Research background: Digital ecosystems in Europe are heterogenous organizations involving different economies, industries, and contexts. Among them, Digital Innovation Hubs (DIHs) are considered a policy-driven organization fostered by the European Commission to push companies’ digital transition through a wide portfolio of supporting services. Purpose of the article: There are DIHs existing in all European economies, but literature needs more precise indications about their status and nature. The purpose is to study a distribution of DIHs and differences in portfolios of DIHs’ services across European economies. Therefore, the paper wants to deliver more precise data on effects on national and European policies. This is required to define their final role and scope in the complex dynamics of the digital transition, depending on regional context and heterogeneity of industries. Methods: Data on 38 economies was collected from the S3 platform (on both existing and in preparation DIHs) and further verified by native speaking researchers using manual web scrapping of websites of DIHs identified from S3. To find potential similarities of digital ecosystems in different economies as emanated by the existence of DIHs, clusterization (Ward’s method and Euclidean distances) was applied according to the services offered. Economies were clustered according to the number of DIHs and the spread of DIHs intensity in different cities. The results were further analyzed according to the scope of the provided services. Findings & value added: The applied clustering classified European economies in four different sets, according to the types of services offered by the DIHs. These sets are expression of the different digitalization statuses and strategies of the selected economies and, as such, the services a company can benefit from in a specific economy. Potential development-related reasons behind the data-driven clustering are then conjectured and reported, to guide companies and policy makers in their digitalization strategies.

Industry 4.0 (I4.0) became an important paradigm to bridge the gap between technologies and humans. The paper aims to diagnose sustainability performance and I4.0 maturity in Poland’s printing and packaging sector and identify research areas where further actions for improvements are necessary. This article adopts a mixed-method study combining in-depth interviews of eleven heterogeneous enterprises, supported with a quantitative survey on a representative sample of 301 companies. The findings revealed an insignificant correlation from a statistical point of view (0.44) between the adopted I4.0 technologies currently used and sustainable best practices. Internet of Things technologies are more often adopted in the printing industry (27.2 %) than in the packaging industry (14 %). The study concludes that using I4.0 technologies boosts the execution of sustainable practices and/or realising sustainable development practices requires I4.0 technology adoption. The paper clarifies that more in-depth analyses are needed to help achieve sustainable objectives for printing and packaging companies through digital technologies. The methodology is replicable and might be applied in other economies across separate multinational enterprises to influence sustainable digitalised business strategy.

Industry 4.0 industrial automation paradigm and the related new Operator 4.0 role and pool of competencies are playing a critical role in bringing forth the Digital Transformation to manufacturing industry and SMEs in particular. The human-centric aspect of Industry 4.0 in combination with resilience, sustainability and circularity of manufacturing processes is gaining wider acceptance in Europe and across the globe while the transition towards Industry 5.0 starts to gain momentum as well as the integration of human centric solutions in Industry 4.0 automation systems. The current work uses a three-pronged approach to wearable sensors integrated with existing Industry 4.0 automation systems, by addressing sensor heterogeneity, data interoperability and network latency issues under the umbrella of a single unified and harmonised solution. Such a solution is realised in a realistic industrial scenario showcasing adaptive Human-Robot collaboration and leverages open-source software and open reference architectures.

This paper investigates the implementation of dataspace technologies in complex supply chains to mitigate the bullwhip effect, i.e., a phenomenon where demand variability amplifies as it propagates along the supply chain. Using a simplified linear supply chain simulator, the study compares two scenarios: a limited transparency case where supply chain partners operate with restricted information, and a full transparency scenario enabled by dataspace technology that allows real-time data sharing. The results demonstrate that dataspace-enabled transparency, combined with Demand-Driven Material Requirements Planning (DDMRP), significantly reduces demand amplification and inventory level fluctuations across the supply chain.

Traditional sustainability frameworks in large scale production systems, such as Process Industry (PI) ones, often overlook operational resilience, creating a “resiliency gap” where systems optimized for efficiency remain vulnerable to disruptions. This study addresses this gap by proposing and empirically validating a Quadruple Bottom Line (4BL) framework that integrates resilience as the fourth pillar alongside economic, environmental, and social goals. The purpose is to evaluate the impact that Autonomic Computing (AC) can imply in this perspective. A Procedural Action Research (PAR) methodology was conducted across four distinct PI industrial cases (asphalt, steel, pharma, and aluminum). This involved the ECOGRAI framework to qualitatively link strategic companies’ objectives to shop-floor Key Performance Indicators (KPIs), guiding the assessment of AC systems. The results show benefits at a business level observed following the introduction of AC systems, which were implemented for enhancing resilience by managing ML model drift. Key findings include reduction in plant downtimes, decreases in waste (steel), reductions in gas consumption, and improved operator trust. This research provides empirical evidence that AC can make resilience an actionable component of industrial strategy, leading to measurable improvements across all four pillars of the 4BL framework. Its contribution is methodological and operational, aiming to demonstrate feasibility and causal plausibility.