{"id":104268,"date":"2024-06-15T12:00:00","date_gmt":"2024-06-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=104268"},"modified":"2025-02-04T18:56:44","modified_gmt":"2025-02-04T17:56:44","slug":"digital-twin-and-vertical-integration","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/digital-twin-and-vertical-integration\/","title":{"rendered":"Digital Twin and Vertical Integration"},"content":{"rendered":"\n

Horizontal integration in the sense of networking between individual components of manufacturing companies serves, among other things, to automate processes based on semantically similar aggregated data models [1]. Machines can communicate and interact with each other and thus adapt and react to changing conditions. Effective communication requires the standardized use of protocols and the creation of platform- and language-independent interfaces. The requirements for standards, both in terms of protocols and the underlying data models, have increased significantly in recent years.<\/p>\n\n\n\n

Vertical integration, on the other hand, involves the integration of machine data into higher-level company processes, mapped in the digital company twin. Efficient vertical integration is intended to support companies in achieving sustainability goals, among other things [2]. In the future, this will not only be a competitive factor for companies and corporate alliances, but also mandatory. In recent years, the consideration of sustainability indicators has increasingly become the focus of political actors both within Germany and at the European level.<\/p>\n\n\n\n

In Germany, the Supply Chain Act was passed in summer 2021 and came into force at the beginning of 2023. The obligations of companies to comply with human rights and environmental due diligence obligations in their supply chains as enshrined in the law relate not only to the company\u2019s own business activities, but also to those of their direct suppliers. The planned EU directive is even more comprehensive in its requirements, as it includes the entire value chain, not just direct suppliers [3].<\/p>\n\n\n\n

In order to be able to make valid statements here, it is necessary to carry out appropriate planning and advance simulation of consumption, e.g. using tools such as the Teamcenter Carbon Footprint Calculator [4], while also making emissions and consumption levels transparent across all life cycle phases of industrial plants [5]. This approach can be supported by digital product and production passports, for example. The latter in particular should enable statements to be made about possible savings and improvement potential. To this end, data from the field must be collected, vertically integrated, analyzed and, depending on the use case, made available in a condensed form via the process twin.<\/p>\n\n\n\n

The asset administration shell as part of the digital ‘resource’ twin<\/h2>\n\n\n\n

As has been published many times [1, 6], a digital twin is a virtual image of an ‘object’. In this context, ‘objects’ can include processes, products, assets or even services. In relation to a product or asset twin, data from the early phases of the life cycle \u2013 from the initial requirements through to the design and planning, e.g. in the digital factory environment, business data such as supplier information, and finally sensor and context data \u2013 are included. This means that a digital twin can exist before its ‘real’ twin. Another important sustainability component is added due to the requirements for ‘green automation’.<\/p>\n\n\n\n

The ability to collect and provide emissions KPIs such as energy consumption figures over the life cycle will be a key requirement for digital twins in the future. This requires cross-manufacturer access to feed in and exchange data. Only horizontally and vertically integrated data that can be semantically analyzed by all parties involved (Fig. 1) will make it possible to implement traceability and quality management under increased sustainability requirements.<\/p>\n\n\n\n

\n

Asset administration shells, as frequently discussed and as explained in more detail in [5], must take these additional requirements into account. As part of the digital twin, they offer a standardized, IT-based implementation of a digital lifecycle file for products and production resources. Asset administration shells (AAS) consist of several sub-models, each of which covers one aspect of an ‘asset’ [7].<\/p>\n<\/div>\n\n\n\n

\"Example
Figure 1: Example architecture for vertical integration [10], modified.<\/em><\/figcaption><\/figure>\n\n\n\n

Example of cooperative robots<\/h2>\n\n\n\n

The Digital Factory Technology Lab at the HTW provides an example of the vertical integration of machine data, whereby data from the two existing Panda robot arms from the company Franka Emika is transferred to the Siemens Insights Hub platform, evaluated and visualized via dashboards throughout the operating time. The underlying objective is to make energy consumption transparent and to implement predictive maintenance scenarios by recording joint positions, speeds and acceleration parameters, torque and the operating and error status. An operational online interface must be implemented in order to record data during the operating time.<\/p>\n\n\n\n

In the first prototype, robot data was connected to the Insights Hub cloud via an Open Platform Communications Unified Architecture (OPC UA) server [8]. For this purpose, a data model was first designed that represents the data and functionalities of the Franka robots in the OPC UA standard. The OPC UA Robotics Companion Specification, a standardized information model for robots [9], was used, making it easier to implement in other environments that also use OPC UA Robotics.<\/p>\n\n\n\n

Communication with the robots takes place using a manufacturer-specific C++ library, which provides functions for data retrieval, parameterization and control. This data and functionality is propagated via the OPC UA server for further processing in accordance with the developed data model.<\/p>\n\n\n\n

This OPC UA server is implemented using the OPC UA SDK and the data model is implemented using the UA modeler from Unified Automation GmbH.<\/p>\n\n\n\n

The selected data was modeled in the Insights Hub as templates, referred to here as aspects. These aspects can be used to instantiate individual real Franka robot assets as digital twins and can also be extended to other cooperative robots. The integration architecture, including the edge device, is shown in Figure 1. The data points from the OPC UA data model can be mapped and connected to the data of the digital twin in the Insights Hub cloud via an edge device.<\/p>\n\n\n\n

Via the angular velocities and torques for each joint, which were recorded directly by the robot controller via ROS (Robot Operation System) and the manufacturer-specific API at millisecond intervals, the sum of the work performed and thus the energy converted can be determined. The power used is multiplied by the operating time, which enables the power consumption to be derived in kWh. This data is then sent to the Insights hub cloud via the OPC UA standard described above.<\/p>\n\n\n\n

Here, the values for each joint per robot are collected, calculated, stored and monitored on the digital asset twin. These values form the basis for predictive analyses, among other things.<\/p>\n\n\n\n

\"Extension
Figure 2: Extension of the energy efficiency sub-model of the administration shell.<\/em><\/figcaption><\/figure>\n\n\n\n

Expanding the asset administration shell to include sustainability information<\/h2>\n\n\n\n

The data collected in the cloud for both robots, due to the joint movements combined with additional acceleration and deterioration data enables an overview of the current energy consumption, predictive maintenance and real-time control of the production process. In order to make this data available for further processing in a standardized way via the digital life cycle file of the robots, the next step is to connect an extended administration shell, as shown in Figure 2. Two sub-models are used for this, the “asset environment” and the “MES connection” [7]. The latter is supplemented by the “energy efficiency” sub-model.<\/p>\n\n\n\n

In order to be able to utilize the real-time data from the real twins, the “energy efficiency” model is expanded to include the “electricity consumption” sub-model. This directly accesses the data already described in the extended companion specification and made available via the OPC UA. A reference to this operational online interface is stored in the administration shell both for the MES connection to determine the operating time and production status and to provide the current torque, among other things. This can be done via HTTP or MQTT, depending on the desired communication model.<\/p>\n\n\n\n

Life cycle of a digital twin<\/h2>\n\n\n\n

One of the many challenges continues to lie in answering the questions of what data needs to be managed, and how and where this should occur. The answer depends not least on the focused business models. On the one hand, sustainability is simply required by the regulations mentioned, on the other hand it can become part of new business models. In order to be able to collect and accumulate data on the life cycle of ‘objects’, the data must, as mentioned, be collected along the entire value chain. To facilitate end-to-end collection here, the asset administration shell and OPC UA must be brought together.<\/p>\n\n\n\n

The approach shown in Figure 3 represents a concept for harmonizing both standards based on [11]. A uniform description of an ‘object’ in the sense of a ‘green’ life cycle file is required. This file is part of the digital twin and is enriched with real-time data during the operating phase. This requires both the descriptive interface assigned to the asset administration shell and an operational online interface as defined and implemented in OPC UA. In order to establish a “connected world” network, all content that can be assigned to the manufactured product over the entire life cycle, e.g. a digital type plate or the CO2 footprint, must be recorded and communicated.<\/p>\n\n\n\n

\""Connected
Figure 3: “Connected World” across the value chain.<\/em><\/figcaption><\/figure>\n\n\n\n

Where the data is collected also depends on various factors. Whether in a PLM system or on an IIoT platform such as Insights Hub, the ability to access and control the digital twin on-demand is crucial. It must be possible to access engineering data in the form of an engineering bill of materials (EBOM) and assign it to an individualized twin \u2013 the “instance”. Solutions for this are already offered in the asset administration shell. For example, links to the master model data can be stored in a PLM system and retrieved from there.<\/p>\n\n\n\n

In addition to making the current status transparent, the real-time data collected during runtime can be used to compare simulation results that were previously generated via the digital (production) twin in the planning phase. As suggested in Figure 3, the OPC UA can not only send data to its referenced asset administration shell (and via this to the cloud), but can also make it available to the simulation model, e.g. the Siemens Mechatronics Concept Designer (Fig. 3), for updating and ‘look ahead’ simulation.<\/p>\n\n\n\n

These simulations make it possible to determine the effects, e.g. on energy consumption or the quality of the product to be manufactured, in advance and thus avoid them by initiating ad hoc countermeasures if necessary. If these generated results are stored in PLM systems, for example, the information can also be ‘frontloaded’, which in turn improves the planning basis.<\/p>\n

Bibliography <\/h2>[1] Dietrich, U.: Digitaler Zwilling als Schl\u00fcsselkomponente f\u00fcr die Automatisierung von Systemen. In: Digitaler Zwilling in der Automatisierungstechnik, Automation Forum 2022, FH Westk\u00fcste.\r
[2] Mazak-Huemer, A. et al.: Rahmenwerk zur modellbasierten horizontalen und vertikalen Integration von Standards f\u00fcr Industrie 4.0. In: Handbook Industry 4.0. Berlin, Heidelberg (2020).\r
[3] Fritsch, M.; Zink, B.: CO2-Fu\u00dfabdruck in Lieferketten: Studie f\u00fcr den Verein ECLASS, 31.10.2022, iWCONSULT, URL: www.iwconsult.de\/projekte\/co2-fussabdruck-in-lieferketten\/, Accessed 10.11.2023.\r
[4] Siemens: Teamcenter Carbon Footprint Calculator, URL: www.plm.automation.siemens.com\/media\/global\/en\/Product-Carbon-Footprint-Calculator-Fact-Sheet_tcm27-107797.pdf, Accessed 14.10.2023.\r
[5] IHK Munich: Leitfaden zur Nachhaltigkeitsberichterstattung: In zehn Schritten zur CSRD, URL: www.ihk-muenchen.de\/de\/Service\/Nachhaltigkeit-CSR\/Nachhaltigkeitsberichterstattung\/, Accessed 23.03.2024\r
[6] Grieves, M.: Digital Twin: Manufacturing Excellence Through Virtual Factory Replication, URL: innovate.fit.edu\/plm\/documents\/doc_mgr\/912\/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf, White paper, 2014, Accessed 20.09.2016.\r
[7] Plattform Industrie 4.0, Spezifikationen der Verwaltungsschale, URL: www.plattform-i40.de\/IP\/Redaktion\/DE\/Standardartikel\/spezifikation-verwaltungsschale.html, Accessed 10.10.2023.\r
[8] Dodrimong, L.N.: Entwicklung eines OPC UA Servers f\u00fcr den Franka Emika Roboter mit digitalem Abbild in der MindSphere Cloud. Berlin University of Applied Sciences (2021).\r
[9] OPC Foundation: OPC 40010-1: Robotics – Vertical Integration, URL: reference.opcfoundation.org\/v104\/Robotics\/v100, Accessed 20. 09. 2023.\r
[10] Dietrich, U.; Dodrimong, L.: Vertikale Integration im Kontext von Industrie 4.0. In: Designing Futures: Zuk\u00fcnfte gestalten. Informatics. GI Informatik Festival – September 2023, GI conference proceedings 337, pp. 289-293 (2023).\r
[11] Drath, R. et al.: Discussion paper \u2013 Interoperabilit\u00e4t mit der Verwaltungsschale, OPC UA und AutomationsML. Document version 5.3, 11.04.2023, URL: opcfoundation.org\/ Accessed 17.10.2923.<\/div>
Tags: Asset administration shell<\/a><\/span> digital product passport<\/a><\/span> digital twin<\/a><\/span> digitaler Zwilling<\/a><\/span> digitaler Zwilling<\/a><\/span> transparency in sustainability<\/a><\/span> vertical integration<\/a><\/span> Verwaltungsschale<\/a><\/span> Verwaltungsschale<\/a><\/span> <\/div>
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Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Jan Schickram<\/a>, Tareq Albeesh<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Empathic Assembly Assistance<\/h4>\n
Combining AI-based data analysis and empathic human digital twins<\/div>
Matthias L\u00fcck<\/a> \n \"ORCID<\/a>, Katharina H\u00f6lzle<\/a> \n \"ORCID<\/a>, Christian Saba-Gayoso<\/a> \n \"ORCID<\/a>, Joachim Lentes<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial companies in Germany face demographic change and stagnating productivity in an increasingly complex world. Manual assembly remains essential for complex, low-volume products, yet productivity and quality lag due to human variability. This paper introduces a concept and demonstrator for an empathic assembly assistance system that merges a human digital twin and AI-based screwdriver data analytics within a modular architecture. Tightening anomalies are classified, linked to inferred worker states and translated into information and recommendations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 6-13 | DOI 10.30844\/I4SE.25.5.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Enabler for the Digital Twin<\/h4>\n
Requirements for Technical Documentation 4.0<\/div>
Christian Koch<\/a>, Lukas Schulte<\/a>, Ren\u00e9 W\u00f6stmann<\/a>, Jochen Deuse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 4 | Pages 76-85<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO\u2082 footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Enabling\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Enabling the Future of Manufacturing with Digital Twins<\/h4>\n
Opportunities and obstacles<\/div>
Javad Ghofrani<\/a> \n \"ORCID<\/a>, Darian Lemke<\/a>, Tassilo S\u00f6ldner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 72-81<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
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Digital Supply Chain Twin: The Pathway to Success<\/h4>\n
A catalyst for increasing competitiveness <\/div>
G\u00f6khan Cenk<\/a> \n \"ORCID<\/a>, Jonas Andersson<\/a>, Tobias Engel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Companies face a variety of challenges when optimizing global supply chains. Economic interests must be balanced with legal requirements, such as the German Supply Chain Due Diligence Act (SCDDA) and the European Sustainability Reporting Standards (ESRS). A digital supply chain twin (DSCT) enables the visualization of value creation networks and supports key business functions, such as purchasing, supply chain management, distribution, service, and sales. By leveraging immersive technologies, the DSCT helps generate sustainable competitive advantages across the entire supply network. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 52-60 | DOI 10.30844\/I4SE.25.3.52<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The implementation of \u201csmart\u201d production processes that focus on sustainability requires a great deal of communication at various levels based on differently aggregated data. The networking of objects, which is required by cyber-physical systems in the context of Industry 4.0, leads to both horizontal and vertical integration requirements. Vertical integration also forms an important basis for helping companies to achieve their sustainability goals in an integrated production environment. Vertical integration also enables companies to use resources more efficiently, minimize waste, reduce environmental impact and ultimately promote more sustainable business practices. This paper will use the example of cooperative robots in the Digital Factory Technology Lab at HTW Berlin to explore vertical integration for implementing sustainability-oriented production and will present a prototype implementation.<\/p>\n","protected":false},"featured_media":107542,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[76809,78956,75884,75880,79631,79555,68000,79636,76808],"product_cat":[],"topic":[67838],"technology":[],"knowhow":[],"industry":[],"writer":[83748],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":{"0":"post-104268","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"category-design-en","7":"category-translate-en","8":"category-typeset","9":"tag-asset-administration-shell","10":"tag-digital-product-passport","11":"tag-digital-twin","12":"tag-digitaler-zwilling","13":"tag-digitaler-zwilling-en","14":"tag-transparency-in-sustainability","15":"tag-vertical-integration","16":"tag-verwaltungsschale-en","17":"tag-verwaltungsschale","18":"topic-digital-twin","19":"writer-ute-dietrich-en","20":"product","21":"first","22":"instock","23":"downloadable","24":"virtual","25":"sold-individually","26":"taxable","27":"purchasable","28":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/06\/Dietrich-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"The implementation of \u201csmart\u201d production processes that focus on sustainability requires a great deal of communication at various levels based on differently aggregated data. The networking of objects, which is required by cyber-physical systems in the context of Industry 4.0, leads to both horizontal and vertical integration requirements. Vertical integration also forms an important basis…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/104268","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/107542"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=104268"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=104268"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=104268"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=104268"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=104268"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=104268"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=104268"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=104268"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=104268"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=104268"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=104268"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=104268"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=104268"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}