Journal of Sustainable Development and Policy
Journal of Sustainable Development and Policy (JSDP) is a peer-reviewed, open-access, interdisciplinary academic journal committed to advancing research, discourse, and practical solutions on issues of sustainability, development, and public policy. The journal serves as a critical forum for scholars, policymakers, practitioners, and civil society actors to explore the dynamic interplay between environmental stewardship, economic resilience, and social equity in an era of accelerating global change. JSDP recognizes that sustainable development cannot be achieved through isolated interventions or sectoral silos. As such, the journal encourages interdisciplinary and cross-sectoral perspectives that bridge the gap between theory and practice, local experiences and global frameworks, and short-term solutions and long-term impacts. Our mission is to publish forward-thinking, empirically grounded, and policy-relevant research that fosters transformative change toward just, inclusive, and sustainable societies.
Systematic Review of Electrical Engineering Contributions to Autonomous Power and Control Systems
This study presented a comprehensive quantitative evaluation of control strategies applied in autonomous power and control systems, focusing on key dynamic performance indicators including response time, settling time, overshoot, steady-state error, and control accuracy. A total of 60 experimental simulation runs were conducted using validated control system models to compare conventional proportional-integral-derivative control with advanced approaches such as adaptive control and model predictive control. The findings revealed that advanced control strategies significantly enhanced system performance across all evaluated metrics. Model predictive control achieved the lowest average settling time (M = 1.82 s) and minimal overshoot (M = 3.4%), while adaptive control demonstrated the highest control accuracy (M = 96.7%) and reduced steady-state error (M = 1.30%). In contrast, proportional-integral-derivative control exhibited slower response time (M = 2.95 s), higher overshoot (M = 7.8%), and greater steady-state error (M = 2.8%), indicating lower efficiency in dynamic environments. Inferential statistical analysis using one-way ANOVA confirmed that these differences were statistically significant at p<0.05p < 0.05p<0.05, with large effect sizes observed for settling time (η2=0.41\eta^2 = 0.41η2=0.41) and control accuracy (η2=0.38\eta^2 = 0.38η2=0.38). Regression analysis further indicated that control strategy type explained up to 62% of the variance in system performance outcomes. Secondary analysis demonstrated that adaptive and hybrid control approaches maintained stable performance under high-load variations, with only an 8% increase in response time, whereas conventional control showed up to a 25% increase in overshoot. Overall, the study confirmed that intelligent and hybrid control strategies substantially improve system responsiveness, stability, and reliability. These findings provide strong empirical evidence supporting the adoption of advanced control methodologies in autonomous power systems and contribute to the development of efficient, robust, and data-driven control architectures.
Copyright (c) 2022 Shamsul Arifeen, Md. Sultan Mahamud (Author)
Meta-Analysis of Distribution Cost Control in Retail Supply Chains: Insights from Data Modeling
This study presents an extended systematic review and meta-analysis of distribution cost control strategies in retail supply chains, synthesizing evidence from operations research and logistics optimization studies. Following PRISMA guidelines, 1,284 records were initially identified through comprehensive database searches. After duplicate removal, screening, and eligibility assessment, 83 peer-reviewed studies were included in the systematic review, of which 54 studies reported sufficiently comparable quantitative outcomes to support meta-analytic synthesis. Collectively, the reviewed studies represent a highly influential body of literature with an estimated 18,000+ cumulative citations, indicating strong scholarly impact and maturity of the research domain. The meta-analysis reveals that optimization-based strategies achieved average total distribution cost reductions ranging from 8% to 25% compared with baseline or non-optimized decision structures, depending on strategy type and network context. Inventory optimization strategies, examined in 46 studies, were associated with 10–22% reductions in total distribution cost, primarily through lower emergency shipments and stabilized replenishment cycles. Transportation and vehicle routing optimization, addressed in 52 studies, demonstrated 12–30% reductions in transportation-related costs, along with 15–35% improvements in vehicle utilization and 10–28% reductions in total distance traveled. Integrated inventory–transportation and network-level planning approaches, analyzed in 39 studies, consistently outperformed sequential planning, yielding additional cost savings of 5–15% beyond standalone optimization methods. Coordination and information alignment mechanisms, evaluated in 44 studies, reduced demand variability and logistics volatility, resulting in 20–40% reductions in emergency logistics actions and 8–18% lower total distribution costs. Uncertainty-aware models, including stochastic and robust optimization approaches examined in 38 studies, demonstrated 25–45% reductions in cost variability and 10–20% lower unplanned logistics expenditures under demand and lead-time uncertainty. Overall, the findings quantitatively confirm that effective distribution cost control in retail supply chains is achieved through coordinated, optimization-driven decision systems that integrate inventory, transportation, network design, coordination, and uncertainty management, producing both cost efficiency and cost stability across diverse retail distribution environments.
Copyright (c) 2026 Md Abdur Rauf, Aditya Dhanekula (Author)
High-Performance Computing–Enabled Hydraulic and Flood Modeling for Urban Water Supply and Drainage Systems
This study examined the effectiveness of high-performance computing–enabled hydraulic and flood modeling for urban water supply and drainage systems through a quantitative, quasi-experimental simulation-based approach. The research compared conventional hydraulic modeling frameworks with HPC-enabled models across multiple urban catchments, rainfall intensities, and drainage conditions to evaluate differences in computational efficiency and predictive accuracy. A total of 120 simulation scenarios were analyzed using high-resolution geospatial and hydrometeorological datasets, ensuring consistency in input conditions across both computational environments. The findings demonstrated that HPC-enabled models achieved a substantial reduction in simulation runtime, with mean execution time decreasing from 148.2 minutes in conventional models to 53.6 minutes, representing an efficiency improvement of approximately 63.8%. In addition to computational gains, HPC-enabled models showed enhanced predictive performance, with flood depth error reduced from 0.21 m to 0.12 m and inundation accuracy improving from 78.4% to 89.7%. Statistical analysis confirmed that these differences were significant, with p-values below 0.05 and large effect sizes observed for runtime (d = 2.85) and moderate to large effect sizes for accuracy-related metrics. Subgroup analysis revealed that the advantages of HPC were more pronounced in high-intensity rainfall scenarios, where runtime reductions reached up to 68% and prediction accuracy improved by approximately 15%, particularly in large and complex urban catchments. Sensitivity analysis further indicated that HPC-enabled models exhibited greater stability, with output variability reduced by over 20% compared to conventional approaches. Visual and spatial analyses supported these findings, showing closer alignment of HPC-generated inundation maps with observed flood patterns. Overall, the results provided strong empirical evidence that HPC integration significantly enhanced both the efficiency and reliability of hydraulic modeling, supporting its application in large-scale urban flood analysis and decision-making processes within water management systems.
Copyright (c) 2024 Palash Chandra Das (Author)
Resilient Smart Manufacturing Systems Using Predictive Analytics and Digital Twin Technologies
This study examined how predictive analytics and digital twin technologies contribute to resilient smart manufacturing systems in increasingly complex and disruption-prone industrial environments, where many firms still struggle with fragmented data systems, reactive maintenance, and limited adaptive response capability. The purpose of the research was to determine whether predictive analytics and digital twin technologies individually and jointly strengthen resilience outcomes in smart manufacturing settings. The study adopted a quantitative, cross-sectional, case-based design and drew on data from cloud-enabled and enterprise-oriented smart manufacturing cases involving production, operations, maintenance, quality, and digital systems personnel. A total of 240 questionnaires were distributed, 221 were returned, and 210 valid responses were used for final analysis, yielding an 87.5% valid response rate. The key independent variables were predictive analytics and digital twin technologies, while the dependent variable was resilient smart manufacturing systems, measured through dimensions such as disruption anticipation, adaptive response, recovery speed, operational continuity, and system flexibility. Data were collected using a five-point Likert scale instrument and analyzed through descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed high mean scores for predictive analytics (M = 4.08, SD = 0.61), digital twin technologies (M = 3.96, SD = 0.66), and resilient smart manufacturing systems (M = 4.14, SD = 0.58). Correlation results revealed that predictive analytics had a strong positive relationship with resilience (r = 0.721, p < .001), while digital twin technologies also showed a strong positive relationship (r = 0.684, p < .001). Regression analysis indicated that predictive analytics (β = 0.482, p < .001) and digital twin technologies (β = 0.361, p < .001) significantly predicted resilience, with the overall model explaining 58.9% of the variance (R² = 0.589, F = 86.47, p < .001). The study implies that manufacturing firms can substantially improve operational continuity, adaptability, and recovery readiness by integrating predictive intelligence with digitally synchronized system representation, thereby advancing more resilient and strategically adaptive smart manufacturing ecosystems.
Copyright (c) 2025 K M Tanvir Anjum Anick (Author)
A Review on Understanding Data Governance Failures in Analytics Systems: Insights from Expert Interviews and Root-Cause Thematic Coding
This study investigates the persistent problem of weak analytics outcomes in organizations despite substantial investment in data platforms, dashboards, and business intelligence tools, arguing that the deeper source of breakdown often lies in data governance failure rather than in technology alone. The purpose of the research was to examine how specific governance failures affect analytics system effectiveness and to identify which failure dimensions are most damaging in organizational settings. Using a quantitative, cross-sectional, case-based design, the study collected survey data from cloud and enterprise analytics cases through 250 distributed questionnaires, of which 214 usable responses were analyzed, yielding an effective response rate of 85.6%. The sample consisted of professionals directly involved in analytics and data processes, including Data/BI analysts, IT and systems staff, compliance officers, and reporting managers. The key independent variables were Data Quality Failure, Accountability and Ownership Failure, Policy and Compliance Failure, Access-Control and Security Failure, and Metadata and Documentation Failure, while the dependent variable was Analytics System Effectiveness. The analysis plan combined descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, and multiple regression modeling in SPSS. The findings showed high perceived exposure across all governance failure dimensions, with Data Quality Failure recording the highest mean score (M = 4.08, SD = 0.71), followed by Metadata and Documentation Failure (M = 4.02, SD = 0.69), while Analytics System Effectiveness remained comparatively low (M = 2.64, SD = 0.83). Reliability was strong across constructs, with Cronbach’s alpha ranging from 0.82 to 0.91 and an overall instrument alpha of 0.93. Correlation results revealed significant negative relationships between governance failures and analytics effectiveness, led by Data Quality Failure (r = -0.68, p < .01) and Metadata and Documentation Failure (r = -0.64, p < .01). The regression model was statistically significant, F (5,208) = 46.37, p < .001, explaining 52.7% of the variance in analytics system effectiveness (Adjusted R² = 0.527). Data Quality Failure emerged as the strongest predictor (beta = -0.31, p < .001), followed by Metadata and Documentation Failure (beta = -0.27, p < .001). The study implies that organizations seeking more trustworthy and decision-supportive analytics should prioritize governance reforms in data quality, documentation, accountability, and policy enforcement rather than relying only on technical upgrades.
Intelligent Decision-Support Systems for Cross-Functional Workflow Optimization in Data-Driven Organizations
This study investigated the quantitative relationships between Intelligent Decision-Support System (IDSS) capabilities and cross-functional workflow performance within a data-driven organizational context, with particular emphasis on the mediating role of cross-functional coordination and the moderating influence of data quality, workflow complexity, and organizational scale. Using a cross-sectional explanatory design, survey data from 238 respondents were integrated with 412 workflow-level cases extracted from organizational records. IDSS capabilities were operationalized through four dimensions: data integration capability, analytics intensity, automation degree, and feedback control strength. Workflow performance was measured objectively using cycle time, process variance proxies, bottleneck frequency, and rework rates. Descriptive results indicated relatively high levels of data integration capability (M = 4.18, SD = 0.62) and analytics intensity (M = 4.05, SD = 0.68), while automation degree showed lower maturity and higher dispersion (M = 3.62, SD = 0.81). Average workflow cycle time was 6.42 days (SD = 2.15), with a mean rework rate of 8.9% (SD = 4.2), highlighting substantial operational variability across cases. Regression analyses showed that IDSS capabilities explained a significant proportion of workflow performance variance beyond control variables, increasing explained variance in cycle time from R² = 0.19 to R² = 0.34 and in rework rate from R² = 0.14 to R² = 0.27. Data integration capability (β = −0.22, p < .001) and analytics intensity (β = −0.19, p < .001) demonstrated the strongest direct associations with reduced cycle time. Mediation analysis confirmed that cross-functional coordination partially mediated the relationship between IDSS capabilities and workflow performance, with significant indirect effects for both cycle time (β = −0.16, p < .001) and rework rate (β = −0.14, p < .001). Moderation results indicated that data quality (interaction β = −0.11, p = .006) and workflow complexity (interaction β = −0.09, p = .021) strengthened the IDSS–performance relationship, while organizational scale did not exhibit a significant moderating effect. Overall, the findings demonstrated that IDSS capabilities contributed to workflow optimization primarily through enhanced coordination and under conditions of high data quality and greater workflow complexity.
Copyright (c) 2022 Md Khaled Hossain, Hisham Mahmood (Author)
The Role of Cloud-Native Infrastructures in Supporting Autonomous and Uncrewed Systems (UXS) in Operations
This study examined the role of cloud-native infrastructures in supporting autonomous and uncrewed systems (UXS) in operations, addressing the problem that many UXS deployments depend on rigid, centralized, or insufficiently scalable infrastructures that struggle to support dynamic workloads, mission reconfiguration, fault tolerance, communication continuity, and secure data exchange in complex operational environments. The purpose of the research was to determine whether key cloud-native capabilities significantly enhance UXS operational effectiveness across case-based settings. A quantitative, cross-sectional, case-study-based design was adopted, using purposive sampling and a structured five-point Likert scale questionnaire administered to professionals engaged in logistics, industrial inspection, maritime surveillance, emergency/security, and ICT/infrastructure contexts. Out of 180 distributed questionnaires, 156 were returned, and 150 valid responses were used for analysis, producing an 83.3% usable response rate. The independent variables were scalability, flexibility, reliability, and security with data management, while the dependent variable was UXS operational effectiveness. Data were analyzed through descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression. The findings showed consistently high perceptions across all major constructs, with mean scores of 4.18 for scalability, 4.09 for flexibility, 4.23 for reliability, 4.15 for security and data management, and 4.21 for UXS operational effectiveness. Reliability coefficients were strong, ranging from 0.81 to 0.88. Correlation results indicated significant positive relationships with operational effectiveness, led by reliability (r = .71), followed by security and data management (r = .67), scalability (r = .64), and flexibility (r = .58), all significant at p < .01. Regression analysis further showed that the model explained 65.9% of the variance in UXS operational effectiveness (R² = .659, F = 69.98, p < .001), with reliability emerging as the strongest predictor (β = .31, p < .001), followed by security and data management (β = .26, p = .002), scalability (β = .24, p = .003), and flexibility (β = .17, p = .021). The study implies that organizations should treat cloud-native infrastructure as mission-critical operational architecture for improving resilience, coordination, adaptability, and readiness in UXS environments.
Copyright (c) 2025 Shamsul Arifeen, Md. Morshedul Islam (Author)
Machine Learning–Enhanced PFAS Contaminant Fate and Transport Modeling for Optimized Groundwater Remediation Strategies at Industrial Sites
This study investigates the persistent challenge of accurately modeling per and polyfluoroalkyl substances (PFAS) fate and transport in groundwater at industrial sites, where heterogeneous geology, variable groundwater flow, mixed PFAS chemistries, and uncertain source zones often weaken remediation planning and delay effective intervention. The purpose of the research was to examine whether machine learning enhanced PFAS fate and transport modeling can improve the optimization of groundwater remediation strategies in complex industrial settings. The study adopted a quantitative, cross sectional, case-based design and drew on a purposive sample of 120 professionals from cloud and enterprise style environmental decision contexts, including environmental engineers, hydrogeologists, remediation specialists, consultants, industrial site managers, and regulatory professionals. The key variables were PFAS transport complexity, machine learning modeling capability, machine learning predictive trust, PFAS predictive modeling quality, and optimized groundwater remediation strategies. Data were collected through a structured 5-point Likert scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression in SPSS. Reliability was strong across all constructs, with Cronbach’s alpha values ranging from 0.82 to 0.89 and an overall instrument reliability of 0.86. Descriptive findings showed high mean scores for PFAS transport complexity (M = 4.21, SD = 0.61), machine learning modeling capability (M = 4.08, SD = 0.66), predictive trust (M = 3.94, SD = 0.70), predictive modeling quality (M = 4.12, SD = 0.63), and remediation optimization (M = 4.18, SD = 0.59). Correlation analysis revealed that predictive modeling quality had the strongest association with optimized remediation strategies (r = 0.76, p < .01). Regression results showed that the model explained 68.4% of the variance in remediation optimization (R² = 0.684, F = 49.87, p < .001), with predictive modeling quality emerging as the strongest predictor (β = 0.38, p < .001), followed by machine learning capability (β = 0.29, p = .001), PFAS transport complexity (β = 0.24, p = .003), and predictive trust (β = 0.21, p = .006). The study concludes that machine learning enhanced modeling offers a practically significant pathway for improving PFAS remediation decision quality, plume control, treatment prioritization, and cost-efficient groundwater cleanup planning at industrial sites.
Copyright (c) 2026 Albert Anokye (Author)
Data Preparation and Feature Engineering in High-Dimensional Predictive Modeling
Data preprocessing and feature engineering are critical determinants of predictive modeling effectiveness, particularly in large-scale environments characterized by inconsistencies, missing values, and heterogeneous variable structures. This study examined the impact of structured preprocessing and feature engineering strategies on machine learning performance using a quantitative experimental design applied to a dataset of 12,500 observations and 48 predictor variables. Multiple preprocessing techniques, including missing value imputation, normalization, categorical encoding, feature construction, and feature selection, were evaluated across five supervised learning algorithms using repeated 10-fold cross-validation. Results demonstrated substantial performance gains over the baseline model, with average classification accuracy improving from 71.4% to 84.7%, F1-score increasing from 0.69 to 0.86, and AUC-ROC rising from 0.74 to 0.91. Statistical testing confirmed significant improvements at the 0.05 level, with moderate to large effect sizes observed for feature engineering and selection interventions. These findings provide empirical evidence that comprehensive preprocessing pipelines meaningfully enhance predictive accuracy, model robustness, and analytical reliability, underscoring their importance as a foundational component of predictive analytics workflows.
Copyright (c) 2024 B M Taslimul Haque, Md. Arifur Rahman (Author)
Digital Technologies and IoT: Reshaping Financial Risk and Investment in Global Supply Chains
The increasing complexity of global supply chains has intensified the need for advanced digital monitoring systems capable of improving financial risk monitoring and investment efficiency. This study examined the influence of Internet of Things (IoT) technologies and digital monitoring infrastructures on financial risk monitoring effectiveness and investment performance within globally integrated supply chain environments. A quantitative research design was employed to investigate the relationships among IoT technology adoption, digital monitoring capability, operational visibility, financial risk monitoring effectiveness, and investment efficiency. Data were collected from 214 professionals working in supply chain management, logistics coordination, procurement operations, and financial risk management roles across multiple industries, including manufacturing, logistics, retail distribution, and international trading organizations. Descriptive and inferential statistical analyses were conducted using correlation analysis, multiple regression, and structural equation modeling to evaluate the proposed conceptual relationships. The descriptive findings indicated relatively high levels of digital technology adoption across participating organizations, with 46.3% of firms reporting high levels of IoT adoption, while 36.9% demonstrated moderate adoption levels. Correlation analysis revealed strong positive relationships among the main constructs, with the highest correlation observed between digital monitoring capability and operational visibility (r = 0.71). Regression results further demonstrated that IoT technology adoption significantly predicted digital monitoring capability (β = 0.61, p < 0.001), explaining 37% of the variance in monitoring capability across organizations. Digital monitoring capability also exerted a significant influence on financial risk monitoring effectiveness (β = 0.63, p < 0.001) and investment efficiency (β = 0.58, p < 0.001). Structural equation modeling confirmed that monitoring capability functioned as a mediating factor linking IoT adoption with improved financial outcomes. The structural model demonstrated satisfactory fit indicators, including CFI = 0.94, TLI = 0.92, and RMSEA = 0.056, confirming the adequacy of the proposed model. The findings indicated that organizations with stronger IoT-enabled monitoring infrastructures demonstrated greater operational transparency, improved financial risk detection, and more efficient allocation of supply chain investments. Industries characterized by complex logistics operations, particularly manufacturing and transportation sectors, exhibited the strongest benefits from digital monitoring systems. Overall, the study provided empirical evidence that integrated IoT-based monitoring technologies significantly enhance financial oversight and investment decision-making within global supply chain networks.
Copyright (c) 2023 Mahfuj Ahmed Ruzel, Md. Mehedi Hasan (Author)
Machine Learning–Driven Optimization of Water Distribution Networks: Demand Forecasting, and Energy Efficiency Analysis
This study examined how machine learning-driven approaches can optimize water distribution networks by improving demand forecasting and enhancing energy efficiency in operational settings where utilities still struggle with demand variability, inefficient pump scheduling, pressure instability, and avoidable energy waste. The purpose of the study was to determine whether machine learning adoption significantly improves demand forecasting performance and energy efficiency and whether these factors jointly strengthen overall water distribution network optimization. A quantitative, cross-sectional, case-based design was employed using data collected through structured questionnaires from 150 usable respondents drawn from cloud-enabled and enterprise-style operational cases in water distribution environments, including utility managers, engineers, operators, maintenance personnel, and data-related staff. The key variables were machine learning adoption, demand forecasting performance, energy efficiency, and water distribution network optimization. Data were analyzed using descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression in SPSS. The findings showed strong internal consistency across all variables, with Cronbach’s alpha values ranging from 0.81 to 0.88 and an overall instrument reliability of 0.90. Descriptive results indicated positive perceptions of machine learning adoption (M = 4.12, SD = 0.68), demand forecasting performance (M = 4.05, SD = 0.71), energy efficiency (M = 3.98, SD = 0.74), and network optimization (M = 4.16, SD = 0.66). Correlation analysis revealed significant positive relationships among all study variables, including machine learning adoption with demand forecasting performance (r = 0.710, p < .01), energy efficiency (r = 0.640, p < .01), and network optimization (r = 0.730, p < .01). Regression results further showed that machine learning adoption significantly predicted demand forecasting performance (β = 0.71, R² = 0.504, p < .001) and energy efficiency (β = 0.64, R² = 0.410, p < .001), while machine learning adoption, demand forecasting performance, and energy efficiency jointly explained 68.2% of the variance in water distribution network optimization (Adjusted R² = 0.682, F = 57.36, p < .001). The study implies that utilities should integrate machine learning, predictive forecasting, and energy-aware control into routine operations to improve service efficiency, reduce waste, and strengthen intelligent water infrastructure management.
Copyright (c) 2023 Palash Chandra Das (Author)
Quantitative Assessment of CRM-Based Business Intelligence on Customer Satisfaction and Retention: Evidence from Multi-Channel Service Operations
This study investigated the effect of CRM-based business intelligence on customer satisfaction and customer retention in multi-channel service operations, addressing the problem that many organizations invest in CRM and BI systems yet still lack clear quantitative evidence of how these integrated capabilities improve customer outcomes across channels. The purpose of the research was to quantitatively assess whether CRM-based business intelligence serves as a significant driver of customer satisfaction and retention in complex service environments. The study adopted a quantitative, cross-sectional, case-based design and drew on 240 valid responses from 270 distributed questionnaires, yielding an 88.9% valid response rate. The sample represented cloud-enabled and enterprise-style multi-channel service cases involving service staff, CRM or analytics users, supervisors or managers, and customer respondents across telephone, email, live chat, mobile or web, and social media interfaces. The key independent variable was CRM-based business intelligence, operationalized through customer data integration, real-time reporting, predictive customer insight, and personalized service intelligence, while the dependent variables were customer satisfaction and customer retention. Analysis was conducted using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression modeling. Findings showed high mean scores for CRM-based business intelligence (M = 4.08, SD = 0.61), customer satisfaction (M = 4.01, SD = 0.64), and customer retention (M = 3.94, SD = 0.67). Reliability was strong, with Cronbach’s alpha values of 0.89 for CRM-based business intelligence, 0.87 for customer satisfaction, and 0.85 for customer retention. CRM-based business intelligence was strongly correlated with customer satisfaction (r = 0.68, p < .001) and customer retention (r = 0.61, p < .001). Regression results confirmed significant positive effects on customer satisfaction (β = 0.71, t = 11.42, R² = 0.504, p < .001) and customer retention (β = 0.64, t = 9.87, R² = 0.412, p < .001). Live chat (M = 4.16) and mobile or web channels (M = 4.12) showed the highest effectiveness. The study implies that enterprise and cloud-based service organizations should strengthen intelligence-driven CRM capabilities to improve service personalization, channel integration, and long-term customer relationship performance.
Copyright (c) 2024 Zakia Afroz, Rukaiya Khatun Moury (Author)
A GIS-Based Geospatial Risk Modeling Framework for Natural Gas Distribution Pipeline Infrastructure Integrity and Resilience
Distribution pipeline systems constitute a critical component of national energy infrastructure, supplying natural gas to residential, commercial, and industrial consumers across extensive urban and rural networks. Ensuring the safety and reliability of these systems requires continuous monitoring, risk assessment, and proactive integrity management strategies. This study develops a Geographic Information Systems (GIS)–based analytical framework for implementing a Distribution Integrity Management Program (DIMP) aligned with regulatory requirements under 49 CFR Part 192 Subpart P. The proposed framework integrates spatial pipeline datasets, environmental risk indicators, infrastructure characteristics, and operational history into a unified geospatial risk modeling environment. Pipeline integrity is operationalized as a quantitative risk index derived from threat indicators including corrosion susceptibility, excavation damage potential, natural force hazards, and material degradation conditions. The analytical dataset includes spatially segmented pipeline assets linked with attribute information such as installation year, pipe material, diameter, operating pressure, and environmental exposure conditions. Spatial modeling procedures include geospatial risk weighting, hotspot detection, and predictive threat scoring using GIS-based statistical modeling techniques. Risk indicators are combined through a weighted scoring algorithm to produce segment-level integrity risk scores across the pipeline network. Preliminary modeling results demonstrate that integrating GIS spatial analytics with distribution integrity management workflows significantly improves the identification of high-risk pipeline segments and enhances decision-making for inspection prioritization, maintenance planning, and regulatory compliance. The findings demonstrate that GIS-driven integrity management frameworks provide scalable and data-driven tools for managing complex pipeline distribution systems. By combining spatial analytics with regulatory integrity management programs, the proposed framework supports improved infrastructure resilience, risk mitigation, and public safety outcomes across natural gas distribution networks.
Distribution Integrity Management Program
Copyright (c) 2026 Ratul Debnath (Author)
Deep-Learning Architectures for Predicting Cardiovascular Outcomes Using High Dimensional Medical Imaging Data
This quantitative study examined deep-learning architectures for predicting cardiovascular outcomes using high-dimensional medical imaging data within a retrospective, multi-center observational cohort framework. Imaging examinations were treated as baseline predictors, and clinically documented cardiovascular events were operationalized as outcome labels using predefined event windows and censoring logic. The unit of analysis was the individual patient, and one index imaging examination was retained per patient to ensure independence of observations. The final analytic sample included 1,248 patients drawn consecutively from four clinical sites, with a mean age of 57.6 years (SD = 12.9) and 56.7% male representation (n = 708). Imaging modalities included cardiac MRI (51.0%), CT/CT angiography (26.0%), and cine echocardiography (23.1%). Composite constructs were derived to represent structural imaging risk, functional imaging dynamics, tissue characterization, and clinical risk covariates, with all constructs standardized prior to modeling. Reliability analysis demonstrated satisfactory internal consistency across constructs, with final Cronbach’s alpha values ranging from 0.80 to 0.88. Regression analyses were conducted using stepwise specifications, beginning with clinical controls and expanding to imaging constructs and an integrated imaging–clinical score. Structural imaging risk (β = 0.287, p < .001), functional imaging dynamics (β = 0.214, p = .001), and tissue characterization (β = 0.246, p < .001) were each significantly associated with cardiovascular event occurrence. The integrated imaging–clinical score produced the strongest adjusted association (β = 0.521, 95% CI [0.407, 0.635], p < .001) and yielded the best model fit (AIC reduced from 914.7 to 889.2; pseudo R² increased from 0.167 to 0.193). Model diagnostics indicated acceptable multicollinearity (max VIF = 2.18) and adequate calibration (calibration slope = 0.97; intercept = 0.03). Holdout performance demonstrated strong discrimination (AUC = 0.82) and low probabilistic error (Brier score = 0.098), with specificity of 0.90 and sensitivity of 0.63 at a 0.50 threshold. Robustness checks, including bootstrap resampling and subgroup stratification, produced consistent estimates. Overall, the findings indicated that multi-domain imaging constructs significantly predicted cardiovascular outcomes, and integrated imaging–clinical modeling provided the strongest and most stable predictive evidence.
Copyright (c) 2024 Jinnat Ara, Samiha Binte Abdullah (Author)
Topology-Optimized, 3D-Printed Thermal Management for Wide-Bandgap Power Electronics in High-Efficiency Drives
This study addressed a persistent thermal bottleneck in wide-bandgap (WBG) power electronics used in high-efficiency drives: as power density rises, localized hotspots and junction-to-coolant resistance increasingly constrain reliability, allowable switching performance, and practical adoption of advanced cooling hardware. The purpose was to quantitatively evaluate whether topology-optimized, 3D-printed thermal-management architectures are perceived as both high-impact and implementable under real manufacturing and integration constraints, and to explain adoption readiness using a quantitative, cross-sectional, case-based design. Data were collected in one time snapshot from a cross-functional sample (N = 132) evaluating enterprise drive integration cases (air-cooled 43.9% and liquid-cooled 56.1%) within industrial-grade, digitally engineered workflows (including enterprise and cloud-enabled collaboration for design/assessment contexts). Key independent variables included topology optimization quality (TOQ), perceived implementation ease (PIE, with an AM Feasibility Index proxy), thermal integration quality (TIQ), and design complexity (DC); key outcome variables were perceived thermal usefulness (PTU), thermal performance improvement (TPI), reliability expectation (RE), and adoption readiness (ARI). The analysis plan applied descriptive statistics, reliability testing (Cronbach’s α: PTU = 0.86, PIE = 0.83, TIQ = 0.81, TOQ = 0.78, DC = 0.74, ARI = 0.80), Pearson correlations, and multiple regression models. Headline results showed high perceived value (PTU M = 4.21, SD = 0.52) and favorable readiness (ARI M = 3.89, SD = 0.59), with feasibility positive but more constrained (PIE M = 3.71, SD = 0.63; AFI = 72.6/100, SD = 10.8). Correlations supported the core relationships: TOQ–TPI (r = 0.52, p < .001), PIE–ARI (r = 0.58, p < .001), and DC–ARI (r = −0.41, p < .001). Regression explained substantial variance in outcomes: TPI (R² = .48) was predicted by TOQ (β = 0.29, p = .002), TIQ (β = 0.25, p = .006), and PTU (β = 0.31, p < .001), with DC reducing TPI (β = −0.18, p = .021); ARI (R² = .52) was driven most by PIE/AFI (β = 0.36, p < .001) and PTU (β = 0.27, p = .001), while DC reduced readiness (β = −0.22, p = .006). A bottleneck attribution map identified the dominant constraint as module-to-cooler interface/TIM resistance (M = 4.12), followed by baseplate/cold-plate spreading limits (M = 3.86), implying that adoption gains will depend as much on repeatable interface control and inspectable manufacturability as on geometry innovation. These findings imply that organizations pursuing WBG drive densification should pair topology optimization with design-for-additive-manufacturing gates (especially inspection/QA and powder/support removal) and standardized interface procedures to convert thermal promise into repeatable, deployable performance.
Copyright (c) 2022 S. M. Habibullah, Zaheda Khatun (Author)
A Quantitative Financial Impact Assessment of Digital Trade Platforms on Export Performance, Capital Efficiency, and Market Competitiveness
This study examined the quantitative relationship between digital trade platform adoption and firm-level trade performance, operational efficiency, and financial outcomes within a digitally mediated cross-border trade context. Using a non-experimental, explanatory research design grounded in observational econometrics, the analysis integrated firm-level administrative trade records, financial statements, and structured survey data to assess how platform participation shaped export behavior under varying firm, sectoral, and destination-market conditions. The unit of analysis was the firm-year, and the final analytical sample consisted of exporting firms operating across multiple sectors with heterogeneous levels of digital engagement. The findings showed that firms participating in digital trade platforms demonstrated significantly higher export engagement compared with non-platform firms. Platform-active firms recorded, on average, a 22–28 percent higher export intensity and served approximately 1.6 more foreign destination markets than non-adopters. Export participation rates were also higher among platform users, with digitally active firms exhibiting export entry probabilities nearly 18 percent greater than firms relying solely on conventional channels. Operational efficiency indicators reflected moderate but consistent improvements, as platform adopters reported shorter average order-fulfillment cycles and lower order-processing variability relative to non-adopters. Financial performance results were more heterogeneous; while revenue growth rates were higher among platform-active firms by approximately 9–12 percent, profitability measures showed smaller and less uniform gains, with margin improvements concentrated among larger and more experienced exporters. The analysis further revealed pronounced heterogeneity across firm size, export experience, sector, and destination-market characteristics. Small and medium-sized enterprises experienced stronger extensive-margin gains in export participation, whereas larger firms captured more stable efficiency and financial benefits. Digitally compatible and standardized product sectors exhibited stronger platform-linked outcomes than highly regulated or logistics-intensive sectors. Destination-market conditions moderated results significantly, as firms exporting to markets with higher logistics reliability and digital payment penetration realized stronger performance effects than those targeting institutionally weaker destinations. Overall, the study concluded that digital trade platforms primarily functioned as access-enabling mechanisms that reshaped export participation and market reach, while realized efficiency and financial outcomes depended on complementary firm capabilities, sectoral suitability, and destination-market enabling conditions.
Copyright (c) 2022 Mostafa Kamal, Md Tohidul Islam (Author)
Systematic Review of Calibration Technologies and their Impact on Safety in Global Critical Infrastructure
Calibration technologies play a critical yet often understated role in maintaining safety across global critical infrastructure systems that depend on accurate, stable, and trustworthy measurements for monitoring, control, and protection. This systematic review examines how calibration technologies influence safety performance across multiple infrastructure domains, including industrial and process facilities, energy and utility systems, nuclear infrastructure, transportation networks, and other safety-critical socio-technical systems. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, an initial pool of 1,247 records was identified through structured database searches and supplementary manual screening. After removing 386 duplicate records and excluding 727 articles during title and abstract screening, 134 peer-reviewed and technical papers were retained for full analysis. These studies span more than 25 years of publication, represent research conducted in over 30 countries, and collectively account for approximately 19,800 scholarly citations, indicating a robust and mature evidence base.Numerical synthesis of the findings shows that 109 of the 134 reviewed studies (81.3%) explicitly identify calibration as a prerequisite for the reliable functioning of safety-critical instrumentation, alarms, and control systems. Instrument degradation and drift are addressed in 97 studies (72.4%), with 63 studies (47.0%) documenting that drift-related measurement errors can remain latent for extended operational periods before detection. Calibration interval determination is discussed in 88 studies (65.7%), of which 54 studies (61.4%) report limitations of fixed time-based scheduling in adequately controlling safety risk. Advanced calibration approaches, including online monitoring, analytical redundancy, and model-based drift detection, are examined in 61 studies (45.5%); however, 44 of these studies (72.1%) emphasize that such approaches are most effective when used in combination with direct, traceable calibration rather than as standalone solutions. Safety incident analyses are included in 67 studies (50.0%), with 49 studies (73.1%) identifying measurement or calibration-related failures as contributing factors to unsafe conditions, near-miss events, or incident escalation. Additionally, 85 studies (63.4%) highlight deficiencies in calibration documentation, traceability, or governance as recurring precursors to measurement failure. Overall, the numerical findings confirm that calibration technologies function as foundational socio-technical safety enablers, with measurable influence on safety margins, barrier integrity, and decision reliability across interconnected global critical infrastructure systems.
Copyright (c) 2024 Efat Ara Haque (Author)
BLOCKCHAIN-ORCHESTRATED CYBER-PHYSICAL SUPPLY CHAIN NETWORKS WITH BYZANTINE FAULT TOLERANCE FOR MANUFACTURING ROBUSTNESS
This study quantitatively examined how blockchain orchestration and Byzantine fault tolerance (BFT) were associated with manufacturing robustness in cyber-physical supply chain networks under varying workload and fault conditions. A controlled, scenario-based experimental design was implemented using network-run–level simulation outputs and system log data, enabling systematic comparison across three coordination regimes: centralized coordination, non-BFT blockchain coordination, and BFT-enabled blockchain orchestration. The analytic sample comprised 360 validated network runs spanning low, medium, and high event loads and low, moderate, and elevated fault intensities. Manufacturing robustness was operationalized using downtime probability, throughput stability, schedule deviation, recovery-time behavior, service level variance, and inventory oscillation indicators, while consensus performance and data integrity were modeled as explanatory mechanisms. Descriptive findings showed that centralized coordination achieved the highest mean throughput (920.3 TPS) and lowest mean confirmation latency (1.12 seconds) but exhibited higher downtime probability (5.20%) and longer recovery time (49.0 minutes) under fault stress. BFT-enabled orchestration demonstrated lower throughput (547.0 TPS) and higher confirmation latency (4.71 seconds) but achieved superior robustness outcomes, including lower downtime probability (2.80%), reduced schedule deviation (14.3 minutes), and faster recovery (31.3 minutes). Correlation analysis indicated strong associations between consensus performance and robustness, with deadline adherence negatively correlated with downtime probability (r = −0.66) and recovery time (r = −0.65). Data integrity metrics were also strongly related to robustness, as data completeness showed a negative correlation with downtime probability (r = −0.61). Hierarchical regression results revealed that coordination regime and BFT configuration explained 31% of the variance in manufacturing robustness, which increased to 63% when consensus performance and data integrity were included. Mediation analysis showed that consensus performance and data integrity jointly accounted for a substantial portion of the architecture effect, with a total indirect effect of −0.21. Interaction models further indicated that the robustness advantages of BFT-enabled orchestration strengthened under higher workload and fault intensity. Overall, the findings demonstrated that manufacturing robustness in cyber-physical supply chains was shaped by coordination architecture through measurable performance and integrity mechanisms, providing quantitative evidence on how distributed trust and fault tolerance influenced operational stability under stress.
Copyright (c) 2023 S. M. Habibullah, Aditya Dhanekula (Author)
DATA-DRIVEN PROCESS OPTIMIZATION IN AUTOMOTIVE MANUFACTURING A MACHINE LEARNING APPROACH TO WASTE REDUCTION AND QUALITY IMPROVEMENT
Data-driven process optimization in automotive manufacturing was examined as a quantitative approach for reducing waste and improving product quality by linking integrated production, quality, maintenance, and batch-traceability data with measurable outcomes. The study was positioned through a structured review of 35 peer-reviewed papers and was executed as a retrospective case analysis using 48,720 eligible production-unit records joined by unit ID, station identity, and synchronized timestamps. Descriptive findings indicated scrap occurrence of 2.60% (n = 1,268), rework routing of 12.55% (n = 6,112), first-pass yield of 87.45% (n = 42,608), and defect occurrence of 6.15% (n = 2,994) at the selected inspection gate; defect cases were distributed across dimensional 36.0% (n = 1,077), weld 24.0% (n = 718), surface/paint 22.0% (n = 659), and functional 18.0% (n = 540) categories. Correlation analysis showed that rework hours per unit were positively associated with torque deviation (r = 0.46) and dimensional deviation (r = 0.41) and negatively associated with a sensor-based stability index (r = −0.38). Measurement reliability supported quantitative interpretation, including inspection label agreement of 0.97, defect-category consistency (κ = 0.78), and high repeatability for key trace-derived indicators (ICC = 0.89). Collinearity screening identified redundancy among correlated telemetry summaries, with maximum VIF reduced from 9.6 to 3.6 after consolidation. Regression results indicated higher defect odds with torque deviation (OR = 1.28, 95% CI [1.21, 1.35]) and dimensional deviation (OR = 1.42, 95% CI [1.31, 1.54]), while stability reduced defect odds (OR = 0.81, 95% CI [0.77, 0.85]). Block wise modeling showed improved explanatory power from context-only (adjusted R² = 0.08) to full models including process, equipment, and batch predictors (adjusted R² = 0.33), supporting an integrated quantitative framework for waste reduction and quality improvement.
Copyright (c) 2022 Md. Foysal Hossain, Subrato Sarker (Author)
IOT-INTEGRATED DEEP NEURAL PREDICTIVE MAINTENANCE SYSTEM WITH VIBRATION-SIGNAL DIAGNOSTICS IN SMART FACTORIES
This quantitative study examined an IoT-Integrated Deep Neural Predictive Maintenance System with Vibration-Signal Diagnostics in Smart Factories through an empirical evaluation informed by a structured review of 92 peer-reviewed journal articles and conference papers on vibration-based diagnostics, deep learning architectures, and industrial IoT deployment practices. Vibration data were acquired from 24 rotating assets operating across two smart-factory production lines at a sampling rate of 25.6 kHz and were segmented into 2.0-second windows with 50% overlap, producing 1,152,000 diagnostic windows and 38,400 prognostic sequences composed of 30 windows per sequence. The experimental design compared signal representation types (raw 1D, STFT, wavelet), model architecture families (1D CNN, temporal CNN, LSTM/GRU), inference placement (edge versus cloud), and data-quality conditions (baseline, noise, and missingness). Descriptive and inferential analyses showed that time–frequency representations consistently outperformed raw time-domain inputs for fault diagnosis and remaining useful life estimation. Wavelet-based cloud configurations achieved a macro F1 of 0.934 and PR-AUC of 0.953, compared with macro F1 of 0.881 and PR-AUC of 0.904 for raw 1D edge configurations. Prognostic accuracy followed a similar pattern, with wavelet-based cloud pipelines producing RUL MAE of 6.8 hours and RMSE of 9.9 hours, compared with 8.6 hours MAE and 12.4 hours RMSE for raw 1D edge pipelines. Mixed-effects regression confirmed statistically significant effects of representation type on diagnostic performance (wavelet versus raw, β = 0.041 for macro F1, p < 0.001) and significant degradation under missingness conditions (β = −0.034, p < 0.001). System-level analysis showed that cloud placement increased median latency by 102.6 ms, P95 latency by 181.2 ms, and bandwidth usage by 8.63 Mbps (p < 0.001), while sustaining higher throughput by 78.4 windows/s. Overall, the findings demonstrated that predictive accuracy and prognostic reliability were primarily governed by representation and architecture choices, whereas IoT placement chiefly determined latency and bandwidth behavior under realistic smart-factory streaming conditions.
Copyright (c) 2022 S. M. Habibullah, Md. Tahmid Farabe Shehun (Author)
COMPUTATIONAL THERMO-FLUID DYNAMICS MODELING FOR PROCESS OPTIMIZATION IN HYDROGEN-INTEGRATED INDUSTRIAL HEAT SYSTEMS
This study evaluated computational thermo-fluid dynamics modeling as a quantitative platform for process optimization in hydrogen-integrated industrial heat systems. A validated three-dimensional reacting CFD model was executed as a controlled numerical experiment across 60 design-of-experiments cases that spanned hydrogen volumetric shares from 0% to 100% (mean 52.3%, SD 30.1), oxidizer staging ratios from 0.50 to 0.90 (mean 0.70), swirl/mixing levels from 0.20 to 0.90 (mean 0.56), jet momentum ratios from 0.60 to 1.80 (mean 1.12), and recirculation rates from 0% to 25% (mean 11.8%). Extracted responses showed practical but non-uniform hydrogen effects: useful heat-transfer efficiency ranged from 71.2% to 86.5% (mean 79.8%, SD 4.1), load temperature spread increased markedly from 18.4°C to 72.9°C (mean 41.6°C, SD 13.8), peak wall temperature varied between 1125°C and 1328°C (mean 1224°C), chamber pressure drop rose from 185 to 412 Pa (mean 298 Pa), and NOx emission index spanned 0.38 to 1.54 g/MJ (mean 0.92 g/MJ). Correlation screening indicated strong positive associations between hydrogen share and NOx (r = 0.61), peak wall temperature (r = 0.52), and temperature spread (r = 0.47), while the link to efficiency was weak (r = 0.18). Reliability checks showed repeatability differences below 3% for all indicators and medium–fine mesh differences below 3.1%, confirming numerical stability. Multivariate regressions revealed that hydrogen share significantly increased NOx, wall hot spots, non-uniformity, and pressure drop, whereas oxidizer staging and swirl significantly mitigated these penalties; the hydrogen–staging interaction produced the largest NOx suppression. Feasible operating windows were therefore characterized by moderate-to-high hydrogen shares combined with elevated staging and swirl, preserving efficiency while meeting emissions, stability, pressure-loss, and material-temperature constraints.
Copyright (c) 2024 Saikat Sarkar (Author)
INTELLIGENT CLIMATE RISK MODELING FOR ROBUST ENERGY RESILIENCE AND NATIONAL SECURITY
This quantitative research investigates the integration of Artificial Intelligence (AI) into climate risk modeling to enhance national energy resilience and inform security planning. Climate risk modeling quantifies the probabilistic impacts of temperature anomalies, precipitation variability, and extreme weather events on energy systems, while AI provides the computational capacity to process multidimensional climatic and infrastructural datasets. Using a multi-site, longitudinal design and federated learning architecture, this study models the dynamic interdependencies among climate variables, grid performance, and defense-critical infrastructures. The results reveal strong correlations between temperature anomalies and outage frequency (r = 0.82, p < 0.01), as well as inverse relationships between precipitation variability and hydropower efficiency (r = –0.64, p < 0.05). Regression analysis indicates that climate predictors explain 71% of the variance in energy reliability outcomes (Adjusted R² = 0.71), which increases to 0.84 with the inclusion of AI-derived variables. Mediation testing demonstrates that energy resilience accounts for 42% of the indirect effect of climatic stressors on national security readiness. Reliability and validity assessments confirm strong internal consistency (Cronbach’s α > 0.85) and predictive accuracy exceeding 93%. These findings substantiate that AI-enhanced models outperform traditional statistical methods in forecasting energy system disruptions and quantifying resilience metrics. The research concludes that AI-driven climate modeling provides a scientifically verifiable foundation for anticipatory energy governance and national defense planning. Policy recommendations emphasize institutionalizing AI-based predictive analytics, establishing federated data-sharing infrastructures, and standardizing resilience metrics across critical energy networks. The study contributes to the evolving discipline of computational climate-security analytics by demonstrating how AI transforms environmental uncertainty into actionable intelligence for sustainable energy and national security systems.
Copyright (c) 2023 FNU Zulqarnain, Subrato Sarker (Author)
THE IMPACT OF SMART MATERIALS AND FIRE-RESISTANT STRUCTURES ON SAFETY IN U.S. PUBLIC INFRASTRUCTURE
This quantitative study examined how smart materials and fire-resistant structural systems influenced measurable fire-safety performance in U.S. public infrastructure. Using a cross-sectional comparative design grounded in performance-based fire engineering, the analysis evaluated bridges, tunnels, transit systems, and high-occupancy public buildings classified into baseline, smart-materials-only, fire-resistant-only, and combined-intervention conditions. Safety was operationalized as a multidimensional outcome family capturing structural survival and occupant tenability, including structural endurance time, residual load capacity after cooling, peak fire deflection, collapse-risk score, and evacuation tenability window. Descriptive results demonstrated a clear improvement gradient across intervention categories. Mean structural endurance increased from 78.4 minutes in baseline assets to 96.2 minutes in smart-materials-only assets and 104.7 minutes in fire-resistant-only assets, reaching 121.3 minutes in combined systems. Residual load capacity followed the same pattern, rising from 62.1% in baseline systems to 69.5% in smart-materials-only systems, 72.8% in fire-resistant-only systems, and 79.6% in combined systems. Peak fire deflection declined progressively from 41.7 mm in baseline assets to 35.2 mm under smart materials, 32.8 mm under fire-resistant structures, and 27.6 mm under combined assemblies. Collapse-risk scores were reduced from 0.34 in baseline systems to 0.27 in smart-materials-only systems, 0.24 in fire-resistant-only systems, and 0.18 in combined systems. Evacuation tenability windows expanded from 9.6 minutes in baseline assets to 11.4 minutes under smart materials, 12.2 minutes under fire-resistant structures, and 13.8 minutes under combined systems, with the largest tenability gains observed in tunnel, transit, and public-building assets. Inferential modeling confirmed that passive protection level and redundancy rating were the strongest predictors of structural endurance and collapse-risk reduction, while sensing responsiveness and compartment integrity were dominant predictors of tenability improvement. Interaction testing indicated statistically meaningful non-additive benefits for combined interventions, showing that integrated smart-material and fire-resistant assemblies corresponded to the most robust and stable safety outcomes across U.S. public infrastructure types.
Copyright (c) 2025 Masud Rana, Md Sarwar Hossain Shuvo (Author)
AI-ENHANCED MIS PLATFORMS FOR STRATEGIC BUSINESS DECISION-MAKING IN SMES
This study investigated how AI-enhanced management information systems (MIS) platforms influenced strategic business decision-making in small and medium-sized enterprises (SMEs) using a quantitative explanatory model grounded in a review of 40 peer-reviewed papers. Cross-sectional survey data were collected from 352 SMEs (70.4% usable response rate) representing services (33.5%), manufacturing (26.1%), retail/trade (21.0%), logistics (10.8%), and technology-enabled sectors (8.5%); 55.7% were small firms, 31.3% medium, and 13.1% micro. Descriptive results showed moderately high AI-MIS capability (M = 5.21, SD = 0.89), led by predictive analytics (M = 5.44) and automated insight/alerting (M = 5.36), while prescriptive recommendation was lower (M = 4.88). MIS integration quality was mid-range (M = 4.76, SD = 0.93), with breadth (M = 4.92) exceeding depth (M = 4.61). Managerial usage intensity was moderately high (M = 4.98), driven by usage frequency (M = 5.24) rather than scenario testing (M = 4.68). Strategic decision outcomes were positive overall (M = 5.06), strongest for decision speed (M = 5.28) and cross-functional alignment (M = 5.26), with weaker risk calibration (M = 4.72). Correlations supported theoretical associations: AI-MIS capability correlated with usage intensity (r = 0.71) and decision outcomes (r = 0.68), and integration quality correlated with decision outcomes (r = 0.60). Reliability and validity were strong (Cronbach’s α = 0.85–0.93; CR = 0.88–0.94; AVE = 0.52–0.68; HTMT ≤ 0.79), and collinearity was acceptable (VIF = 1.58–2.13). Regression results indicated significant direct effects of AI-MIS capability on decision outcomes (β = 0.41, t = 8.72, p < .001) and usage intensity (β = 0.58, t = 12.10, p < .001). Usage intensity predicted decision outcomes (β = 0.47, t = 10.24, p < .001) and partially mediated the capability–outcomes relationship (indirect β = 0.27, 95% CI [0.20, 0.35]; residual direct β = 0.14, p = .003). Integration quality independently improved decision outcomes (β = 0.26, p < .001) and moderated capability effects on usage (β = 0.12, p = .007) and outcomes (β = 0.11, p = .014). The final models explained 54% of variance in usage intensity and 62% in strategic decision outcomes.
Copyright (c) 2023 Aditya Dhanekula, M.A. Rony (Author)
ASSESSMENT OF DATA-DRIVEN VENDOR PERFORMANCE EVALUATION IN RETAIL SUPPLY CHAINS ANALYZING METRICS, SCORECARDS, AND CONTRACT MANAGEMENT TOOLS
This quantitative explanatory study assessed data-driven vendor performance evaluation in retail supply chains by integrating multi-KPI scorecards with downstream operational outcomes. The measurement architecture and variable logic were grounded in a structured review of 32 empirical papers on retail vendor evaluation, KPI taxonomies, scorecard construction, and outcome modeling, which informed the selection of delivery, quality, cost, and flexibility indicators. Operational data were compiled from ERP purchasing tables, WMS receipt logs, TMS carrier scans, POS demand records, supplier portal submissions, and claims databases. Of 312 vendors initially identified, 47 inactive vendors and 23 vendors with insufficient transaction histories were excluded, yielding 242 active vendors observed from January 2021 to December 2021. After data cleaning, 3,805 vendor–period records remained for analysis. Descriptive results showed strong average delivery performance (on-time delivery mean 91.4%, SD 6.8; order fill rate mean 94.1%, SD 5.2), while lead-time deviation displayed wider dispersion (mean 2.6 days, SD 1.9). Quality performance was generally stable but risk-concentrated (defect rate mean 1.9%, SD 1.4; return ratio mean 2.7%, SD 2.0), and flexibility exhibited the highest volatility (rush-order acceptance mean 76.8%, SD 14.9; recovery time mean 4.1 days, SD 3.3). Reliability tests supported dimensional consistency (Cronbach’s α 0.86 delivery, 0.83 quality, 0.79 cost, 0.76 flexibility; overall scorecard α 0.88). Correlation patterns confirmed KPI coherence (on-time delivery–fill rate r 0.74; defect rate–return ratio r 0.69) and strong alignment between vendor scores and retail outcomes (overall score–stockout r −0.58; overall score–shelf availability r 0.62). Driver models indicated that forecast variability (β −0.27, p < .001), distance (β −0.14, p = .010), and product complexity (β −0.17, p = .003) reduced vendor performance, while vendor size improved scores (β 0.18, p = .002). Outcome regressions demonstrated temporally ordered effects, with delivery strength reducing stockouts (β −0.36) and quality strength lowering return processing costs (β −0.29). Overall, the study showed that data-driven scorecards reliably differentiated vendor capability and explained meaningful variation in retail service and cost outcomes.
Copyright (c) 2022 Md. Abdur Rauf, Zamal Haider Shish (Author)
SMART HYBRID MANUFACTURING: A COMBINATION OF ADDITIVE, SUBTRACTIVE, AND LEAN TECHNIQUES FOR AGILE PRODUCTION SYSTEMS
Smart Hybrid Manufacturing (SHM)—the coordinated integration of additive, subtractive, lean, and smart control capabilities—has emerged as a central pathway for enabling responsive, high-quality, and resource-efficient production in modern manufacturing systems. Yet, empirical evidence explaining how these multidimensional capability foundations contribute to productivity, quality, efficiency, and agility remains limited. This study addresses this gap by developing and testing a comprehensive capability–performance model using data from a heterogeneous sample of industrial plants and hybrid manufacturing cells operating across multiple sectors, complexity classes, and volatility conditions. The research examines how additive and subtractive process maturity, lean–smart integration governance, and cyber-physical smart control jointly shape agile production system performance (APSP), while also assessing the mediating role of smart control capability and the moderating role of lean maturity. Additional multi-group comparisons evaluate whether SHM performance impacts differ across hybridization architectures and environmental turbulence. Measurement model evaluation demonstrated strong psychometric robustness. All constructs achieved acceptable levels of internal consistency (Cronbach’s α ≥ 0.85; CR ≥ 0.90), convergent validity (AVE ≥ 0.70), and discriminant validity across Fornell–Larcker, HTMT, and CFA criteria. The overall measurement structure supported a multidimensional representation of SHM capability, as well as second-order factors for SHM Capability (SHMC) and Agile Production System Performance (APSP). Model fit indices (CFI = 0.957; TLI = 0.948; RMSEA = 0.045; SRMR = 0.037) indicated strong alignment between data and the hypothesized measurement structure. Structural equation modeling results provided strong support for the hypothesized relationships. SHM Capability exhibited a significant and substantial effect on APSP (β = 0.62), explaining more than half of the observed variance in agile system performance. Each SHM sub-dimension demonstrated its expected directional relationship with specific performance outcomes: additive maturity predicted agility, subtractive finishing predicted quality, and lean–smart integration predicted productivity and efficiency. Smart Control Maturity partially mediated the SHM → quality relationship, confirming that in-situ sensing and closed-loop correction serve as essential mechanisms for enabling conformance and reducing variability. Lean maturity significantly moderated the SHM → productivity and SHM → agility relationships, indicating that SHM benefits are amplified under strong pull-flow discipline and structured continuous improvement.
Copyright (c) 2023 Md Al Amin Khan, Md Mesbaul Hasan (Author)
INTEGRATION OF IOT AND EDGE COMPUTING FOR LOW-LATENCY DATA ANALYTICS IN SMART CITIES AND IOT NETWORKS
This quantitative, cross-sectional, case-based study investigates how the integration of Internet of Things (IoT) infrastructures with edge computing architectures enhances low-latency data analytics and improves the quality of smart city services. Although smart city ecosystems continue to expand globally, a persistent challenge lies in their reliance on cloud-centric analytics models, which often struggle to satisfy stringent latency, reliability, and responsiveness requirements associated with time-sensitive public services such as traffic control, emergency response, environmental monitoring, and utility management. The central problem addressed in this study is that traditional cloud-dependent analytics pipelines frequently introduce processing delays and network congestion, thereby constraining the ability of municipalities and smart service operators to deliver real-time, high-quality services. This study therefore aims to provide empirical evidence, grounded in real IoT edge deployment scenarios across cloud and enterprise environments, regarding how integration quality and underlying infrastructure conditions shape latency outcomes and perceived service performance. Data were collected using a structured questionnaire administered to professionals directly involved in smart city, IoT systems design, and network infrastructure projects. Out of 250 distributed surveys, 200 valid responses were obtained, yielding an 80 percent usable response rate suitable for inferential analyses. Key constructs—including IoT Edge Integration Quality, Network Infrastructure Quality, System Reliability, Low Latency Analytics Performance, Smart City Service Quality, Adoption or Optimization Intention, and Perceived Integration Challenges—were measured using multi-item Likert scales. Analytical procedures included descriptive statistics, reliability and validity testing, Pearson correlations, and multiple regression modeling to examine predictive relationships among the variables. The regression models accounted for 56 percent of the variance in low-latency analytics performance and 61 percent of the variance in smart city service quality. IoT edge integration quality (β = 0.39), system reliability (β = 0.26), and network infrastructure quality (β = 0.17) emerged as significant predictors of low-latency analytics, underscoring the combined importance of architectural coherence, dependable system behavior, and communication efficiency. Furthermore, low-latency analytics performance demonstrated a strong positive effect on smart city service quality (β = 0.37), highlighting latency reduction as a key mechanism for improving user experience, operational responsiveness, and service effectiveness.
Copyright (c) 2023 Md Harun-Or-Rashid Mollah, Mst. Shahrin Sultana, Sai Praveen Kudapa (Author)
IOT INTEGRATION IN INTELLIGENT LUBRICATION SYSTEMS FOR PREDICTIVE MAINTENANCE AND PERFORMANCE OPTIMIZATION IN ADVANCED MANUFACTURING INDUSTRIES
This study investigates how the integration of Internet of Things (IoT) technologies into intelligent lubrication systems enhances predictive maintenance capability and supports performance optimization within advanced manufacturing industries. The research addresses a critical empirical gap: although Maintenance 4.0 frameworks widely highlight the strategic role of smart lubrication and sensor-embedded subsystems, there remains insufficient quantitative evidence regarding how IoT-enabled lubrication solutions influence plant-level operational outcomes such as equipment availability, unplanned downtime, throughput stability, and overall equipment effectiveness (OEE). To respond to this gap, the study develops and empirically tests a conceptual model that links IoT integration, predictive maintenance effectiveness, and multidimensional performance outcomes in cloud-connected, data-driven manufacturing environments. A quantitative, cross-sectional, case-based survey design was adopted, utilizing a structured five-point Likert questionnaire administered to maintenance, reliability, production, and engineering professionals working in plants that have deployed IoT-driven lubrication and condition monitoring technologies. A total of 210 usable responses were collected, reflecting an effective response rate of 80.8 percent and representing diverse industrial contexts with varying levels of digital maturity. The model incorporated key variables including IoT integration in intelligent lubrication systems, predictive maintenance effectiveness, operational performance outcomes, and user acceptance—recognized in technology adoption theory as a critical enabler of system effectiveness. Data analysis involved multiple stages, beginning with descriptive statistics and reliability assessments to verify internal consistency across constructs, followed by Pearson correlation analysis to establish initial relationships among variables. A series of multiple regression models and mediation–moderation analyses, supplemented with bootstrapping procedures, were employed to rigorously test the hypothesized relationships. Results indicated that IoT integration exerted a strong and statistically significant positive effect on predictive maintenance effectiveness (β = 0.66, R² = 0.54), confirming that advanced, sensor-enabled lubrication systems materially enhance a plant’s capability to anticipate equipment failures and prevent lubrication-related anomalies. Further, IoT integration and predictive maintenance jointly accounted for 63 percent of the variance in multidimensional performance outcomes, highlighting the centrality of intelligent lubrication systems as a technological lever for improving OEE and reliability-centered performance metrics. Mediation results showed that predictive maintenance partially mediated the IoT–performance relationship (indirect effect = 0.34), indicating that IoT-generated condition data translate into performance gains primarily through improved diagnostic and prognostic capabilities. Additionally, user acceptance significantly moderated the IoT–predictive maintenance pathway, demonstrating that even sophisticated digital lubrication and monitoring architectures require high levels of user readiness, trust, and operational engagement to deliver their full value.
Copyright (c) 2023 Zobayer Eusufzai (Author)
INTEGRATING INDUSTRIAL ENGINEERING AND PETROLEUM SYSTEMS WITH LINEAR PROGRAMMING MODEL FOR FUEL EFFICIENCY AND DOWNTIME REDUCTION
This study addresses the persistent operational challenges of high fuel consumption and costly downtime in petroleum refinery environments, focusing on how the coordinated application of industrial engineering practices and linear programming (LP)–based optimisation can jointly improve these outcomes. Refinery operations typically involve complex, multistage conversion processes that are prone to inefficiencies arising from suboptimal process routing, equipment degradation, and inconsistent production planning. Motivated by these systemic issues, the present research adopts a quantitative cross-sectional, case-based approach conducted in a single enterprise refinery, integrating perceptual and objective data sources. Survey responses were collected from a diverse sample of 120 refinery personnel—including engineers, operators, maintenance technicians, and managerial staff—capturing organisational practices and capabilities related to production engineering and operational management. These perceptual constructs were complemented with plant-level operational records, including historical data on fuel consumption, throughput volumes, and downtime frequency and duration, thereby enabling a comprehensive evaluation of both human-centred practices and technical performance indicators. The study focused on four key explanatory variables: Industrial Engineering Practices, Energy-Management Capability, Maintenance and Reliability Practices, and LP-Based Optimisation Capability. Each construct was operationalised using multiple items measured on five-point Likert scales to reflect employees’ assessments of procedural integration, resource utilisation, reliability culture, and modelling proficiency. The outcome variables were derived directly from refinery performance data: a Fuel-Efficiency Index calculated from total fuel consumed relative to throughput, and a Downtime Ratio reflecting the share of operational hours lost to equipment or process failures. The analytical strategy proceeded in several stages. First, descriptive statistics established baseline distributions of the constructs and performance metrics. Second, Pearson correlation analyses identified initial associations between practice constructs and operational outcomes. Third, multiple regression models were estimated to quantify the individual and combined predictive effects of the four practice constructs on fuel efficiency and downtime. The regression findings indicated that all four explanatory constructs significantly predicted the Fuel-Efficiency Index, collectively explaining 39 percent of its variance (R² = 0.39). This suggests that production planning discipline, energy-conscious operating behaviour, robust maintenance routines, and modelling capability have measurable and complementary effects on refinery energy performance. For the Downtime Ratio, the analysis revealed that Maintenance and Reliability Practices, Industrial Engineering Practices, and LP-Based Optimisation Capability were statistically significant predictors, jointly explaining 42 percent of the variance (R² = 0.42). These results reinforce the influential role of reliability-centred maintenance, structured engineering interventions, and model-informed decision-making in reducing operational disruptions and system bottlenecks.
Copyright (c) 2023 Sabuj Kumar Shil (Author)
VEHICLE-TO-INFRASTRUCTURE (V2I) COMMUNICATION AND TRAFFIC INCIDENT REDUCTION: AN EMPIRICAL STUDY ACROSS U.S. HIGHWAY NETWORKS
Vehicle-to-Infrastructure (V2I) communication has emerged as a critical component of intelligent transportation systems designed to enhance roadway safety, operational efficiency, and real-time decision-making across highway networks. This empirical study examines the extent to which V2I-enabled information exchange—particularly involving roadside units, traffic management centers, and connected vehicle systems—contributes to measurable reductions in traffic incidents across major U.S. highways. Drawing on multi-state datasets that integrate incident logs, roadway sensor feeds, and V2I communication records, the analysis assesses correlations between V2I deployment density and decreases in crash frequency, severity, and secondary collisions. Advanced statistical modeling and spatiotemporal analysis reveal that highways with mature V2I infrastructure experience significantly improved driver hazard awareness, reduced response lag for incident management teams, and smoother traffic flow patterns under high-volume conditions. Additionally, the findings highlight regional disparities in V2I effectiveness influenced by infrastructure investment levels, network design, and operational integration with legacy systems. The study contributes to the growing body of empirical evidence demonstrating the tangible safety benefits of connected transportation ecosystems and underscores the importance of policy alignment, sustained infrastructure funding, and interoperable communication standards for maximizing V2I’s impact on national roadway safety outcomes. Beyond its empirical contributions, this study reinforces growing national and international evidence supporting the safety benefits of connected transportation ecosystems. It highlights the critical need for continued infrastructure funding, harmonized policy frameworks, and interoperable communication standards to ensure that V2I technologies operate cohesively across jurisdictions and platforms. Ultimately, the expanded insights offered here illustrate how robust V2I deployment not only mitigates crash risks but also strengthens the overall resilience and efficiency of the transportation network, positioning connected vehicle technologies as an essential component of future roadway safety strategies.
Vehicle-to-Infrastructure (V2I) Communication
Copyright (c) 2025 Md Asfaquar Rahman (Author)