. In many cases, maritime facilities and infrastructure, such as ships, barges, docks and ports provided transport over waterways between the points of origin 102 and one or more destinations 104. Organizations have access to an almost unlimited amount of data. With the advent of smart connected devices, wearable technologies, the Internet of Things (IoT), and the like, the amount of data available to an organization that is planning, overseeing, managing and operating a value chain network has increased dramatically and will likely to continue to do so. For example, in a manufacturing facility, warehouse, campus, or other operating environment, there may be hundreds to thousands of IoT sensors that provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors that can track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many others. Additionally, as wearable technologies become more prevalent, wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers. Furthermore, as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets. The presence of more data and data of new types offers many opportunities for organizations to achieve competitive advantages; however, it also presents problem