Industry 4.0 is reshaping manufacturing, with Zero Defect Manufacturing (ZDM) emerging as a cornerstone for competitiveness and sustainability. This paper introduces a readiness assessment framework that evaluates four critical dimensions — infrastructure, processes, personnel, and company culture — bridging technological capability with quality management. A structured questionnaire operationalizes these dimensions and was applied to five European firms in the automotive and semiconductor sectors. Results show that while technological infrastructure is relatively advanced, predictive analytics and workforce data literacy remain underdeveloped, limiting the realization of ZDM’s full potential. The findings highlight the importance of integrating human-centered factors such as training, culture, and risk awareness with advanced digital tools. By providing both a diagnostic baseline and actionable insights, the framework supports companies in benchmarking their progress and guiding strategic investments. Beyond individual firms, it also offers researchers and policymakers a foundation for advancing ZDM adoption as part of a sustainable, defect-free manufacturing ecosystem.
Drawing on the three-in-one model for evaluating entrepreneurship policies, this paper quantitatively assesses 52 policies on scientists’ entrepreneurship introduced in Shanghai’s Zhangjiang and Tianjin’s Binhai districts since 2017. The results show that the policy suite has become increasingly comprehensive, spanning laws, regulations and detailed implementation rules, yet its effectiveness remains low and inter-agency coordination is weak. Policy supply is broadly balanced: early-stage support for scientists predominates initially, whereas culture-building and training measures decline as start-ups progress, while barrier-reduction instruments expand. Overall, current policies still provide insufficient and ineffective support for scientists’ entrepreneurial activities. Binhai has begun to foster an enabling ecosystem and to tackle deep-seated institutional obstacles. The findings improve policy design and thus ensure more effective implementation.
In recent years, management analysis has become more pragmatic. While data infrastructure is more mature and artificial intelligence (AI) tools are more powerful, the supply chain and organizational environment are more uncertain. The focus of academic research has also changed accordingly. It no longer merely pursues higher prediction accuracy but pays more attention to how to understand complex systems and how to support actionable decisions. This article selects 36 papers published in the Journal of Management Analytics in 2025 for a literature review. The results show that the annual research focuses on five themes: supply chain optimization and resilience, AI and Generative Model-driven Decision Support, Human resource analysis, optimization under Sustainability and ESG constraints, method integration and modeling innovation. This article forms an annual research map through a literature review, which presents references for academic research and management practice.
We present a review on the applications of large language models (LLMs) in health, e.g. social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning and ethical considerations. We integrate findings from interdisciplinary studies utilizing diverse data sources such as social media posts, electronic medical records and multimodal inputs to enable early detection of depression, suicide risk assessment, personalized therapy support and psychoeducational content generation. Our review highlights advancements in LLM models and annotation strategies that enhance interpretability and clinical relevance, while we also emphasize the critical role of prompt engineering for domain adaptation. We also discuss emerging multimodal fusion techniques integrating text, speech and sensor data for improved mental health diagnosis and monitoring. Finally, we address ongoing ethical, sociotechnical and regulatory challenges, and advocate frameworks to ensure safe, equitable and accountable deployment of LLMs in real-world mental health care.
The integration of blockchain technology into Internet of Things (IoT) forensics transforms digital investigations and resolves challenges in the security and validation of evidence within increasingly interconnected systems. This paper explores how blockchain’s immutable, decentralized, and transparent ledger enhances the integrity, authenticity, and traceability of forensic data collected from IoT devices. By embedding blockchain within IoT ecosystems, investigators gain access to tamper-resistant records and establish credibility and admissibility in legal proceedings. Smart contracts and decentralized trust models automate security protocols to reduce human error while enhancing efficiency. Real-world applications in smart homes, healthcare, industrial automation, and communication networks demonstrate the framework’s potential to strengthen forensic processes. This paper interprets blockchain’s transformative role in advancing IoT forensics, for robust, transparent, and future-ready investigative methodologies.
Cloud computing involves collecting data and software along with seamless access over the internet rather than the computer’s hard drive. Cloud storage facilitates files and programs to be downloaded and uploaded, and viewed over the internet instead of a computer’s hard disc, using any digital device. This modern production age transforms industries worldwide with an increasingly digital future where the cloud provides special processing, storage and networking capabilities. This technology is an essential component of Industry 4.0; it recognizes emerging innovations to investigate methods to be modified to fulfill current customer requirements. Due to significant advances in the Internet of Things (IoT), robotics, cloud-based technologies and Artificial Intelligence (AI), the fourth technological revolution is now being realized. This new revolution is triggering tremendous transformations in different areas, particularly in financial services, products, healthcare, automobiles, and building services. This paper details cloud computing’s significant potential and a study on different cloud computing applications for Industry 4.0. Calculation services make it possible for the platform, leading in the long term to innovative technologies, combining automation, the Internet of Things, and robotics.
This work systematically investigates the challenges impeding the organizational transition from Industry 4.0 to Industry 5.0 in smart manufacturing. A systematic literature review protocol was applied, sourcing 39 high-quality peer-reviewed publications from the Web of Science database (2019-2024), filtered through rigorous inclusion and exclusion criteria. A total of 16 key challenges were identified, which are further categorized as organizational, social, and environmental, considerably affecting the transition from Industry 4.0 to 5.0. Eight of these challenges were classified as critical challenges based on citation in the literature are: Non-human-centered approach, insufficient training and new competences, environmental sustainability, risk in privacy and data security, lack of knowledge management, work-life balance, job loss and job market transformation and increased energy demand. Further, a rigor and relevance assessment of these 39 studies is carried out, which revealed strong methodological quality and practical applicability but lacked empirical robustness. The results emphasize the necessity for the development of a hybrid industrial framework that fuses the technological strengths of Industry 4.0 with the ethical and sustainable values of Industry 5.0.
Background: Many institutions of higher learning still rely on learning management systems that were developed in the past, which do not fully utilize social media (SM) to engage scholars in collaborative learning (CL). A lot of research has been done to determine how to use SM to enhance researchers’ academic performance (AP) through CL, as SM usage has increased recently, particularly among researchers as well as lecturers at educational institutions. It seems that SM can offer to the development of academic communities, which enhance group projects and boost students’ grades. Methodology: The extended Technology Acceptance Model (TAM) model is used to find the impact of SM on CL and AP. The sample was collected from 150 postgraduate students who provided the data. Perceived utility is one of five categories that were studied. PEOU, SM purpose, AP, and CL were other factors. By using PLS-SEM, the study robustly measures the relationships between these factors, highlighting the complex interplay of digital tools in higher education. Findings: It finds that CL plays a vital role in the link between SM use and academic success, emphasizing the value of teamwork in the digital age. Using the TAM, the research offers new insights into how SM can boost student engagement, improve communication, and encourage knowledge sharing in education. PLS-SEM is used for data analysis. The result demonstrates that SM use fosters a cooperative learning environment in which students may successfully complete coursework and group projects. The research finds that SM boosts CL and AP by increasing student engagement and facilitating group work. Students who perceive SM as beneficial and easy to use show improved academic outcomes, making SM a valuable tool in higher education. Unique Contribution: SM improves the learning environment and boosts academic achievement by promoting student engagement and involvement, managing group conversations, and aiding the completion of assignments or research projects. Researchers have indicated the significant impact of SM and cooperative education on students’ academic efficacy.
This study, based on a real industrial case, demonstrates that applying Network Data Envelopment Analysis (NDEA) to Overall Equipment Effectiveness (OEE) uncovers stage-specific bottlenecks and reveals efficiency patterns that traditional OEE overlooks, for example, identifying units misclassified as inefficient. The implications of the proposed NDEA-OEE approach extend far beyond the company analyzed. Since OEE is one of the most widely adopted performance indicators across industries such as automotive, electronics, steel, and pharmaceuticals, any methodological advance in its measurement carries broad industrial relevance. By capturing the multi-stage nature of production systems, NDEA-OEE prevents misinterpretation of efficiency levels, avoids misguided investments in maintenance or equipment replacement, and enables more accurate benchmarking across plants and sectors. The case study results highlight the model’s ability to distinguish stage-specific technical efficiency from overall resource allocation efficiency, offering insights unattainable through OEE alone. In the context of Industry 4.0 and digitalized production, this approach can be scaled across diverse industrial settings, supporting decision-makers in achieving cost reduction, productivity gains, and sustainable competitiveness at an industry-wide level.
Evaluating complex military systems and materials offers significant challenges for defense organizations, requiring robust methodologies to assess multiple competing criteria. This study develops and applies an integrated Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution with a dual normalization approach to support armored vehicle acquisition decisions for the Brazilian Army. The research addresses critical gaps in current procurement practices by transforming unstructured technical requirements into machine-readable ReqIF standards and implementing a systematic framework for multi-criteria decision analysis. Through this hybrid AHP-TOPSIS-2N method, the study demonstrates how hierarchical weighting of technical, logistical and industrial factors can be combined with mathematical ranking of alternatives against ideal solutions. The results identify the optimal vehicle configuration, maintaining consistent top performance across two normalization scenarios. Validation by defense acquisition specialists confirms the effectiveness in enhancing transparency and objectivity in procurement decisions. Beyond military applications, the research contributes to a generalizable decision-support framework adaptable to complex acquisitions across public and private sectors where structured requirements evaluation is paramount.
Small and Medium Enterprises (SMEs) must improve their organizational readiness to effectively implement Industry 5.0 (I5.0) technologies. However, there is limited guidance on which organizational factors are critical for this transition. This study investigates how strategic, technical, and social factors influence the adoption of I5.0 technologies in SMEs. This research integrates the Natural Resource-Based View (NRBV) and the Dynamic Capabilities Theory (DCT). A survey of 80 companies was conducted to assess the influence of various organizational factors on I5.0 adoption. The companies were grouped into clusters based on organizational readiness, using K-means clustering and Analysis of Variance. Ordinal Logistic Regression was also employed to identify significant organizational predictors of I5.0 adoption in SMEs. The study found that company size significantly impacts organizational preparedness for I5.0 adoption. Additionally, the analysis revealed that companies should focus on developing a strategic vision, building a skilled workforce, securing financial resources, and integrating into digital supply chains to facilitate I5.0 adoption. The findings offer useful insights for managers, identifying key organizational factors to prioritize for successful I5.0 implementation. The results provide actionable guidance for companies uncertain about how to begin their I5.0 journey.
This research designs a biofuel supply chain network based on uncertain demand. This network combines economic, environmental, social, and transportation risk goals. A new multi-objective mixed-integer linear programming model has been developed. It includes three biomass types: second-generation Jatropha, livestock and agricultural waste, and third-generation microalgae. The model optimizes biodiesel and electricity production using transesterification and anaerobic digestion. It considers supplier discounts and uncertain demand using chance-constrained programming. According to the results, the least amount of energy is produced when the environmental goal is given the most importance. It will be less expensive to invest in social dimensions than in other goals, except for the economic one. The situation where energy production is prioritized has the highest costs and the biggest carbon emissions. The smallest social effects happen when reducing the risk of carrying hazardous materials is preferred. Overall, the largest deviation from all objectives occurs when the first objective function (energy) is prioritized, and the lowest distance to the optimal value of all objective functions occurs when reducing the transportation risk of hazardous materials has a higher weight and importance. Therefore, managers can get more advantages by focusing on reducing transportation risks and investing in it.
The rapid advancement of Industry 4.0 (I4.0) technologies has revolutionized various industries, including the cold supply chain (CSC) sector. When it comes to preserving the quality and integrity of temperature-sensitive goods, such as perishable food items and medications, CSC plays an important role. Despite this, traditional CSC management methods often lack real-time visibility, leading to inefficiencies, higher costs, and potential product spoilage. This study aims to eliminate inefficiencies and bottlenecks in the traditional CSC management system and to improve cold supply chain performance (CSCP) by leveraging I4.0 technologies across all stages. The study proposed the 10 most significant assessment criteria to evaluate the potential of I4.0 technologies to improve CSCP. Furthermore, the study presents a new I4.0-based assessment model that captures the mutual interactions among the assessment criteria and I4.0 technologies, especially tailored for CSCP improvement. The quantitative analysis of the developed model has been conducted using an integrative methodology comprising the Interval-Valued Neutrosophic-based Analytical Hierarchy Process (IVN-AHP) and the Combinative Distance-based Assessment (IVN-CODAS) methods. Sensitivity and comparative analyses have been conducted to assess robustness and validate the results obtained from the proposed methodology.
Aiming at the balance problem between the rapid development of the regional economy and carbon emission (CE) control, a method for analyzing the impact of urban industrial ecosystems based on spatial econometric models was studied and proposed. By constructing a CE index system and a spatial error model, the dynamic relationship between provincial CEs and urban industrial ecosystems in China from 2012 to 2021 was empirically explored. The experimental results show that for every 1% increase in CEs, the development index of the urban industrial ecosystem significantly increases by 1.027% (P<0.1), indicating that CEs drive economic growth in the short term. The Moran index analysis shows that there is a spatial positive correlation between CEs and urban industrial ecosystems. For example, the total CE index Moran's I=0.154, P<0.01, confirming the linkage effect between CEs and economic activities among regions. While CE intensity decreased by an average of 2.3% annually, the ratio of GDP to CE intensity increased by 15.8%, confirming that low-carbon transformation can promote the coordinated development of the economy and ecology by improving energy efficiency. Research shows that CEs have a dual role of short-term driving and long-term optimization in urban industrial ecosystems. It is necessary to achieve a dynamic balance between economic growth and carbon reduction through policy guidance such as green investment and increasing the proportion of renewable energy, providing quantitative decision-making basis for the green transformation of urban industrial ecosystems.
Identifying and analyzing the cooperative effectiveness of regional innovation systems is an important prerequisite for reflecting the operational efficiency and effect of regional innovation systems. The regional innovation system is a complex system network composed of many subsystems. Therefore, it is crucial to propose an effective measurement and evaluation method to reflect the essential characteristics of collaboration. Based on the principle of order parameters in synergetics and the idea of cloud model, this paper proposes the mechanism of order parameters membership cloud in the operation of regional innovation system, then proposes the evaluation process of cooperative order degree of regional innovation system based on order parameters membership cloud, and gives the identification method of order parameters in the cooperative order degree process of regional innovation system, and then builds the order parameters member cloud model. According to the possible results, the evaluation criteria and management and control strategies of cooperative order degree are constructed. Finally, taking the innovation system of 30 administrative regions in China as an example, the measurement and evaluation method proposed in this paper is applied to verify the scientific rationality of the model and method proposed in this paper. The method and control strategy proposed in this paper can provide method support for decision-making of relevant departments and enterprises of regional innovation management.
This paper presents an environmentally sustainable Economic Production Quantity (EPQ) model under the influence of imperfect production and shortages. The defective items are reworked under an asynchronous rework process, where the reworking starts just after the production process stops. In this study, we have proposed two models under shortages, one with shortages satisfied by perfect production, and the other with shortages satisfied by production permitting defective item production which are reworked under asynchronous approach. During the processes of production, transportation and storage of inventory items, significant amount of carbon emissions is generated. In order to minimize these emissions and to reduce the setup costs, this paper proposes the integration of investment in green technology. The primary objective of this paper is to obtain the optimal values of lot size, backordered quantities, setup costs and green investment amounts in order to minimize the total cost of the manufacturer. To find the optimal solution, we have employed a metaheuristic method, Grey Wolf Optimization (GWO). A numerical example is also presented, accompanied by a sensitivity analysis to illustrate and validate the outcomes of the proposed inventory models.
Robotics is becoming prevalent in our everyday lives. Agriculture is the world's most significant industry, with a tremendous technological demand. It is presently appropriate to adopt robotics applications in farming since the global food chain is under strain from factors such as population expansion, climate change, population drift from rural to urban areas and ageing populations. Robotics is seen as a means of escaping the unsettling reality. The robots are very sophisticated; they know how much water a specific plant needs. The same situation holds with fertilizer. Every plant will get just the proper quantity of fertilizer to keep it healthy. They may move into fields fast and resemble corn plants. This paper is about the need for robotics in agriculture. Several available cooperative robots, their tasks, and the associated challenges and prospects for the agriculture domain are discussed. Finally, it identified and discussed significant applications of Robotics for Agriculture. Robots can be used to monitor every plant in a field, whether big or small. This can assist in spotting any faults or concerns and provide their report immediately to the farmers. Farmers may quickly determine what types of problems exist in their fields in this manner without having to inspect them physically. These robots are remarkable for their accuracy and fineness. Agricultural robots are specialized technological devices that may help farmers with various tasks. They may be designed to develop and adapt to meet the requirements of different activities, and they can assess, consider and perform multiple tasks. There are some limitations to implementing this technology in agriculture, such as safety, maintenance, environmental factors, high initial cost, training requirements and increased unemployment. In the future, robots will determine the optimum planting locations, the ideal harvesting times, and the best paths for crisscrossing the farms.
The study aims to understand the way in which Explainable Artificial Intelligence (XAI) is enhancing decisions made in the process of project management. It was a quantitative and research design where a structural survey tool using a validated scale was used to measure key constructs of study XAI, trust, understanding and quality of decision-making. The 233 samples were selected through an online questionnaire that was sent to experienced project managers who operate in fields of technology, construction, healthcare and financial aspects of different industries. The analysis of data employed structural equation modeling in the framework of SmartPLS software. The findings suggest that XAI considerably improves trust and understanding among project managers, which further boosts the quality of decision-making processes. To be more precise, path analysis revealed that the coefficient between XAI and trust ([Formula: see text]), XAI and understanding ([Formula: see text]), trust ([Formula: see text]) and understanding ([Formula: see text]) and quality of decisions was high and positive. Combined, they comprised 53% of the variance in the quality of decisions and this implies the mediating influence of the latter. Current research allows introducing findings that are considered a contribution to the current corpus of knowledge because they empirically prove the significance of XAI as a method of creating grounds of trust and comprehension within the project management environment.
The study investigates determinants of interest in fintech careers and the role of fintech-specific education in entrepreneurial intention. Using a cross-sectional survey (n=325), the study models relationships among technological proficiency, innovativeness, risk-taking and career interest. Reliability and convergent validity were satisfactory (CR=0.77-0.87; AVE=0.60-0.70). Structural paths were significant (e.g. technological proficiency -> career interest beta=0.25; innovativeness beta=0.30; risk-taking beta=0.20; all p<0.01). Participation in fintech-specific education/training strongly predicted fintech entrepreneurial intention (beta=0.82, p<0.001), with the model explaining R2=0.58 of variance in career interest and R2=0.67 in entrepreneurial intention. The study discusses implications for curriculum design and talent pipelines in the fintech sector.
The technological evolution that enterprises are facing is increasingly fast and dynamic, demanding more and more flexibility and agility in the application of new technologies in an increasingly globalized and competitive business environment. This work has a broad view of the industry 4.0 influence in the global supply chain companies, as it is essential for long-term business sustainability and one of the key drivers of profitability and growth. The model proposed in this research effort considers the application in the processes of an aerospace enterprise, which includes, from the beginning of the demand request, also the industrial activities, to the post-sale analysis; finally, an unprecedented indicator was developed to measure the level of application of Industry 4.0 concepts in organizations according to the state of the art in the evaluated processes. Then, the model was applied in real cases of aerospace companies. The first findings demonstrate the ease of understanding and applicability of the model for companies to analyze the digital transformation in their processes and identify gaps to convert them into real opportunities to leverage their business.
