Open Access

Der Rückbau kerntechnischer Anlagen stellt eine langfristige und komplexe Aufgabe dar, an der zahlreiche Stakeholder beteiligt sind, darunter Behörden, Betreiber von Kernkraftwerken, Sachverständige sowie weitere Akteure aus der Industrie. In Deutschland sind laut dem Bundesamt für Strahlenschutz derzeit etwa 424.000 Personen beruflich strahlenschutzüberwacht, wobei hiervon ca. 68.000 Personen in Bereichen Forschung und Lehre (17.000), Industrie (34.000) und Kerntechnik (17.000) beschäftigt sind. Ein erheblicher Teil der Belegschaft wird in den kommenden Jahren in den Ruhestand gehen.

Robotic Process Automation (RPA) and Artificial Intelligence (AI) integration offer great potential for the future of corporate automation and increased productivity. RPA rapidly evolves into Intelligent Process Automation (IPA) by incorporating advanced technologies and capabilities beyond simple task automation. The paper aims to identify the organisational, technological, and human-centred challenges that companies face in transitioning from RPA to IPA. The research process involved conducting the scientific literature search using the ResearchRabbit AI tool, which provided a set of reference papers relevant to the formulated research questions. As a result of the conducted literature review, the authors identified key challenges and possible countermeasures for companies transitioning from RPA to IPA. The resulting collection of reference scientific articles formed the basis for this study’s content and substantive analysis. Furthermore, this study contributes by identifying artificial intelligence techniques and algorithms, such as Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), predictive analytics, and others, that can be integrated with RPA to facilitate the transition to IPA. The paper also offers insights into potential future research areas.

The ReMuNet project focuses on enhancing multimodal transport ecosystems along two essential European Trans-European Transport Network (TEN-T) corridors: the North Sea-Baltic (NSB) and Rhine-Danube (RD). The overall objective of ReMuNet’s third work package is to design a collaborative platform that enables high operational interconnectivity for event-based, synchromodal relay transport, tailored to the unique characteristics and requirements of these corridors. Within this work package, Task 3.2 investigates and documents the current European multimodal transport ecosystem, identifying key pain points and defining the core value proposition of the ReMuNet solution. This task sets the stage for innovative business model (BM) development, laying the groundwork for the ReMuNet solution to be effectively integrated into existing transport ecosystems. Task 3.2 kicks off with a comprehensive analysis of the current ecosystem using Business Ecosystem Mapping to capture the unique features of the NSB and RD corridors. This mapping draws on extensive desk research, surveys, and workshops, supplemented by detailed interviews with a range of stakeholders, many of which were initiated in Task 1.1. These interviews help to establish archetypical role profiles for central actors, documenting their exchange relationships and specific pain points, and highlighting the responsibilities and challenges faced by each role. By visualising the transport processes, including information and financial flows, ReMuNet creates a clear picture of how value moves through the system, allowing the team to identify inefficiencies and establish the requirements for an improved platform model. Insights gathered through this process are synthesised in a Value Stream Model developed collaboratively through workshops. This model provides stakeholders with a clear understanding of the system’s existing structure, mapping the flow of information and resources while highlighting key challenges and areas needing improvement. By creating a common understanding of these value flows, the Value Stream Model informs the development of a catalogue of requirements that guides the platform’s design, ensuring it is closely aligned with the ecosystem’s pain points and specific needs. This catalogue of requirements serves as the foundation for ReMuNet’s platform ecosystem. Designed using the Ecosystem Design Canvas, the ReMuNet platform is structured to provide tailored solutions that adapt to each actor’s specific role within the network, fostering sustainable business models that are both cooperative and competitive - a "coopetitive" approach. This design enables role-specific adaptations and supports the development of sustainable practices that align with the ecosystem’s goals. ReMuNet’s iterative approach to design also ensures flexibility, allowing the platform to evolve alongside the transport ecosystem as new roles or needs emerge. Deliverable 3.2, one of two formal outcomes of Task 3.2, consolidates these findings and insights. Led by FIR at RWTH Aachen University with the support of industry experts and academic researchers, Deliverable 3.2 provides a comprehensive description of the NSB and RD ecosystems, using Business Ecosystem Mapping to structure the existing local value creation systems and summarise role-specific pain points and needs. This deliverable also prepares the way for integrating the ReMuNet solution by facilitating a shared understanding among stakeholders, essential for building resilience in cases of rerouting or disruptions. The unified approach provided by Deliverable 3.2 enables ReMuNet to establish easily implementable solutions and develop a robust transformation methodology for the current ecosystem. Building the ReMuNet platform on a thorough understanding of the existing ecosystem allows for a smoother transformation process. By aligning ReMuNet with existing structures and processes, changes become more accessible and comprehensible to all involved stakeholders, thus enhancing acceptance and effectiveness within the multimodal transport network. This approach also supports the platform’s long-term sustainability, making it a practical and widely acceptable solution for the European multimodal transport network.

The Eurozone faces growing economic and environmental challenges, with supply chain disruptions causing losses of over EUR 112.7 billion in 2021. Climate change, geopolitical risks, regulatory shifts, and infrastructure weaknesses strain intermodal freight transport, highlighting the need for digital solutions to enhance resilience and efficiency. This paper examines key challenges in intermodal freight transport, including disruption triggers, network vulnerabilities, and inefficiencies in disruption management. Extreme weather, and capacity shortages impact both performance and sustainability. Using the Total Quality Framework (TQF), the research includes interviews with 23 stakeholders from 10 countries, focus workshops, and surveys. The analysis reveals shortcomings in real-time data integration, interoperability, and disruption response. Regulatory fragmentation and low digital maturity hinder resilience strategies. Addressing these gaps requires harmonized data frameworks, improved interoperability, and the use of collaborative digital platforms. The Horizon Europe project ReMuNet leverages intelligent algorithms and digital platforms to enhance multimodal networks, optimize route planning, and improve disruption response, contributing to the vision of the Physical Internet.

The quarrying industry, which largely consists of less digitized SMEs, is an integral part of the German economy. More than 95% of the primary raw materials produced are used by the domestic construction industry. Quarrying companies operate demand-oriented with short planning horizons at several locations simultaneously. Due to the low level of digitization and the reluctance to share data, untapped efficiency potential in data-based demand forecasting and capacity planning arises. The situation is aggravated by the fact that SMEs have a heterogeneous mobile machinery so as not to become dependent on individual suppliers, and that transport distances of over 50 kilometers are uneconomical due to high transport costs and low material values. Within the research project PROmining a data-centric platform which improves demand forecast accuracy and multi-site capacity utilization is developed. One of the core functionalities of this platform is an industry-specific demand forecasting model. Against this background, this paper presents a methodology for establishing this forecasting model. To this end, expected demands of secondary industry sectors will be analyzed to improve mid-term volume-forecasting accuracy for the local quarrying industry. The data-centric platform will connect demand forecasting data with relevant key performance indicators of multi-site asset utilization. Following this methodology, operational planning horizons can be extended while significantly improving overall production efficiency. Thus, quarrying businesses are enabled to respond to fluctuating demand volumes effectively and can increase their personnel and machine utilization across multiple quarry sites.