Engineering Management Research Articles

Research perspectives of supply chain knowledge management: A bibliometrics study

The purpose of this article is to present a bibliometric analysis of scientific publications on knowledge management (KM) and supply chain (SC), provide an overview of research activities in this field and recognise its most substantial and fundamental aspects. In addition, this study aims to quantitatively analyse KM in SC in other words supply chain knowledge management (SCKM) research trends, forecasts and citations from 1999 to 2021 in Web of Science (WOS). A total of 499 documents related to SCKM research were collected from the following databases: SCI-EXPAND, SSCI, AHCI and ESCI. These documents were carefully reviewed and subjected to bibliometric data analysis techniques. We map the time trend, disciplinary distribution, high-frequency keywords and topics, major authors and influential publications to show emerging topics. Based on the findings of other researchers, many implications emerged that improve one’s understanding of the identity of SCKM as a distinct multi-discipline scientific field, and its publication has grown significantly since 2018. SC papers were published in engineering and Operations Research and Management Science journals, while the coverage of research topics related to KM and SCKM is broader and more interdisciplinary than those of SC papers. High-frequency keywords associated with SCKM research are listed. From the citation of 499 papers with both KM and SC as keywords, we find the most popular one.

Evaluating the research and development (R&D) efficiency of the Chinese construction industry

PurposeThe construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.Design/methodology/approachBased on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.FindingsResults suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.Originality/valueThis study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures.

Performance Characteristics of Newly Developed Real-Time Wave Measurement Buoy Using the Variometric Approach

Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument.

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

Synergistic influence of gyrotactic microorganisms and bimolecular reaction on bidirectional tangent hyperbolic fluid with Nield boundary conditions: A biomathematical model

In biomedical engineering, the behaviour of gyrotactic microorganisms with non-Newtonian fluids such as tangent hyperbolic fluids improve the design of targeted drug delivery systems. In this system control over microorganism movement is essential. The present study deals with the synergistic influence of gyrotactic microorganisms and bimolecular reactions on the bidirectional flow of tangent hyperbolic fluids under Nield boundary conditions. Further, the flow characteristic of the non-Newtonian fluid is enhanced by incorporating the impact of thermal radiation, heat sources, Brownian motion, and thermophoresis. The presentation of these phenomena is vital for an extensive range of applications, including industrial processes, biomedical engineering, and environmental management. The analysis employs advanced mathematical modelling which needs suitable transformation rules to get the non-dimensional form and further numerical simulation is presented with the assistance of the “shooting-based fourth-order Runge-Kutta technique”. The results are depicted for the several contributing factors via the built-in in-house function bvp4c in “MATLAB”. The authentication of the study with the prior research is a benchmark to precede further research in this direction. However, the outstanding results are; the fluid velocity is controlled by increasing non-Newtonian Weissenberg number whereas the velocity slip shows dual characteristics on the axial velocity distribution. Further, the motile microorganism profile is controlled by the enhanced bioconvection Lewis number.

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.

Research and Development of A Comprehensive Management Platform for Intelligent Production and Construction of the Entire Process of Sulige Gas Field

Abstract The Sulige gas field is the first fully installed atmospheric field in China with an annual production capacity exceeding 30 billion cubic meters. It has the characteristics of low permeability and density, rapid development decline, and large annual compensation decline workload. As a pilot project for the regional production and construction project system in Changqing Oilfield, Sulige Gas Field Development Branch is facing problems such as a wide range of project management points, limited personnel, difficulty in engineering data collection, and tight time connection between various professional businesses. By using digital and information technology, it has developed and created a closed-loop management platform for the digital production capacity construction of the entire business chain of production and construction project drilling, testing, and investment, and constructed a multi-dimensional visual control mechanism for the entire process of a single well, Realized digital management of the operation of a single account for the entire business chain production and construction project of drilling and trial investment; Relying on operator resources to quickly and low-cost expand the basic network, achieving real-time video monitoring of multi platform drilling and testing sites, collection of construction data, and online tracking; Establish a big data model for condition analysis and early warning, and innovate the formation of a “digital+safety dual prevention mechanism” linkage management and dynamic and static risk early warning push; Develop 16 APP functions such as supporting coordinates and well site handover to achieve online flow management of production and construction business, more efficiently guide production organization and close connection of processes, and significantly improve the efficiency of production and construction organization; Creating a dedicated digital and intelligent environment for production and construction, achieving deep integration of digital and intelligent business with Oil and gas fields production and construction, further enhancing project management and collaborative combat capabilities among various specialties. The innovative digital production capacity closed-loop management of Sulige Gas Field platform realizes the full lifecycle and multidisciplinary integrated management of wellbore engineering. Its efficient digital production organization model can provide a certain reference for the organization and management of similar projects in the upstream of exploration and development.

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.