Artificial Intelligence (AI) has emerged as a transformative enabler for supply chain optimization, offering advanced capabilities in predictive analytics, real-time decision-making, and operational efficiency. However, successful AI integration in supply chain management (SCM) is influenced by multiple interdependent technological, organizational, and governance-related factors. This study aims to identify and structure the key factors affecting AI-driven supply chain optimization in manufacturing industries. A mixed-methods approach was employed. In the qualitative phase, semi-structured interviews with domain experts were conducted to identify critical factors in AI adoption. These factors were then analyzed using Interpretive Structural Modeling (ISM) to develop a hierarchical model of relationships. Subsequently, MICMAC analysis was applied to classify factors based on their driving and dependence power. The results indicate that the technology deployment strategy, governance, compliance, and financial considerations are the most influential independent factors, serving as foundational drivers of AI adoption. Data and system requirements emerged as linkage factors with high driving and dependence power, while operational applications such as demand forecasting and automation were identified as dependent factors. The findings highlight the importance of structured AI strategies and robust data governance frameworks in achieving effective AI-driven supply chain transformation, providing valuable insights for practitioners and policymakers.
Pipe flange connections are critical components in nuclear and petrochemical facilities, where reliable sealing and structural integrity are essential for safe operation. In research on open-pool reactors, ion beam tubes represent a specialized application of flange-connected pressure boundaries. This study investigated the structural response and sealing performance of a beam tube–flange assembly using three-dimensional nonlinear finite element analysis. The beam tube was evaluated under two operating conditions: an empty configuration and a configuration subjected to externally applied mechanical loads. Stress analysis indicated that the maximum Von Mises stress in the beam tube remained well below the material yield strength and satisfied the design deformation limit of 3 mm for both loading scenarios. Sealing performance assessment of the flange joint showed a bolt stress variation of 23 MPa, which remained within acceptable design limits. The corresponding gasket stress variation was 0.49 MPa, with maximum and minimum gasket stresses were within the recommended operating range of 1.4–8 MPa. Time-dependent analysis revealed gasket stress relaxation of 3.5% for the empty beam tube and 3.78% for the externally loaded case. The finite element results were validated using analytical models, demonstrating good agreement with discrepancies ranging from 0.01% to 5.8%. The findings confirmed the structural adequacy and sealing reliability of the beam tube–flange assembly under the investigated operating conditions, providing a robust basis for safe design and operation in research reactor applications.
Aluminum 7075 (AA7075) thin plates are used extensively in the marine industry, especially for the manufacture of skin panels for hydrofoils. Although surface grinding is a finishing process for AA7075 thin plates, the extent of the influence of grinding parameters on the apparent elastic modulus is not well understood. This study statistically evaluates the influence of surface grinding parameters on the apparent elastic modulus of AA7075 thin plates. Samples of AA7075 thin plates were ground with respect to an experimental design, and their apparent elastic modulus was measured from the DI-CP/V2 Servo-hydraulic testing machine. It was found that the feed has the highest standardized effect (7.0) on the apparent elastic modulus (9.84–32.81 GPa), followed by table speed (5.8) and grinding depth (5.5). The two-way interactions were significant except for the table speed-grinding depth interaction. The regression model shows a close match to the experimental data as indicated by the low standard error (S = 2.14), large coefficient of determination (R2 = 82.51%), and high adjusted coefficient of determination (R2 adj = 73.52%), which means that the chosen factors and interactions can be used to explain the large percentage of the variability of the apparent elastic modulus. The optimal grinding parameters were found at high table speed (50 spm), high feed (5 mm), and high grinding depth (1 mm).
Indoor air pollution currently poses a significant challenge, adversely impacting both the physical and mental health of women. The substantial use of fossil fuels for domestic chores endangers women's health and exposes them to considerable risks. In this regard, this study elucidates the factors driving the use of fossil fuels in domestic kitchens in Pakistan. The association between primary indicators such as women's health, duration of women’s current pregnancy, and pregnancy status with household air pollution is analyzed. We use Ordinary Least Squares (OLS) regression in Stata, and the results indicate that the type of cooking fuel significantly affects women's health. Furthermore, indoor cooking also negatively influences the duration of pregnancy. Based on these results, the paper provides practical policy recommendations for governments and policymakers to mitigate health risks. This study is instrumental in analyzing the current state of household air pollution, offering a robust model for countries heavily reliant on fossil fuels, and examining the consequent health impacts on women.
This is a conceptual theory-building paper that questions the notion of a "trade-off narrative" between sustainable supply chain management. It claims that advanced Information Systems (IS) play a crucial dual function in designing the co-construction of environmental and competitive value. The paper proposes a Dual-Role framework based on an abductive analysis of modern literature, mapping the synergistic impact pathways of these technologies. The framework proposes that the Internet of Things (IoT) allows data-driven closed-loop control, blockchain provides cryptographic trust, Artificial Intelligence (AI) offers predictive intelligence for systemic leanness, and cloud platforms offer orchestration and alignment. The framework results in a series of research propositions that can be tested and specify the likely conditions for this dual value creation. We argue that strategically located IS can transform sustainability from a constraint on operations to a parameter of the design for competitive advantage. This study offers a propositional theory for scholars to guide empirical research, as well as a principled rationale for managers to view digital investments as a foundation for planetary and business resilience.
The manufacturing industry in Nigeria has been the so-called cornerstone of economic development since the sector has continuously played a major role in providing jobs and industrial capital. However, there remain issues that impede its progress, including inefficient resource use, unstable regulatory systems, and the need to comply with international market requirements. Specifically, it examined the effect of employee engagement, process efficiency, and resource allocation on Nigeria’s manufacturing sector. A survey research design was adopted, with a total population of 117 employees across five manufacturing firms. A total of 91 participants were administered a structured questionnaire. Data collected was analyzed using PLS-SEM. Findings revealed that employee engagement has the strongest effect on sustainable production (β = 0.412, t = 6.250, p < 0.000), followed by resource allocation (β = 0.237, t = 3.610, p < 0.000), and process efficiency (β = 0.174, t = 2.877, p = 0.004). It concluded that continuous improvement is significantly vital for sustainable production in the five sugar manufacturing firms studied in Nigeria. It is therefore recommended that management of these selected firms focus on developing unique resource allocation strategies, employee engagement, and process efficiency to ensure sustained workforce improvement, thereby achieving sustainable production outcomes.
The purpose of this study is to investigate the factors that contribute to the success and resilience of construction projects in Ethiopia’s construction industry. A major gap exists in the current literature regarding the empirical validation of the relationships between resilience and success in the Ethiopian construction industry. Data collection was conducted via an online survey between March 8th and May 13th, 2024. PLS-SEM analysis was performed on the survey responses. Results indicate that resilience significantly impacts a project's success. The following are resilience-enabling factors that increase a project's resilience and enhance overall project performance. These are: resilient leadership (taking risks; learning from subordinates; being flexible during decision making); organisational structures (having few direct reports; narrow span of control; job rotation); and project team culture (assign right people for right jobs; recognition or rewarding the team members; team passions for contribution for project success; team trusts); external environment factors (mitigating high inflation effect; managing unstable economy; using appropriate legal enforcement); and risk management practices (communicating frequently; understanding/knowing how to implement risk management practices; integrating risk management practices into routine activities; having enough budget/finance). Success indicators for construction projects include quality, cost, time, safety and customer satisfaction. Therefore, developing resilience-enhancing strategies could greatly increase the effectiveness of construction project performance. The contribution of this study includes empirical evidence from the Ethiopian context, as well as practical recommendations for policymakers and project managers to create customised resilience strategies. The limitations of this study include reliance on self-reported data and convenience sampling in collecting survey responses. Overall, the study emphasizes the importance of resilience in overcoming the challenges of the construction industry in Ethiopia.
In recent years, the use of high-strength steels in hardfacing process has become increasingly common. One typical industrial example is the case of hydraulic shears used in building demolition operations, where the components are exposed not only to significant abrasive wear but also to intense dynamic loading. The use of quenched and tempered high-strength steel grade S690QL has become particularly widespread in this field, primarily as the base material for the hardfacing applied to the most heavily loaded regions of demolition shears. However, quenched and tempered high-strength steels are highly sensitive to the effects of the welding thermal cycle, which typically cause detrimental changes in the microstructure and mechanical properties of the heat-affected zone. The thermal cycles occurring during hardfacing differ from those typical of fusion welding, and consequently, the structure and mechanical properties of the resulting heat-affected zone may also vary. In addition, the penetration depth of the hardface layer can differ, which may significantly alter the load-bearing cross-section of the high-strength steel and, thus, the in-service behavior of the component. In the experimental work, hardfaced samples were performed on S690QL base material using different levels of heat input, thereby producing varying penetration depths. The aim of the study was to determine the effect of penetration depth on the resistance of the hardfaced component to dynamic loading. The tests were carried out at both +20 °C and –40 °C. The results clearly demonstrated that samples with deeper penetration exhibited reduced toughness at both investigated temperatures.
Logistics costs have become a concern for the Indonesian government, and in 2024, they accounted for 14% of the Gross Domestic Product (GDP). Transportation costs are one of the elements of logistics costs. This condition has compelled leaders and teams in the apparel industry to allocate resources efficiently, effectively, and productively, with minimal waste. Based on this reason, the organization sought to identify the root causes of waste and implement improvements in transportation. These wastes of road transportation were identified and reduced by DMAIC (Define, Measure, Analyze, Improve, Control) method and lean tools, including Value Stream Map (VSM), Lean Metrics, Five Whys (5Ws), Transportation Overall Vehicle Effectiveness (TOVE) – Overall Equipment Effectiveness (OEE), transport software, and SmartSheet. Data collection and observations were conducted in 2024 and 2025, resulting in improvements across various aspects, including a 75.75% reduction in parking time, a 4.67% decrease in distance traveled, an 82.66% decrease in vehicle utilization, and a 16.66% reduction in transportation costs. The Lean concept remains an effective tool for reducing waste.
The evolution of AI is changing the landscape of project management. The integration of AI into project management brings many advantages, yet it is also accompanied by prominent weaknesses and serious challenges. In addition, rapidly evolving technologies continue to transform the field’s dynamics. These evolving dynamics result in ambiguity about the current state of the field, and consequently, create an uncertainty regarding a roadmap for future advancements. The purpose of this paper is to address this challenge by developing a well-grounded conceptual insight that identifies the risks associated with AI adoption in project management, guiding both academia and industry towards a structured approach to its future advancements. This paper conducts a detailed structured literature review, adhering the PRISMA protocol, to evaluate the impact of AI on key facets of project management, its potential benefits and implementation challenges. Then it analyzes the literature and synthesizes the key findings. Finally, it conducts comprehensive analysis to identify both positive and negative risks i.e. opportunities and threats. This in-depth analysis and its findings enable us to understand the nature of the risks, and how those can be harnessed or mitigated to advance the field. Furthermore, it provides both academia and industry the foundation to plan improved risk mitigation strategies and to develop a structured adoption framework. This study is expected to make a significant contribution to the advancement of the field.
This work investigates the wettability properties of a glass surfaces by using atmospheric pressure cold plasma systems. Treatments were performed by using a rotating-head unit and a jet-type torch during the plazma treatments. The nozzle-to-surface distance (8–15 mm) and the feed rate (50–400 mm/s) were modifying. The untreated glass showed limited wetting, with average water and ethylene glycol contact angles (WCA and EGCA) of 64.7° ± 1.8° and 45.2° ± 1.5°, respectively. After plasma treatment, both systems showed clear improvements, although their efficiency profiles were different. Using the rotating plasma head at 8 mm and 100 mm/s speeds, the WCA decreased to 9.3° ± 0.8°, indicating almost complete wetting. Jet plasma achieved similar results (WCA = 14.1° ± 1.2°), but slightly less uniformly. Changes in wettability were closely related to the exposure time determined by the feed rate: slower movement increased activation, while overexposure occasionally resulted in small thermally induced surface marks that were visible under an optical microscope. As the results showed the rotating plasma reached more homogeneous activation, while the jet system provided stronger local effects at a lower energy input. Based on these results the atmospheric plasma is effective in increasing the surface energy. Rotating systems appear to be advantageous for large, flat areas, while jet plasma is better suited for localized surface modification aimed at improving adhesion or coating performance.
Quality 4.0 builds on the principles of Industry 4.0 to improve quality management. Integrating Quality 4.0 into Business Process Management (BPM) examines how digital technologies enhance process efficiency and innovation. The main research question is how Quality 4.0 can be aligned with existing BPM frameworks and how the digital transformation of traditional quality management techniques facilitates efficiency. A bibliometric analysis was conducted using the Scopus database, employing keyword analysis, link mining techniques, and network mapping to identify research trends and gaps. The results show the main directions of research on integrating Quality 4.0 and BPM and highlight implementation barriers such as organizational resistance and the need for strategic alignment. The study also suggests directions for future investigation, including the development of standardized frameworks for evaluating digital BPM outcomes and exploring emerging debates around human-centric, sustainable, and resilient approaches reflected in Industry 5.0 and Quality 5.0.
This paper deals with the numerical analysis of functionally graded spherical bodies subjected to combined thermal and mechanical loads. A method is presented to train deep neural networks to approximate the important solutions. We outline two approaches for generating the training dataset for a deep neural network, followed by a method for creating the neural network itself. Then, through a numerical example, we investigate the axisymmetric problems of radially graded spherical bodies (e.g., ideal spherical pressure vessels). Based on the results obtained, we evaluate the accuracy of solving the outlined problem using the proposed neural network.
The construction supervision consultant is an appointed party responsible for overseeing the implementation of construction projects from start to finish. The satisfaction level of project owners with the consultant’s performance is a key indicator of project success. This study aims to determine the level of satisfaction and key factors influencing it in public building projects. A quantitative method was employed by distributing questionnaires to respondents from the Public Works Office, particularly within the Cipta Karya Division in Tapin Regency, which serves as the case study location. Data were analyzed using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The results showed that overall, the consultant’s performance was rated as very satisfactory, with the highest CSI score on the communication indicator (86.64%) and the ability to ensure and improve work quality (85.33%). The lowest CSI score was found in the documentation/administration indicator (80.11%). Although the CSI score indicates a high level of satisfaction, there is still room for improvement, particularly in administrative aspects and understanding of technical regulations. The IPA analysis also shows that several indicators fall under top priority, such as documentation/administration, supervision, internal human resources, communication, and the ability to ensure and enhance work quality. The improvement strategies proposed include training, high discipline, and effective communication.
The rapid advancement of digital technologies has raised uncertainty about the adequacy of traditional maintenance models to meet Industry 4.0 requirements. This study develops and validates an asset management framework to support the South African petrochemical industry’s transition to Maintenance 4.0. The framework was validated through a quantitative survey conducted within a leading petrochemical company in South Africa, ensuring its practical applicability. Descriptive statistical analysis confirmed 15 of 17 framework characteristics and supported five of seven theoretical propositions. Key enablers of Maintenance 4.0 adoption include the integration of human intelligence, machine learning, and real-time data, as well as the role of organizational culture and asset resilience in shaping outcomes. The study offers both theoretical contributions and practical guidance for maintenance professionals seeking to align maintenance practices with Industry 4.0 principles, with relevance extending beyond the immediate case context.
This research looks at the vital roles of leadership and organisation design in the attainment of project resilience and success in the construction sector. Informed by contemporary theories on organisational resilience and leadership, a framework was developed and rigorously tested against data using Partial Least Squares Structural Equation Modelling (PLS-SEM) and with more advanced techniques of segmentation (FIMIX-PLS and PLS-POS) to identify and take into consideration unobserved heterogeneity. Using data collected from project professionals, resilient leadership and adaptive organisation design were shown to be critical to project resilience, but the effect of leadership and organisation design on project resilience differed from segment to segment as well as across demographics. The ex-post analysis suggested that the awareness of resilience, practical experience and higher education exacerbated the relationships between aspects of resilient leadership and project resilience, as well as between adaptive organisation design and project resilience. The analysis also showed that relationships between leadership, organisational structure, and resilience can be mediated by demographic factors, such as awareness, experience, and education. The findings highlighted the importance of fostering inclusive, participative type leadership styles and continuous forms of experiential learning to enhance resilience outcomes. The value of specific indicators such as team participation in decision making or the leader's self-confidence was also identified as being critical aspects of resilient organisational structures and effective leadership. The implications of this study were important for each group of stakeholders: organisations should encourage resilience-based leadership, experiment with multi-dimensional flexible team structures and create a culture of continued, experiential learning and communications as knowledge and industries evolve. The theoretical contributions that validated the effects of segments of latent variables and offered insight into the added value of using segmentation were positive contributions to theory. Limitations, such as sample size and sector, stimulate avenues for future work and in particular reinforce the case for longitudinal, cross-sector research to build sectors’ internal and external constructs of project resilience. Future research needs to apply multi-facilitated empirical, qualitative and advanced analytics means to enable further quantification, and complexity in project survival, success and resilience.
This study evaluates the long-term economic feasibility of electric waste collection vehicles (EVs) as a sustainable alternative to diesel-powered counterparts in urban municipal services. Using real operational data from a Hungarian waste management company, we developed a total cost of ownership (TCO) model spanning 10 years, which incorporates investment costs, energy consumption, maintenance, depreciation, and battery replacement. Our analysis reveals that although EVs require a significantly higher upfront investment (€350,000 vs. €183,200), their lower operational and maintenance costs result in a break-even point around year 8. When accounting for a €50,000 battery replacement in year 6, the total 10-year cost of the EV remains lower (€431,769 vs. €450,914) than the diesel vehicle, resulting in a net saving of €19,145. The study emphasizes the significance of local energy prices and service structures in assessing fleet electrification. While the findings are based on Hungarian data, the proposed methodology can be adapted internationally to support data-driven decision-making in sustainable waste logistics.
This study examines Romania’s progress in emergency caller location technologies and accessibility between 2020 and 2024, within the broader European context. Applying a PRISMA-informed review methodology, it draws on official reports, academic sources, and EU regulatory data to evaluate the implementation of Advanced Mobile Location (AML), HTML5 geolocation, and the Apel 112 mobile application Romania was among the first EU countries to deploy AML and subsequent performance metrics indicate notable advancements in geolocation accuracy through hybrid handset- and network-based methods. However, despite these advancements, the adoption of the Apel 112 app has declined, raising concerns about user trust, public awareness, and accessibility. To assess system inclusiveness, this study applies the International Classification of Functioning, Disability and Health (ICF) framework. Findings reveal that persons with hearing, speech, and cognitive impairments continue to face substantial barriers due to the absence of real-time text (RTT), video relay services, and universally designed interfaces. These results support both hypotheses: Romania has strengthened its technical infrastructure for caller location (H1), yet persistent accessibility and interoperability limitations remain (H2). The study concludes that inclusive design, user education, and cross-platform compatibility must become priorities for emergency communication policy to ensure equitable access for all users.
