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Dieses Kapitel vertieft das Konzept der dezentralen technischen Intelligenz (DTI) und ihre Rolle bei der Revolutionierung der vernetzten Produktion. Es zeigt, wie DTI fortschrittliche Technologien wie KI, maschinelles Lernen und IoT integriert, um autonome, kollaborative Systeme zu schaffen, die Produktivität, Qualität und Nachhaltigkeit steigern. Das Kapitel stellt das europäische Projekt knowlEdge vor, das DTI in Aktion veranschaulicht und sein Potenzial zur Optimierung von Lieferketten, zur Verbesserung der Entscheidungsfindung und zur Förderung von Innovationen aufzeigt. Darüber hinaus skizziert er einen Fahrplan für die Implementierung von DTI und betont die Bedeutung von Mensch-Maschine-Zusammenarbeit, Echtzeit-Datenaustausch und agilen Operationen. Das Kapitel schließt mit dem Hinweis auf die transformativen Auswirkungen von DTI auf die vernetzte Produktion, die Wettbewerbsfähigkeit, Widerstandsfähigkeit und Anpassungsfähigkeit angesichts sich wandelnder Marktanforderungen fördern.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
Networked production, supported by advanced logistics and supply chain processes, is crucial for companies to stay competitive and foster cooperation and integration of production resources. It replaces sequential processes with dynamic arrangements, presenting challenges like managing product variants, short life cycles, and process optimisation. Agility is vital for adapting to changes and natural disasters. Decentralised Technical Intelligence (DTI) is an approach that manages complexity and incentivises integrating new technologies in planning and manufacturing.
DTI involves distributed and autonomous intelligence embedded in interconnected systems, where humans and machines collaborate to achieve common goals. Humans bring unique skills like creativity and intuition, complementing AI’s capabilities. DTI relies on a multi-agent architecture, enabling trust, interoperability, and data sharing for better decision-making and efficiency. The EU knowlEdge project exemplifies this by providing AI solutions that are distributed, secure, standardised, and collaborative, integrating cognitive technologies, data analytics, IoT and more.
DTI’s human-centric design fosters a different quality of intelligence, leading to greater autonomy within multi-agent systems. To realise advanced networked production, a roadmap must be implemented, focusing on a vision, value promise, and development pathway. Europe can maintain its leadership in future networked production through this approach.
1 Introduction
In today’s rapidly evolving global marketplace, networked production has risen to the forefront as a critical factor in maintaining competitiveness [1]. Initially driven by the pursuit of profitability, manufacturers have strategically focused on their core competencies, outsourcing non-core functions to specialised suppliers [2]. This transformation has led to increased fragmentation within the industry, necessitating a heightened focus on collaborative digitalisation.
Integrating state-of-the-art technologies and digital platforms enables companies to build robust connections between manufacturing operations and a diverse stakeholder network with local and global reach. This interconnectedness underpins competitive advantage by promoting broader supplier-customer engagement, cost efficiencies, operational improvements and higher product standards [3]. It also enables agility, rapid response to market changes and resilience [4, 5]. Partnerships further accelerate innovation and align with technical paradigms such as collaborative design. [6]. This dynamic supports scalability through collaboration to manage peaks in demand and disruptions , while innovative management addresses increasing complexity [7] and contests traditional hierarchical norms.
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In meeting these challenges, future-oriented companies adopt advanced technologies like AI and data analytics to devise new management strategies [8]. Decentralised Technical Intelligence (DTI) emerges as a transformative solution, merging human and machine intelligence in decision-making. DTI forges a decentralised network of systems, devices and agents, fostering collaboration that reduces hierarchical reliance. [9] Here, the workforce plays a key part, contributing a unique human dimension to the integrated system.
The article provides insights into the concept of DTI as a form of collaborative digitalisation, where distributed activities and expertise strengthen production processes. It introduces the European knowlEdge project, which provides a platform that enables DTI and collaboration and shows how pooling expertise and resources improves problem solving, ideation and product development. Finally, the article looks at the building blocks for a roadmap of DTI and collaboration platforms, highlighting core features such as technology fusion, intelligent control, seamless collaboration, co-creation, sustainability and resilience.
2 Decentralised Technical Intelligence
Decentralised Technical Intelligence (DTI) is an innovative concept that will revolutionise production by leveraging technologies for collaborative digitalisation. In response to the demands of networked production, DTI aims to empower decentralised, autonomous systems with embedded intelligence. This concept was born out of the European Technology Platform ManuFuture’s call to increase productivity and efficiency in future manufacturing [9, 10].
DTI utilises advanced technologies and coordination methods to drive significant improvements. It integrates multi-sensor networks, AI, machine learning, simulation and more to optimise processes, enhance performance and ensure sustainability. DTI empowers systems to self-optimise, adapt in real time and collaborate seamlessly between human and AI agents. Its objectives encompass high productivity, quality, efficiency, flexibility and resilience, contributing to a holistic improvement in manufacturing. Additionally, DTI strives for sustainability by setting zero-impact goals, including zero emission, zero defect, zero waste and zero downtime, ensuring both efficiency and responsibility. [9, 10]
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Building on earlier advances, DTI’s interdisciplinary approach marks a next step towards more sophisticated production systems and contributes to a comprehensive transformation. By combining human expertise and advanced technologies, DTI creates an adaptable, intelligent production ecosystem capable of responding quickly to changing conditions. This convergence recognises the unique strengths of humans and machines and envisions a multi-agent architecture in which all stakeholders work towards common goals. [9] In this sense, collaborative digitalisation is the channel through which insights, decisions and actions flow, ensuring efficient interaction and seamless coordination within the production network. [11]
3 The KnowlEdge Platform and Its Implications
The European project knowlEdge - Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop] (2021 – 2023) embodies principles of DTI and aligns with the concept of collaborative digitalisation [12]. The knowlEdge project (www.knowledge-project.eu) offers AI solutions that are distributed, scalable, secure, standardised and collaborative. The project’s platform is designed to support the management of distributed data and knowledge sharing, thereby enhancing collaboration across the network (Fig. 1).
Fig. 1.
knowlEdge response to the needs of industrial supply chains
The integration of various cognitive technologies such as data analytics, IoT, digital twin and edge-to-cloud computing further amplifies collaboration. These technologies enable real-time data collection, analysis and dissemination, ensuring that relevant information is accessible to all stakeholders in the network. This interconnectedness optimises decision-making, sustainable resource allocation and overall operational efficiency.
Moreover, human-centric design and human-computer interaction signify the project’s commitment to involving human intelligence and expertise within the digital ecosystem. This aligns with the essence of DTI and collaborative digitalisation, which aim to combine the collective intelligence of humans and tools to achieve synergistic results and create a cognitive, continuously learning production system.
A pilot study exemplifies the platform’s functionality, focusing on production scheduling. The use case encompasses supply chain optimisation, demand projection and production batch refinement within an industrial setting, with DTI technologies playing a pivotal role. Real-time data from production facilities becomes usable via sensors and edge-based interfaces, integrating monitoring, analysis, and informed decision-making. Machine learning algorithms analyse historical and current data for demand forecasting, production schedule optimisation and bottleneck identification. Digital twins anticipate and simulate future scenarios, enabling proactive production plan adjustments. [13]
The fusion of AI and data analytics forms the basis for effective decision-making, error avoidance and time saving. The AI system consistently learns and broadens its knowledge base through documented anomalies, errors, and their resolutions. This positively impacts various performance metrics like scheduling hours, response time, productivity and forecasting accuracy [14]. User interfaces are pivotal in this interaction, prioritising human-centric engagement and real-time monitoring. They facilitate process adjustments and edge coordination, accelerating proactive decision-making and reinforcing synergy between humans and AI agents. This holistic approach creates a viable production system.
Essentially, DTI’s multifaceted impacts accelerate the evolution of networked production. Firstly, it enhances collaboration among humans and AI agents, boosting decision-making in the collaborative supply network. Secondly, it expedites knowledge sharing, leveraging digital connectivity and standardised data exchange. Thirdly, DTI enables dynamic, autonomous operations, bolstering responsiveness and resilience. Fourthly, it drives innovation through enhanced collaboration, fostering adaptability. Lastly, DTI promotes standardisation for cohesive data exchange, benefiting from its decentralised nature.
4 Building Blocks of a Roadmap
A roadmap for advancing networked production through DTI can leverage collaborative digitalisation to drive Europe’s production future [10]. Its foundation envisions integrated human-machine intelligence, seamless collaboration, and agile production networks. Within the frame of this article, the roadmap encompasses a number of building blocks, each contributing with a value proposition and based on various technological and organisational specifications, including:
Integrating multiple technologies, such as AI, IoT, data analytics and cyber-physical systems, to create a holistic digital ecosystem
Establishing standardised interfaces for seamless interactions
Ensuring robust data governance and security
Designing for human-centric collaboration
Encouraging real-time knowledge sharing for continuous learning
Empowering DTI agents for agile decision-making
Promoting decentralised operations and resilience
Fostering partnerships to accelerate innovation
Fig. 2.
Specifications forming the roadmap building blocks
The outlined specifications serve as a foundation for the roadmap’s building blocks, which propel networked production through the implementation of DTI (Fig. 2). Additionally, the roadmap could also function as a dynamic research agenda, guiding ongoing innovation efforts [15]. The following five building blocks of the roadmap exemplify how collaborative digitalisation plays a pivotal role in shaping the future of networked production.
1.
Universal Transparency: Aims to achieve comprehensive end-to-end transparency across value networks, fostering collaboration, efficiency, and trust through real-time monitoring and information availability.
2.
Cooperate to Compete: Focuses on collaborative innovation through co-design, co-engineering, and co-production, resulting in customised products and services while leveraging the benefits of shared knowledge and resources.
3.
Sustainable and Circular Operations: Optimises resource efficiency, minimises waste and adopts circular economy principles to achieve extended machine operation and secure new resources, ultimately enhancing sustainability and reducing environmental impact.
4.
Intelligent Control of Value Networks: Focuses on decentralised and autonomous management of value networks, leveraging both human and artificial agents to enhance anticipation, agility and overall performance.
5.
Integration with (New) Network Partners: Concerned with dynamic integration, separation and collaboration with partners, fostering overall network performance, efficiency and customer satisfaction.
5 Conclusion
DTI is a catalyst for rapid networked production development that goes beyond incremental progress. Integrating work into a multi-agent system and merging human expertise with AI insights adds a unique dimension to intelligence and improves processes and autonomy. For example, in a supply chain context, multiple DTI agents work autonomously together across a decentralised network.
The roadmap for networked production is a strategic plan that embodies Europe’s future potential. The holistic approach of DTI synergy with production illustrates the strengthening of competitiveness, innovation and sustainability. This approach extends to supply chains, logistics and mobility, triggering a cascading effect that increases resilience, efficiency and adaptability. Consistent use of DTI strengthens industrial performance and permeates supply chains and mobility systems, reinforcing the foundation of networked systems.
Acknowledgements
This research has received funding from Horizon 2020 under grant no. 957331.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
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