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Industrie 4.0 ist in Politik, Medien, Wissenschaft und Wirtschaft derzeit omnipräsent. Intelligenter, individueller, effizienter, schneller, vernetzter – so lauten nur einige Versprechen dieses neuen industriellen Zeitalters. Tatsächlich sind die Potenziale gerade für den deutschen Maschinen- und Anlagenbau gewaltig: Sowohl für Anbieter als auch für Anwender von Technologien rund um das Thema Industrie 4.0. Aber noch existieren viele ungelöste Fragen, Unsicherheiten und Aufgaben. Hier wollen wir mit unserer Readiness-Studie ansetzen und Hilfestellung leisten. Denn ein Selbstläufer wird Industrie 4.0 nicht. Mit der vorliegenden Studie soll die große Vision näher an die betriebliche Realität gebracht werden. Auch zeigen wir die anspruchsvollen Wegmarken auf, die für viele Unternehmen hinsichtlich ihrer Industrie 4.0-Fähigkeit noch zu passieren sind. Die Studie untersucht, an welcher Stelle der Maschinen- und Anlagenbau aktuell bei der Umsetzung steht. Motivation und Hemmnisse der Unternehmen werden ebenso in den Blick genommen wie die Unterschiede, die sich zwischen Mittelstand und großen Unternehmen ergeben. Im Ergebnis ist es erstmals möglich, die „Industrie 4.0-Readiness“ der Maschinenbau-Industrie detailliert und systematisch abzubilden.
Industrie 4.0 is all around us today: in politics, in the media, and on the agendas of researchers and entrepreneurs. Smarter, faster, more personalized, more efficient, more integrated – those are just some of the promises of this new industrial era. The potential, especially for Germany ́s mechanical
engineering industry and plant engineering sector, is indeed great, both for providers and for users of technologies across the spectrum of Industrie 4.0.
But there are still many unresolved questions, uncertainties, and challenges. Our readiness study seeks to address this need and offer insight. Because Industrie 4.0 will not happen on its own.
This study is intended to bring the grand vision closer to the business reality. We also highlight the challenging milestones that many companies must still pass on the road to Industrie 4.0 readiness.
The study examines where companies in the fields of mechanical and plant engineering currently stand, focusing on what motivates them and what holds them back, and on the differences that emerge between small and medium enterprises on the one hand and large enterprises on the other.
The results make it possible for the first time to develop a detailed, systematic picture of Industrie 4.0 readiness in the engineering sector.
The study concludes with recommendations for action in the business community, complementing the diverse suite of programs and activities offered by VDMA’s Forum Industrie 4.0. We would like to take this opportunity to thank the two sponsors of this project from the VDMA Forum, Dietmar Goericke and Dr. Christian Mosch, whose efforts played a critical role in making this study a success.
We are convinced that Industrie 4.0 can become a success story for Germany’s engineering sector. May our “Industrie 4.0 Readiness” study do its part in this effort.
Erfolgreiche Serviceinnovation im Zeitalter industrieller, datenbasierter Dienstleistungen unterscheidet sich deutlich von bisherigen Ansätzen der klassischen Dienstleistungsentwicklung. Diese Erkenntnis konnte aus einem breit angelegten Benchmarking in der deutschen Industrie gewonnen werden. Die Benchmarking-Studie identifizierte besonders erfolgreiche Unternehmen, deren Methoden und Ansätze zur Gestaltung innovativer Dienstleistungen in Form von Fallstudien im Detail untersucht wurden. Als Kernergebnis ergeben sich sechs Prinzipien, die erfolgreiche Serviceinnovation für datenbasierte Dienstleistungen auszeichnen.
Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly complex and diverse production processes. Thus, the susceptibility to disruptions in manufacturing rises, turning the task of Production Planning and Control (PPC) into a complex, dynamic and multidimensional problem. Addressing PPC challenges such as disruption management in an efficient and timely manner requires a high level of manual human intervention. In times of digitization and Industry 4.0, companies strive to find ways to guide their workers in this process of disruption management or automate it to eliminate human intervention altogether. This paper presents one possible application of Machine Learning (ML) in disruption management on a real-life use case in mixed model continuous production, specifically in the final assembly. The aim is to ensure high-quality online decision support for PPC tasks. This paper will therefore discuss the use of ML to anticipate production disruptions, solutions to efficiently highlight and convey the relevant information, as well as the generation of possible reaction strategies. Additionally, the necessary preparatory work and fundamentals are covered in the discussion, providing guidelines for production companies towards consistent and efficient disruption management.
Smart Service Engineering
(2019)
In our digitalized economy, many traditional service engineering models lack flexibility, efficiency and adaptability. As today’s market differs significantly from the market of the late 20th century, service engineering models must meet different requirements today than they had to meet in the past. The present paper starts off by providing an overview of the requirements that modern service engineering models need to fulfill in order to succeed in today’s economic environment. Afterwards, three promising models that meet several of these requirements will be introduced.
Nowadays, cyber physical systems support the improvement of efficiency in intralogistics by controlling and manipulating the production and logistic environment autonomously. Due to the complexity of the individual production processes, designing suitable cyber-physical systems based on their existing production environment is a challenge for companies.
This paper presents a new methodology on how to design cyber-physical systems conceptually to suit an individual production environment. Compared to existing design approaches, this methodology matches immediately the required functions to existing information and communication technology’s components insisting on the neutral assimilation of requirements.
Therefore, the requirement specification asks for needed functions in relating to offered functions of information and communication technology (ICT) components. The paper focusses the use case of implementing a cutting-edge mobile network technology into an existing tracking and tracing process.
Manufacturing companies are constantly increasing their efforts in the subscription business, also known as product-as-a-service business, offering usage and outcome based solutions (value-in-use) instead of transactional services and products (value-in-exchange). Customers are becoming contractual subscribers of the solution in return for recurring, performance-related payments. To address arising, inevitable challenges like (1) reducing customer churn, (2) increasing usage intensity and outcome quality, (3) ensuring the adoption of product and software releases as well as (4) fostering customer loyalty, leading manufacturing companies are setting up a new organizational, customer-facing unit, called Customer Success Management (CSM). This unit has its origins in the software-as-a-service business, operating next to established entities like sales, key account management and customer service. Since there are currently no holistic models for an end-to-end description of CSM-tasks in the manufacturing industry, this paper contributes to a taskoriented reference model, using a grounded theory approach, examining both manufacturing and software companies. Containing a reference framework with 8 main tasks, 17 basic tasks and 76 elementary tasks, the reference model supports manufacturing companies in adapting and customizing a company-specific CSM concept.
Electricity generated by wind turbines (WT) is a pillar of the transition to renewable energy [1]. In order to economically utilize WTs, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WTs, are a focus of cost reduction activities [2]. A prescriptive maintenance approach can support in achieving this goal. Prescriptive maintenance is a maintenance approach, where asset condition data is collected and analyzed to recommend specific actions to prevent breakdowns and reduce downtimes. However, the processing and analysis of data is quite complex. Especially unstructured data (such as comments of service technicians in free text fields) is often left unused, as companies, mostly SMEs lack the capacity to carry out these analyses. In this work we propose an approach to utilize the information from service reports, maintenance reports as well as status records from SCADA systems for the development of a prescriptive maintenance approach to onshore WTs. To achieve this, an ontology was utilized in this approach to codify implicit knowledge of service technicians and aid in making unstructured data usable for further analysis. The ontology was used to link historical service and maintenance reports with status codes, thus enabling automated analysis. In interviews with WT topic experts and through further research, damage mechanisms and corresponding maintenance measures were identified and a measure catalogue was developed to support service and maintenance activities. The recognition of the root cause of problems allows for a prescriptive maintenance approach that recommends targeted actions to reduce downtimes and optimize maintenance activities, it also allows to effectively control the outcome of maintenance activities and optimize their execution.
Pricing for Smart-Product-Service-Systems in Subscription Business Models for Production Industries
(2021)
In the production industry, subscription business models have the potential to create long-term relationships where a supplier provides a continuous value-oriented service to a customer based on digitalisation. Monetising this increase in value through pricing represents a central challenge for suppliers in subscription business. Unlike the current dominant transactional business, the focus of pricing is on the value-in-use of the customer (e.g. on the increase in output for the customer). In this regard, there is so far no pricing approach for practice that allows the linking of the performance data of the customer with the periodically charged price. However, in subscription businesses, such an approach is required to create win-win situations for the customer and supplier through continuous performance improvement. Therefore, this paper develops a novel process model for pricing of smart-product-service-systems in subscription business for production industries. This process can serve as basis for suppliers of subscriptions in the production industry to align pricing with the created value-in-use. In the long term, this allows companies to systematically develop their pricing to monetise the potential of digitalisation.
The Impact Of Manufacturing Execution Systems On The Digital Transformation Of Production Systems
(2021)
With the focus of manufacturing companies on the digital transformation, Manufacturing Execution Systems are market-ready, modular software solutions for manufacturing companies to integrate the value-adding and supporting processes horizontal and vertical in the company. Companies, especially small and mediumsized companies, face high internal and external costs for the implementation of the MES modules. An advantage of MES is the possibility to implement the systems in a continually, module-by-module approach, with the benefit of timely distributed investments. By realizing fast improvements, companies can use the benefits for further module implementations. This paper proposes a maturity model to measure the impact of an MES on the digital transformation of the company’s production systems. The model fulfils two purposes. The first, companies can measure the impact based on the difference between its current maturity index and the potential index of an implemented MES. The second is, the user can identify what impact an MES has in general on the digital transformation since the developed maturity model is derived from an established industry 4.0 maturity model. The development of the maturity model is based on the methodologies of AKKASOGLU and focuses on the further development of an established model. As an outlook, the application of the model will be described briefly. The proposed maturity model can directly be used by practitioners and offers implications for further development of MES functionalities.
Industrial practice shows a strong trend towards digitalization. It is not only economic crises, such as those triggered by Covid-19, that are reinforcing this trend. It is also the entrepreneurial urge to fulfill customer wishes in the best possible way and to adapt to new requirements as quickly as possible. Due to the advancing digitalization, the role of business application systems in manufacturing companies is therefore becoming increasingly important. The data processed in IT-Systems represent a great potential, especially for the evaluation of change requests in production. Through efficient change management, companies can record and process changes quickly. However, the necessary data basis to decide on existing change requests is still hardly used. Existing IT-Systems for change management coordinate the processing of change requests, but do not relate to data of operational application systems such as Enterprise-Resource-Planning. Therefore, a conceptual approach is required for the evaluation of change requests. This approach is based on an objective recording system that enables the transformation from the change description to an evaluation space. The paper presents an approach for the systematic transfer of requirement characteristics into the world of operational IT-Systems.
The do-it-yourself mentality is particularly widespread in the furniture sector. Homemade furniture is very popular. The individualisation of furniture can be observed in internet forums, such as the online platform Pinterest. These creative ideas of potential customers show a need for individualized sustainable pieces of furniture. The current production structures, however, do not allow individual production according to the end customer's specifications. In addition, information logistics faces a major challenge: making the creative ideas of end consumers available to producers in parametric form. Topics such as customer requirements in relation to sustainable production, material specifications, industrial property rights, fair production conditions and traceability are the focus of this data interchange. An open and innovative European furniture ecosystem must be created to connect all stakeholders in the production process. This is made possible by a platform that channels the creativity of consumers and makes it designable and producible through the professional skills of designers. This requires the involvement of manufacturing specialists who can produce personalised products through sustainable intelligent production technologies. An exchange of information must also take place securely and quickly in order to protect the personal rights of the sources of ideas. This is being developed in the EU research project INEDIT - Open Innovation Ecosystem for do-it-together process. By connecting many different stakeholders along the entire value creation process, a change towards efficient collaborative collaboration is achieved. This paper presents a project insight for the development of an international co-creation platform by presenting the problem and linking it to a potential solution.
High Resolution Supply Chain Management aims to counteract the trend towards more and more centralised and rigid enterprises. Today, most companies strive to increase efficiency of business processes applying highly sophisticated, centralised planning approaches. These centralised approaches limit the companies’ ability to react flexibly and act adaptively due to external and internal turbulences. In today’s buyer’s markets companies usually try to bypass these turbulences keeping high levels of inventory resulting in a low overall efficiency. High Resolution Supply Chain Management tries to solve the problem at its root from a holistic perspective. Based on the Viable System Model developed by Stafford Beer a four-dimensional holistic production management system model, embedding an organisational structure view, an cause and action view, a control loop perspective and a decision making level has been elaborated. The basis of this model is the integration of all four perspectives into an interacting framework.
Manufacturing companies (MFRs) are increasingly extending their
portfolios with services and data-driven services (DDS) to differentiate themselves from competitors, tap new revenue potential, and gain competitive advantages through digitization and the subsequently generated data. Nonetheless, DDS fail more often than traditional industrial services and products within the first year on the market. Particularly, companies are failing to sell DDS successfully and efficiently with their existing (multi-level) distribution structures. Surprisingly, there is a lack of scientific research addressing this issue. Since there are currently no holistic models for an end-to-end description of distribution-tasks for DDS in the manufacturing industry, this paper contributes to a task-oriented reference model for mapping interactions in the multi-level distribution management. Therefore, a case study research approach is used, to identify and describe the interactions in the multi-level distribution management of DDS, as well as to develop a regulatory framework for MFRs and their multi-level distribution management. This research uses the established theoretical framework of Service-Dominant-Logic to address the co-creation in multi-level distribution management of DDS. As a result, this paper identifies different interaction variants as well as the need for a new management function with 4 main and 14 basic tasks.
Trends und Entwicklungen
(2020)
Der traditionelle After Sales Service inklusive der hohen Margen und Gewinnbeiträge steht einem Wandel gegenüber. Digitale Transformation, Globalisierung oder disruptive Geschäftsmodelle verändern die etablierten Rahmenbedingungen des Geschäftsbereiches zunehmend. Daher sollten sich Unternehmen auf diesen Wandel vorbereiten und Veränderungen in der eigenen Organisation anstoßen. Aus diesem Grund wird in dem letzten Kapitel des vorliegenden Buches auf wichtige Trends im After Sales Service eingegangen. Zu nennen sind hierbei der Ansatz der Servitization (Abschn. 7.1), die Digitale Transformation im After Sales Service (Abschn. 7.2), digitale Geschäftsmodelle (Abschn. 7.3), das Smart Service Engineering (Abschn. 7.4) oder der Einfluss der Elektromobilität auf den automobilen After Sales Service (Abschn. 7.5).
Ziel des Forschungsvorhabens war die Erhöhung der Effizienz und Effektivität von Suchanfragen in ERP-Systemen. Dabei sollte der Aufwand für den Nutzer reduziert und die Qualität der Ergebnisse verbessert werden. Die Erreichung der Ziele wurde durch die Entwicklung einer selbstlernenden, kontextbasierten Suchmaschine für ERP-Systeme realisiert. Mit der Berücksichtigung des Kontexts einer Suchanfrage, des Benutzerverhaltens und einer Ergebnisbewertung durch den Anwender wurde die Ergebnisqualität von Suchanfragen kontinuierlich gesteigert. Durch die Entwicklung eines Demonstrators wurde der Nutzen des Konzepts nachgewiesen, indem dieser in verschiedenen Szenarien erprobt und anhand einer Wirtschaftlichkeitsbetrachtung bewertet wurde.
Die Finanzkrise hat viele Unternehmen kalt erwischt. Die Folgen des Konjunktureinbruchs sind drastische Absatzeinbrüche, Produktionsstopps und Liquiditätsengpässe. Die Tendenz zur Wertminderung der Produkte zwingt vor allem Industrieunternehmen, Abschreibungen auf ihre Bestände vorzunehmen. Dies gilt insbesondere für die äusserst dynamischen und hochwertigen Lagerbestände im Automobilbau, Maschinenbau oder der chemischen Industrie.
High Resolution Supply Chain Management (HRSCM) aims to stop the trend of continuously increasing planning complexity. Today, companies in high-wage countries mostly strive for further optimization of their processes with sophisticated, capital-intensive planning approaches. The capability to adapt flexibly to dynamically changing conditions is limited by the inflexible and centralized planning logic. Thus, flexibility is reached currently by expensive inventory stocks and overcapacities in order to cope with rescheduling of supply or delivery. HRSCM describes the establishment of a complete information transparency in supply chains with the goal of assuring the availability of goods through decentralized, self-optimizing control loops for Production Planning and Control (PPC). HRSCM pursues the idea of enabling organization structures and processes to adapt to dynamic conditions. The approach includes the strengths of the existing planning models as well as the process of decision making in organizations. A precondition for this decentralized adaptation is the synchronization of the objectives of the several units or process owners. The basis for this new PPC Model are information transparency, stable processes, consistent customer orientation, increased capacity flexibility and the understanding of the production system as a viable, socio-technical system.
Analysis of the Harmonizing Potential of Order Processing Attributes in Spread Production Systems
(2010)
The paper discusses an approach how to measure the competitive advantage of harmonized order processing data by making use of knowledge about the interdependencies between related benefit dimensions. Corresponding harmonization projects are all projects that strive for common structures in product attributes, classification systems or product structures. The main objective of the underlying research work is the development of a method for the estimation of the benefit potential of harmonized order processing data.
Viable Production System for adaptable and flexible production planning and control processes
(2009)
High Resolution Supply Chain Management (HRSCM) aims at designing adaptable and flexible production planning and control (PPC) processes according to the needs of the company’s supply chain environment. To reach this goal a model for a Viable Production System (VPS) has been elaborated and is presented in this paper. Based on the Viable System Model (VSM) developed by Stafford Beer current production systems are analyzed in terms of integrity. With the gained knowledge a complete recursive framework of a VPS is developed. The framework allows the design of a decentralized production system that meets all requirements of a dynamic environment. Flexible and adaptable PPC processes can be developed for each identified subsystem of the VPS. Hence, further research focuses on the development of process and control loops in order to assure the application of the framework. Exemplarily the decentralised control loop for inventory management is elaborated in a case study.
Supply Chain Management delivers a considerable amount of ideas and methods to design the value stream. Each of these concepts may lead to significant cost reduction and higher service levels. But the same concept does not work for different customers and their diverse needs. Thus, a “one size fits it all” supply chain cannot lead to success. The key to overcome this obstacle is the hybrid supply chain. This paper outlines the application of hybrid system theory to supply chains. After a comprehensive overview of existing methods for the design of supply chains is given, a methodology for a customer-to-customer oriented supply chain design is presented. This approach adopts the hybrid system theory to supply chains which is in a nutshell that hybrid systems use the advantages of its subsystems to reach a superior result to one system alone. Concluding a case study illustrates the application of the methodology.
Increasing the energy efficiency and meanwhile avoiding unplanned maintenance breaks are keys for manufacturing companies to stay competitive in the future. This paper presents an energy saving and maintenance cost reducing approach for manufacturing environments. The approach describes first occurring types of energy wastage within manufacturing and characterizes them in more detail. Including additional external information, the significance of an identified on-going wastage can be determined. Based on the type of wastage and the significance; concrete recommendations for measures to prevent the wastage are delivered. The identified wastage facilitates detecting inefficient operating mode as well as wearing and malfunctioning at machines. By using complex event processing technologies realtime information can forwarded directly to the responsible persons to enable quick reactions to prevent energy wastage and unplanned downtimes. The paper presents an approach to identify detection and propose concepts for manufacturing enterprises. The information processing procedure is used for the implementation of two Use Cases.
Distributionslogistik
(2013)
Die Umgebung von Industrie- und Handelsunternehmen hat sich in den letzten Jahren tiefgreifend verändert. Beispielhafte Auslöser waren der Wandel vom Produzenten zum Käufermarkt, der faktische Wegfall der nationalen Grenzen und die damit verbundene Intensivierung des europäischen Binnenmarktes sowie die zunehmende Bedeutung ökologischer Anforderungen. Um die Kundenbedürfnisse dennoch befriedigen zu können und damit dem Wettbewerb gewachsen zu sein, müssen sich die Distributionsstrukturen der Unternehmen immer schneller an diese Veränderungen anpassen. Nur so können die Waren flexibel, kostengünstig und schnell an die Kunden geliefert werden. In diesem Spannungsfeld kommt der Planung und Steuerung der Distributionsabläufe eine immer wichtigere Bedeutung zu.
Ziel dieses Kapitels ist nicht nur die Vermittlung grundlegender Begrifflichkeiten und Zusammenhänge der Distributionslogistik, sondern weiterhin auch Methoden zur Distributionsplanung und steuerung sowie Kennzahlen zur Messung der Distributionsleistung und -kosten.
Aufgrund kürzer werdender Produktzyklen und steigender Produktvielfalt werden produzierende Unternehmen mit einer zunehmenden Anzahl von Produktanläufen konfrontiert. Ziel aktueller Forschungsaktivitäten ist es daher, anlaufintensive Unternehmen zu befähigen, verlässliche Produktionsprogramme in kurzer Zeit zu erstellen. Lerneffekte sollen genutzt werden können ohne Diversifikationseffekte zu vernachlässigen. Zur Erreichung dieser Zielsetzung wird ein Modell für eine kybernetische PPP bei Produktanläufen entwickelt.
In recent years supply chain participants are increasingly suffering the effects of disturbances in transportation supply chains. Both, dynamics in consumer demands and global supply chains lead to a growth in unplanned supply chain events. These can cause from rather manageable disturbances through to complete break-downs of transportation chains, resulting in high follow-up and penalty costs.
Consequently, concepts for an efficient supply chain disturbance management are needed, preferably with a real-time identification and reaction to disturbance events. Therefore in the following paper the research results of the German research project Smart Logistic Grids with the focus on designing an integrated model for the real-time disturbance management in transportation supply networks are presented. This includes the introduction of elaborated classification models for disturbances and action patterns as well as an associated costs and performance measurement system. Finally, a procedure model for the disturbance management is presented.
Einführung
(2012)
Zusammenfassung und Ausblick
(2012)
Bemühungen um eine Verbesserung der betrieblichen Planung richten sich verstärkt auf die Planung von Terminen, Kapazitäten und Kosten aller an der Leistungserstellung beteiligten Bereiche. Ein Hilfsmittel hierzu ist im Einsatz von Planzeiten für unterschiedliche Verwendungszwecke zu sehen. Mit der vorliegenden Arbeit werden die Grundlagen für eine anforderungsgerechte zeitliche Planung mittels einer dem Aufgabenbereich des Anwender anpaßbaren Planzeitsystematik erarbeitet. Es sollen dabei insbesondere Aussagen hinsichtlich Einzel-und Gesamtgenauigkeit sowie des gezielten Eingriffs des Anwenders zur Genauigkeitssteigerung möglich werden. Dazu wurde eine mathematische Verfahrensweise aufgestellt und rechnermäßig implementiert. Um die unterschiedlichen Aufgabenstellungen des Gesamtverfahrens zu differenzieren, wurden die Komponenten Mikro-Modell, Makro-Modell und Analysemodul unterschieden. Durch Erprobung mit Daten aus einer Selbstaufschreibung im Bereich Fertigungsplanung eines Maschinenbauunternehmens wurde untersucht, welche Auswirkungen die verschiedenen Kenngrößen der Planzeiten, wie Anzahl der Datensätze, Wert der Planzeit, Verwendungshäufigkeit der einzelnen Planzeiten sowie Standardabweichung der Ist-Werte, auf die Genauigkeit der Gesamtplanzeit haben.
Data-driven transparency in end-to-end operations in real-time is seen as a key benefit of the fourth industrial revolution. In the context of a factory, it enables fast and precise diagnoses and corrections of deviations and, thus, contributes to the idea of an agile enterprise. Since a factory is a complex socio-technical system, multiple technical, organizational and cultural capabilities need
to be established and aligned. In recent studies, the underlying broad accessibility of data and corresponding analytics tools are called “data democratization”. In this study, we examine the status quo of the relevant capabilities for data democratization in the manufacturing industry.
(1) and outline the way forward.
(2) The insights are based on 259 studies on the digital maturity of factories from multiple industries and regions of the world using the acatech Industrie 4.0 Maturity Index as a framework. For this work, a subset of the data was selected.
(3) As a result, the examined factories show a lack of capabilities across all dimensions of the framework (IT systems, resources, organizational structure, culture).
(4) Thus, we conclude that the outlined implementation approach needs to comprise the technical backbone for a data pipeline as well as capability building and an organizational transformation.
Der Technologie- und Trendradar 2022 enthält die neusten Technologien und Trends des vergangenen Jahres. Im aktualisierten Radar wurden die Technologiereifegrade in den Steckbriefen neu bewertet, die Anwendungen, Potenziale und Herausforderungen der Technologien wo nötig aktualisiert und neue Technologien aufgenommen.
Der Technologie- und Trendradar 2022 enthält elf neue Steckbriefe. Das Technologiefeld Vernetzung wurde um Eventgetriebene IT-Architekturen, Internet of Behaviors und Web3 erweitert. Dem Feld Virtualisierung wurde die Technologie Metaverse hinzugefügt. Das Technologiefeld Datenverarbeitung wurde um den Trend Data-Centric AI ergänzt, das Feld Prozesse um den Trend Digitale Souveränität. Im Technologiefeld Produkte wurden die Technologien Edge AI, Inter Planetary File System (IPFS), Photonische Siliziumchips, Soft-Robotik und Neuromorphic Computing aufgenommen.