Engineering Management Research Articles

Uncertainties in Water Engineering Design and Management: The Shortcomings of Technology and the Centrality of the Human Being

Uncertainties in Water Engineering Design and Management: The Shortcomings of Technology and the Centrality of the Human Being

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

The digital twin framework: A roadmap to the development of user- centred digital twin in the built environment

With the prompt digital revolution in architecture, engineering, and construction (AEC), it is crucial to set the early practical steps for advanced digital technology namely digital twin technology. Although theoretical evidence on the potential of digital twins exists in AEC scope, the practical framework to the construction and operation of user-centred digital twin is yet to be investigated. Hence, this applied research investigates systematic methods and techniques for the construction and enablement of a user-centred digital twin paving the way to advanced digital applications in AEC centred on the user of the built environment. As a result, the framework unleashes a systematic roadmap to the creation and development of a city-scale digital twin centred on the human and capable of describing, analyzing, and assessing a built context per its users and usability manner. Further, the framework pours the foundations for a spectrum of digital twin applications in the scope of architectural design, building construction, infrastructure engineering, and facility management. The production of this research is foreseen to outline a systematic and transferable framework for the construction and enablement of a user-centred digital twin.

Strategizing Originality for Technology Convergence: An Engineering Management Approach

This study examines how the integration of internal expertise with external research partnerships influences the confluence of technologies. Analyzing a decade’s patents, it clarifies the efficacy of varied knowledge acquisition strategies on innovation. The research quantifies the contribution of such synergies to innovation across technological, industrial, and geographic realms. Methodologically, it employs patent analytics to dissect originality into technology scope, assignee variety, industry diversity, and country-specific innovation trends from 2010 to 2019. Findings reveal a positive link between comprehensive technological and industrial knowledge and enhanced technological convergence. Significantly, it accentuates the role of global collaboration in unifying new technologies and industries. This interdisciplinary approach and in-depth organizational learning understanding are vital for spurring innovation. The results offer pivotal insights for academia and policy formulation in technology governance, advocating for integrated knowledge strategies to drive sector-spanning technology unification. The study provides a blueprint for fostering innovation through inter-sectoral and interdisciplinary exchange.

Long-time prediction of multi-indicator water quality based on the improved WaveNet under time series decomposition and phase space reconstruction

Abstract Long–time prediction of water quality indicator such as chlorophyll–a (Chl–a) is crucial for water process engineering and environmental management. In order to capture the characteristics of long–time series and reduce the limitations of traditional long–time prediction strategies, this paper proposes a novel hybrid model by combining data decomposition, phase space reconstruction, feature fusion and improved WaveNet. Firstly, the original data is decomposed into several subsequences through time series decomposition. Then, the subsequences with chaotic characteristics are integrated with multiple features for phase space reconstruction. Next, the decomposed and reconstructed subsequences are fed back into the improved WaveNet model separately. Finally, the prediction results are obtained by summing the predicted values of the subsequences. In this paper, the reliability of the method is assessed using the dissolved oxygen, water temperature, pH and Chl–a data of a monitoring station in the Beihai coastal sea area, ablation experiments are conducted to demonstrate the effectiveness of each module in the proposed model, and comparisons with multiple benchmark and hybrid models show that the proposed model exhibits better performance in long–time prediction of coastal water quality in the next fourteen days.

ENGINEERING MANAGEMENT PERSPECTIVES ON SAFETY CULTURE IN CHEMICAL AND PETROCHEMICAL PLANTS: A SYSTEMATIC REVIEW

This systematic review examines the influence of engineering management practices on cultivating and sustaining a strong safety culture in chemical and petrochemical plants. Given the high-risk nature of these industries, establishing a robust safety culture is essential for mitigating operational hazards, reducing risks, and preventing accidents that can have serious human and environmental impacts. The review synthesizes findings from 135 peer-reviewed studies, focusing on key elements such as leadership commitment, employee engagement, regulatory compliance, safety management systems, and the integration of advanced technologies like automation and artificial intelligence (AI). The results highlight that safety performance in these industries is closely linked to management's active involvement in promoting safety practices, clear communication strategies, and the continuous assessment and improvement of safety protocols. Engineering managers play a critical role in driving safety initiatives and fostering an organizational culture where safety is a shared responsibility. The review also underscores the importance of regulatory compliance as a foundation for safety, while emphasizing that the most successful organizations go beyond compliance to embed safety deeply into their operational practices. Technological advancements, particularly in automation and real-time monitoring, were found to significantly enhance safety outcomes by enabling proactive risk identification and mitigation. Overall, this review offers valuable insights and practical guidelines for engineering managers aiming to strengthen safety protocols, reduce risks, and enhance safety performance in high-hazard industrial environments.

Directional Transportation Behavior of Droplets on a Magnetically Responsive Micropillar Array Surface.

Technologies for manipulating droplets have applications in various fields, such as biomedical devices, chemical engineering, water collection, and thermal management. The morphology of magnetically responsive surfaces are modified using magnetic fields for inducing droplet directional rolling and bouncing. Herein, a study on the directional transport of droplets on magnetically responsive surfaces is reported, identifying three movement modes and analyzing the mechanisms influencing the transport behavior. The study explored working fluids with surface tension lower than that of water, achieving the directional transport of 30% ethanol droplets with a maximum transport velocity of 130 mm s-1. The practical applications of the droplets on a modified surface were analyzed, and methods were developed to accelerate droplet mixing. This study explored efficient operations and automation of complex chemical processes.

TIC y gestión administrativa en la Escuela de Ingeniería y gestión ambiental de la Universidad Nacional Autónoma de Huanta, Ayacucho-2024

Objective: To determine the relationship between ICT and administrative management in the School of Engineering and environmental management of the National Autonomous University of Huanta, Ayacucho-2024. Method: Theoretical, descriptive and explanatory, non-experimental, correlational, sample of 80 collaborators of the entity. Results: The correlation between these variables was 0.502. Conclusions: These findings show that the effective use of IT by employees contributes to optimizing administrative management.

Realizing the Promise of Digital Engineering: Planning, Implementing, and Evolving the Ecosystem

ABSTRACTGaining benefits of digital engineering is not only about implementing digital technologies. An ecosystem for innovation is a system of systems in its own right, only partly engineered, subject to risks and challenges of evolving socio‐technical systems. This paper summarizes an aid to planning, analyzing, implementing, and improving innovation ecosystems. Represented as a configurable model‐based reference pattern used by collaborating INCOSE working groups, it was initially applied in targeted INCOSE case studies, and subsequently elaborated and applied to diverse commercial and defense ecosystems. Explicating the recurrent theme of consistency management underlying all historical engineering, it is revealing of digital engineering's special promise, and enhances understanding of historical as well as future engineering and life cycle management. It includes preparation of human and technical resources to effectively consume and exploit digital information assets, not just create them, capability enhancements over incremental release trains, and evolutionary steering using feedback and group learning.

Application of Laplace and Sumudu transforms to the modeling of unsteady rotating MHD flow in a second-grade tetra hybrid nanofluid in a porous medium

Understanding and managing the complex rheological behavior of fluid models is crucial for many real-world applications, such as industrial fluid dynamics, biomedical engineering, and environmental management. This study aims to predict the unsteady magnetohydrodynamic (MHD) flow of a second-grade tetra hybrid nanofluid in a porous medium containing uniformly dispersed dust particles. To address this challenge, we develop a mathematical model utilizing a non-fractional approach with ramping conditions. Our methodology incorporates Laplace and Sumudu transforms to analyze the velocity fields of both the fluid and dust particles. Additionally, we examine the heat transfer characteristics and the underlying dynamics of the magnetized second-grade tetra hybrid nanofluid flow. The momentum transfer of the dust particles is modeled using a separate equation. Key outcomes of this research include visual representations of novel findings, facilitating comparison with existing literature. Numerical simulations based on Sumudu and Laplace transformations produce velocity profiles for both fluid and dust particles, as well as temperature distributions, aligning with theoretical expectations. These results demonstrate the effectiveness of Laplace and Sumudu transforms in analyzing and simulating the turbulent MHD flow of nanofluids through porous media with dispersed dust particles.

Exploration of Collaborative Design Practice and Potential Issues of Blockchain Technology in the Whole Process Project Management of Construction Engineering

With the development of social economy, urban planning has been paid more and more attention. At present, China is implementing low-carbon environmental planning in stages, stages and stages with the development goal of building two types of society, namely, national master plan and strategic development. This paper analyzes low-carbon environmental planning based on blockchain, then expounds the specific content of whole process project management, and completes the design of green building index system based on low-carbon environmental planning. At the same time, the design method of new dynamic structure and index system of green building is expounded. The comparative analysis shows that the material loss cost of the new environmental protection building engineering and index system is low, and the material utilization rate is high, accounting for more than 95%.

CEST conversation with Prof. Marc A. Rosen: Evolving responsibilities and contributions in science and engineering for global carbon neutrality transitions

Prof. Marc A. Rosen is a Professor of Mechanical & Manufacturing Engineering at Ontario Tech University (formally University of Ontario Institute of Technology) in Oshawa, Canada, where he served as founding Dean of the Faculty of Engineering and Applied Science. Prof. Rosen has served as President of the Engineering Institute of Canada and of the Canadian Society for Mechanical Engineering. He has received numerous awards and honors, including an Award of Excellence in Research and Technology Development from the Ontario Ministry of Environment and Energy, the Engineering Institute of Canada Smith Medal for achievement in the development of Canada, and the Canadian Society for Mechanical Engineering Angus Medal for outstanding contributions to the management and practice of mechanical engineering. Prof. Rosen received a distinguished scholar award from Toronto Metropolitan University (formerly Ryerson University) and a Mid-Career Award from University of Toronto. He is a fellow of the Royal Society of Canada, the Engineering Institute of Canada, the Canadian Academy of Engineering, the Canadian Society for Mechanical Engineering, the American Society of Mechanical Engineers and the International Energy Foundation.

Compaction Characteristics of Fly Ash and Pond Ash: A Comparative Analysis

This study investigates the compaction characteristics of Fly Ash and Pond Ash, which are by-products of coal combustion in thermal power plants. Thermal power plants generate three types of ash: (a) Fly Ash, (b) Pond Ash, and (c) Bottom Ash. Fly Ash consists of fine particles carried out of the boiler with flue gases, while Bottom Ash is the residue that settles at the bottom of the boiler. In most coal plants, Fly Ash is captured using electrostatic precipitators and other filtration equipment before the flue gases are released through the chimney. Pond Ash, on the other hand, is a by-product of thermoelectric power plants, often regarded as waste material, posing significant environmental disposal challenges. The compaction characteristics of Fly Ash and Pond Ash are influenced by various factors, including specific gravity, moisture content, compaction energy, layer thickness, and mold area. In this study, Fly Ash collected from NTPC Sipat , Chhattisgarh exhibited an Optimum Moisture Content (OMC) ranging from 18% to 27% and a Maximum Dry Density (MDD) ranging from 0.90 to 1.59 gm/cc under standard Proctor compaction energy. Similarly, Pond Ash demonstrated an OMC ranging from 33% to 46% and an MDD ranging from 0.856 to 1.248 gm/cc under the same conditions. The findings indicate that variations in these factors significantly affect the dry density of both Fly Ash and Pond Ash. The study also aims to determine the geotechnical properties of these materials, providing insights into their potential applications in engineering and environmental management

A displacement prediction model Based on IQGA-LSSVM for Mogu toppling rock slope in Lianghekou reservoir, China

Predicting landslide displacement is essential for developing early warning systems, assessing disaster risks, and understanding spatiotemporal evolution patterns. To address the limitations of insufficient accuracy in traditional landslide displacement prediction models, this study proposes a landslide displacement prediction model that combines an improved quantum genetic algorithm (IQGA) with a least squares support vector machine (LSSVM). The Mogu toppling rock slope is employed as a case study to demonstrate the application of the empirical mode decomposition (EMD) method for the decomposition of landslide displacement into trend and periodic components. Subsequently, these components are accurately predicted using Fourier series fitting and the IQGA-LSSVM model, respectively. The prediction results are subsequently evaluated and analyzed. The findings indicate that this model outperforms traditional QGA-LSSVM models, BP neural networks, and Support Vector Machine models in terms of prediction accuracy. Furthermore, the study illustrates how high-accuracy landslide displacement prediction models can effectively prevent and control disasters in critical areas such as hydraulic and hydropower engineering, urban planning, and geological disaster management.

The sensuous governmentality of glitter: Educating managing women scientists with gleaming STEM Barbies

This essay conceptualizes glitter as a sensuous materialization of governmentality. It does so, arguing that glitter governs in alluring ways by materializing light and sight. As such, the essay explores glitter as a sparkly substance, governing individuals in certain behavioural directions, which it also demonstrates in the illustrative case of a small group of gleaming STEM Barbies, intended to govern young girls in the direction of Science, Technology, Engineering and Mathematics management careers, through sensuous stimulations of sight. However, in materializing light and sight, glitter leaves everything un-glittery in gloomy darkness, while governing with glitter also comes with the danger of producing an unfortunate ignorant blindness, potentially countering the aims behind the use of glitter as a governing means.

Competency requirements for engineering management undergraduates in the era of intelligent construction

ABSTRACT In the era of intelligent construction, engineering management requires diverse abilities that cannot be provided through traditional engineering management education. Moreover, few studies focused on the competency requirements for engineering management undergraduates. In this study, a questionnaire with 12 competency indicators, including traditional engineering management competencies and new competencies arising from the advancement of information technology and artificial intelligence (AI), was designed and distributed to 942 employees from enterprises related to engineering construction. A total of 723 responses were received, of which 514 were deemed valid. The survey results indicate that the competency in traditional engineering management is considered “strongly necessary” or “necessary” by 84.24% of respondents. Similarly, competencies in analyzing and processing large-scale construction data, and in assisting feasibility analysis with AI, are valued by 80.35% and 78.99% of respondents, respectively. The above results illustrate that the integration of intelligent construction into traditional engineering management competencies is critical for future engineering management undergraduates. Therefore, courses on intelligent construction, such as data analysis, Building Information Modeling, and the regulated use of large language models, should be included in engineering management education. This study can provide significant support for reforming the education of engineering management.

AI And Machine Learning For Optimizing Renewable Energy Systems In Engineering Management

AI And Machine Learning For Optimizing Renewable Energy Systems In Engineering Management

Challenges in Applying System Dynamics to Address Scoping and Estimating Problems

System dynamics (SD) is a fundamental model that facilitates research by defining and modeling a project’s scope and estimation. Scoping and estimation is a set of approaches that ensure engineering managers that a project’s accuracy and cost estimation are properly specified and mapped. Engineering management’s primary project planning responsibilities include project scoping and estimating. Insufficient project scoping and estimation criteria significantly fail the project plan. The study authors collaborated with five distinctive journal institutions to address project scoping and estimation challenges. The authors thoroughly analyzed one core research issue and five secondary research questions from each article. In terms of core issues, most project managers dislike SD modeling because of its complexity, especially if they lacked engineering, programming, mathematical analysis, or IT expertise. The first secondary question revealed that project managers’ awareness of scoping and estimating problems ranges from 78.3333% to 91.6666%. The secondary question revealed that 83.3333% to 90% of managers are familiar with SD, which can help resolve scoping and estimation issues in project management. The third secondary question showed that 83.3333% to 91.6666% of managers are aware of SD during the project’s scoping and estimating phases. The fourth secondary question found that 83.333% to 91.6666% of managers can use mathematical equations and programming for SD design during the scoping and estimating phases. The authors developed a novel SD scoping and estimating model since the last question indicated that there were no downloadable models from the five journals.

Virtual Engineering: Hands‐on Integration of Product Lifecycle Management, Computer‐Aided Design, eXtended Reality, and Artificial Intelligence in Engineering Education

AbstractEngineering education at the Institute for Information Management in Engineering integrates product lifecycle management (PLM), computer‐aided design (CAD), eXtended reality (XR), and artificial intelligence (AI) to enhance learning and prepare students for modern challenges. Our interdisciplinary approach, emphasizing digital twins and virtual twins, fosters immersive, hands‐on experiences. This paper reviews our strategies, comparing them with global initiatives, highlighting the transformative impact of our curriculum on preparing future engineers for complex industrial environments.

Understanding the Variety of Domain Models: Views, Programs, Animations, and Other Models

Humanity has long since used models, in different shapes and forms, to understand, redesign, communicate about, and shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This has resulted in a wide range of modeling practices. When the models as used in such modeling practices have a key role to play in the activities in which these practices are ‘embedded’, the need emerges to consider the effectiveness and efficiency of such processes, and speak about modeling capabilities. In the latter situation, it also becomes relevant to develop a thorough understanding of the artifacts involved in modeling practices/capabilities. One context in which models play (an increasingly) important role is model-driven systems development, including software engineering, information systems engineering, business process engineering, enterprise engineering, and enterprise architecture management. In such a context, we come across a rich variety of modeling related artifacts, such as views, diagrams, programs, animations, specifications, etc. In this paper, which is actually part of an ongoing ‘journey’ in which we aim to gain deeper insights into the foundations of modeling, we take a fundamental look at the variety of modeling related artifacts as used in the context of model-driven (systems) development, while also presenting an associated framework for understanding, synthesizing the insights we obtained during the ‘journey’ so-far. In doing so, we will also argue that the aforementioned artifacts are actually specific kinds of models, albeit for fundamentally different purposes. The provided framework for understanding involves definitions of domain model, the Return on Modeling Effort (RoME), the conceptual fidelity of domain models, as well as views as a mechanism to manage the complexity of domain models.