Search Results (61)

Strong technological development and competitive pressures have driven organizations, especially in the automotive sector, to implement strategies that enhance operational efficiency, thereby improving their performance. One critical topic is the reduction in machine setup times, where the Single Minute Exchange of Die (SMED) methodology has shown significant potential. However, SMED is mostly approached as a technical tool to improve efficiency, but with limited emphasis on how its implementation can be improved through the implementation as a participatory and strategic approach based on structured workshops. This study addresses this gap by presenting the planning and execution of systematic SMED workshops to engage cross-functional teams in setup time optimization. The field tests were conducted in an automotive manufacturing firm. The setup time on a selected injection line was reduced from 48:30 to 29:41 min (38.8% improvement). Its broader applicability was validated with improvements up to 53.66% across other machines. This study contributes a practical, replicable framework for SMED implementation that integrates structured training workshops into continuous improvement processes in automotive manufacturing and highlights the importance of employee engagement and standardized work in a Lean approach. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

This paper applies a multi-level social network analysis to examine Aragón’s innovation ecosystem, focusing on a decade of competitive public projects (2014–2023) aligned with the region’s Smart Specialisation Strategy (S3) 2021–2027. By mapping and weighting the participation of regional entities across regional, national, and European calls, the study uncovers how all types of local actors organise themselves around key specialisation areas. Moreover, a comparative benchmark is introduced by analysing more than 33,000 Horizon 2020 and Horizon Europe initiatives without Aragonese partners, revealing how to fill structural gaps and enrich the regional ecosystem through international collaboration. Results show strong funding concentration in four fields—Energy, Health, Agri-Food, and Advanced Technologies—while other historically strategic areas like Hydrogen and Water remain underrepresented. Although leading institutions (UNIZAR, CIRCE, ITA, AITIIP) play central roles in connecting academia and industry, direct collaboration among them is limited, pointing to missed synergies. Expanding previous SNA-based assessments, this study introduces a diagnostic tool to guide policy, proposing targeted actions such as challenge-driven calls, dedicated support programs, and cross-border consortia with top EU partners. Applied to two contrasting specialisation areas, the method offers sector-specific recommendations, helping policymakers align Aragón’s innovation capabilities with EU priorities and strengthen its position in both established and emerging domains. Full article

This study explores the complex interaction between the Internet of Things (IoT) and the new energy sector and analyzes how their integration can catalyze a transition toward a sustainable low-carbon economy. Through the full-sample and rolling sub-sample methods, we empirically examine the dynamic interrelationship between China’s IoT index (IoT) and the New Energy Index (NEI). Quantitative analysis reveals significant time-varying characteristics and bidirectional causal complexity in the interaction between the IoT and new energy. The IoT has a dual-edged impact on the development of new sources of energy. In the long run, the IoT plays a dominant role in incentivizing new energy, helping to enhance its stability and economic value. However, during stages characterized by technological bottlenecks or resource competition, the high energy consumption of IoT infrastructure may suppress the investment returns of new energy. Simultaneously, new energy has both positive and negative impacts on the IoT. On the one hand, new energy provides low-cost, sustainable power to support the IoT, driving the construction of the IoT ecosystem. On the other hand, it may threaten the continuity of IoT power supply, and the complexity of standardization and regulation in the sector may constrain the development of the IoT. This study provides a fresh perspective on promoting the integration of digital technology and green energy, uncovering nonlinear trade-offs between innovation-driven growth and carbon reduction goals, and offering policy insights for cross-sectoral collaboration to achieve sustainability. Full article

Small- and medium-sized enterprises (SMEs) face dynamic and competitive environments where resilience and data-driven decision-making are critical. Despite the potential benefits of artificial intelligence (AI), machine learning (ML), and optimization techniques, SMEs often struggle to adopt these tools due to high costs, limited training, and restricted hardware access. This study reviews how SMEs can employ heuristics, metaheuristics, ML, and hybrid approaches to support strategic decisions under uncertainty and resource constraints. Using bibliometric mapping with UMAP and BERTopic, 82 key works are identified and clustered into 11 thematic areas. From this, the study develops a practical framework for implementing and evaluating optimization strategies tailored to SMEs’ limitations. The results highlight critical application areas, adoption barriers, and success factors, showing that heuristics and hybrid methods are especially effective for multi-objective optimization with lower computational demands. The study also outlines research gaps and proposes future directions to foster digital transformation in SMEs. Unlike prior reviews focused on specific industries or methods, this work offers a cross-sectoral perspective, emphasizing how these technologies can strengthen SME resilience and strategic planning. Full article

The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article

Amid growing concern about labour market resilience in an era of digital and green transitions, this study carries out an investigation on how academic innovation and industrial transformation jointly shape sustainable employment outcomes across EU-27 member states. We frame this inquiry within the emerging concept of the “employment trilemma”, which posits inherent tension between competitiveness, innovation, and social inclusiveness in modern economies. Drawing on a dynamic panel dataset (2005–2023) and employing System SMM estimations, we test the hypothesis that the alignment of academic innovation systems and industrial transformation strategies enhances long-term employment sustainability. Our results reveal a nuanced relationship: academic innovation significantly supports employment in countries with high knowledge absorption capacity, whereas industrial transformation contributes positively only when embedded in cohesive, inclusive economic frameworks. Thus, these findings provide valuable insights for international business due to their emphasis on the importance of cross-sectoral collaboration, policy synchronisation, and investment in human capital for firms navigating increasingly volatile labour markets. Likewise, the study offers actionable insights for business leaders, policymakers, and universities striving to balance innovation with equitable labour market outcomes in an integrated European economy. Full article

This study investigates the dynamic effect of economic liberalization, privatization, and globalization on the export performance of Ethiopian manufacturing firms. We use structural equation modeling (SEM) to examine the direct and indirect influences between these macroeconomic reforms and export performance, which are mediated by firms’ competitive priorities in the global market, using cross-sectional data from 114 manufacturing privatized manufacturing firms by using key informant techniques. The study looks into how firms’ export competitiveness and export performance are affected by economic liberalization, privatization, and global market integration since 1991 national economic reform. This model identified liberalization, privatization, and globalization as independent variables that mediated export performance under competitive priority. The findings of the proposed framework showed that all of the predictive variables (LPG) were significant at p < 0.05, indicating that liberalization, privatization, and economic globalization influence export performance across all competitive priorities. The result further revealed that by expanding access to international markets and promoting competitive efficiency, economic globalization/integration, privatization incentives, and economic liberalization changes all significantly improve export performance. The results also infer that LPG provides an intervening role in boosting export performance under firms’ competitive priorities (cost, flexibility, and quality). However, resolving issues, including inefficient regulations and inconsistent incentives, is important to realize these advantages. To optimize the advantages of these dynamics, policymakers must concentrate on establishing a business environment that encourages firms to partake in export, innovation, and competition. The study contributes to the literature by offering sector-specific insights for policymakers aiming to optimize privatization strategies and trade reforms to boost Ethiopia’s manufacturing exports. The results underscore the need for targeted policy interventions to mitigate short-term disruptions while maximizing long-term export gains in a liberalized economy. Full article

This paper explores how bioeconomy-specialized clusters function as systemic intermediaries that support innovation, coordination, and business transformation in the transition to a circular bioeconomy. Using a qualitative case study methodology, we analyze how bioclusters in Romania enable SME development, knowledge transfer, and cross-sector innovation, ultimately strengthening regional entrepreneurial ecosystems. Findings highlight the role of clusters in supporting innovation management, access to financing, and policy alignment in the transition toward a circular bioeconomy. By fostering entrepreneurial networks, facilitating skill development, and supporting SME-driven innovation, clusters act as catalysts for inclusive and resilient business growth. This study highlights how clusters create an enabling environment by providing access to financing, technology, and market expansion opportunities. Although focused on a specific regional biocluster ecosystem, the findings underscore the scalability and replicability of this model across Europe, reinforcing its contribution to entrepreneurship, innovation, and regional economic development. This study contributes to the literature on entrepreneurial ecosystems and circular bioeconomy governance, emphasizing the role of clusters as intermediary organizations that facilitate business-led sustainability transitions. The findings offer practical insights for policymakers, industry leaders, and researchers, outlining strategies to strengthen cluster-based innovation, enhance SME competitiveness, and embed circular practices into national and regional bioeconomy strategies. This paper’s added value lies in combining the Technological Innovation Systems (TIS) framework with empirical evidence from Romanian clusters—a context underexplored in current literature on circular bioeconomy transitions. Full article

In this study, the mediating effect of psychological empowerment (PE) is examined along with the impact of talent management (TM) in improving employee retention (ER). Companies increasingly realize how important strategic TM is in retaining talented workers and promoting organizational success in today’s cutthroat business world. Using a cross-sectional survey of 226 employees across multiple sectors in São Tomé and Príncipe, this study examines TM’s effects on ER through PE’s mediating role. This research demonstrates that TM has a significant positive effect on ER and that PE mediates the relationship between TM and ER. Organizations can create an environment that nurtures commitment, motivation, and long-term retention by empowering employees through targeted TM practices. According to this study, PE is crucial in connecting TM tactics to better ER results, advancing HRM theory. The findings support the theories that PE mediates the interaction between TM and ER and that TM positively impacts ER and PE. To maintain a competitive edge in the modern workforce, HR professionals looking to boost employee engagement, satisfaction, and loyalty will find this study offers helpful insights. Full article

Non-fungible tokens (NFTs) represent a promising application of blockchain technology that can potentially disrupt various sectors, mainly tourism. While there have been conceptual discussions regarding the opportunities and challenges of utilizing NFTs for purposes such as digital souvenirs, ticketing, loyalty programs, and conservation initiatives, there remains a significant need for a robust methodological framework to assess the impact of real-world NFT implementations empirically. This paper presents the methodological foundation of ongoing research. It proposes a comprehensive approach to researching NFT initiatives within the tourism sector, which includes data collection methods, analytical techniques, and the design of a workbench for monitoring key performance indicators (KPIs). The proposed framework combines quantitative and qualitative measures to capture the complex nature of NFT adoption, including financial performance, visitor engagement, user experience, and operational efficiency. By establishing standardized protocols and metrics, the proposed methodology aims to enable cross-study comparisons and contribute to developing the best practices for leveraging NFTs in the tourism industry. The work highlights the potential of NFTs to enhance visitor experiences, generate new revenue streams, and promote destinations as tech-savvy hubs, while also addressing ethical and sustainability concerns. The conclusion emphasizes the importance of a structured approach to evaluating NFTs initiatives, which can provide valuable insights for tourism organizations seeking to innovate and remain competitive in a digital landscape. Future research should focus on validating the framework through real-world case studies, exploring additional applications of NFTs in tourism, and addressing challenges related to data availability, technological integration, and stakeholder collaboration. Full article

Jiangsu province’s forestry industry offers valuable insights for resource-poor regions in China. This study aims to determine the competitiveness of different forestry sectors in Jiangsu province and identify the industrial structural changes in this context. The shift-share method is employed to analyze the primary, secondary, and tertiary forestry industries, encompassing 17 sub-sectors, and investigate the industrial regional types. By combining location quotient values and inter-sector characteristics, the leading sectors are identified. The findings reveal that the Kuznets facts and the Engel effect in the industry structure have facilitated the process of increasing the value of ecological products; this is particularly evident between 2002 and 2018, as well as in 2021. Within the primary forestry industry, the Flower cultivation sector and the Economic forest product sector emerge as the leading sectors, outperforming the national growth effect by 7.18 and 1.02 times, respectively. In the secondary forestry industry, the Wood processing sector, the Furniture manufacturing sector, the Wood paper-making sector, and the Non-timber forest product sector mostly show the Progressive types as the leading sectors from 2006 to 2020. The tertiary industry’s regional types fluctuate from the Progressive type to the Medium type, with the Forestry tourism sector, the Ecological service sector, and the Non-forestry service sector displaying leading industry characteristics. Recommendations for enhancing inter-sector integration include fostering collaborative agglomeration, unlocking the potential value of wetland resources, and strengthening cross-industry integration through e-commerce clusters to improve the forester’s e-commerce operational capabilities. Full article

In our paper, we examine the simultaneous impact of the agglomeration economy (AE) and the innovation ecosystem (IE) on the competency development of SMEs in the Piemonte region in three high-tech sectors: aerospace, mechatronics, and automotive. This study focuses on the critical challenge for SMEs: survival and market position expansion amidst rapidly changing external environments. We used the capability approach, which includes identifying and assessing a company’s competencies through the capability matrix, as a tool to analyse a company’s competitiveness in the industry based on structural changes; we determined the key insights from managerial practices of SMEs that help to comprehend the behaviour of high-performing, sustainable-performing, and low-performing companies in the development of competencies. The key finding is that SMEs’ sustainability in the context of structural changes in the external environment is primarily influenced by factors such as investment in the development of company competencies, industrial cooperation, strategic planning, cross-fertilisation, and the impact of company age and size on the availability of resources, along with the level of internationalisation. This study also identifies the main challenges these SMEs face, particularly the lack of highly skilled human resources to meet the sector’s specific needs, as well as the difficulties companies face in adapting to changes in consumer behaviour and forecasting future business strategies. In addition, this study introduces the AE and IE variables as strategic tools to enable company representatives to improve their development strategies. Full article

In recent years, the global energy sector has seen significant transformation, particularly in Europe, with a notable increase in intermittent renewable energy integration. Italy and the European Union (EU) have been among the leaders in this transition, with renewables playing a substantial role in electricity generation as of the mid-2020s. The adoption of Battery Energy Storage Systems (BESS) has become crucial for enhancing grid efficiency, sustainability, and reliability by addressing intermittent renewable sources. This paper investigates the feasibility and economic viability of batteries in wholesale electricity markets as per EU regulation, focusing on the dynamics of very different markets, namely the Day-Ahead Market (DAM) based on system marginal price and the Cross-Border Intra-day Market (XBID) based on continuous trading. A novel model is proposed to enhance BESS operations, leveraging price arbitrage strategies based on zonal price predictions, levelized cost of storage (LCOS), and uncertain bid acceptance in continuous trading. Machine learning and deep learning techniques are applied for price forecasting and bid acceptance prediction, respectively. This study finds that data-driven techniques outperform reference models in price forecasting and bid acceptance prediction (+7–14% accuracy). Regarding market dynamics, this study reveals higher competitiveness in the continuous market compared to the DAM, particularly with increased risk factors in bids leading to higher profits. This research provides insights into compatibility between continuous markets and BESS, showing substantial improvements in economic profitability and the correlation between risk and profits in the bidding strategy (EUR +9 M yearly revenues are obtained with strategic behavior that reduces awarded energy by 60%). Full article

Nowadays, there is a constant focus on implementing the net-zero emission (NZE) concept in the manufacturing supply chain (MSC). To reduce emissions and improve organisational efficiency, adopting the net-zero concept is a prevalent trend in today’s highly competitive global business environment. Governments and stakeholders are pressuring the manufacturing sector to use natural resources efficiently and reduce environmental impacts. As a result, the manufacturing industry is focusing on cleaner production using net-zero practices. This study aims to identify and analyse the interaction among the drivers of net-zero adoption in the MSC. Through a systematic literature review (SLR), a list of drivers was recognised. To validate these drivers, we conducted an empirical study with 173 respondents from the Indian manufacturing industry. Further, we employed an artificial neural network (ANN) to weigh the nonlinear effect of drivers. Fuzzy interpretive structural modelling (F-ISM) was used to identify the interaction relationships among the drivers and construct a hierarchical structure among these identified drivers. The fuzzy matrix of cross-impact multiplications applied to the classification (F-MICMAC) method was used to categorise these drivers into driving and dependent categories. The outcomes of ANN show that Environmental predictors (100%) emerged as the most significant drivers, followed by Economic drivers (60.38%) and Technological drivers (59.05%). This study is a valuable resource for academia and industry professionals, providing essential insights into how adopting net zero facilitates the manufacturing industry’s ability to achieve net zero across the supply chain. Full article