International Journal of Scientific Interdisciplinary Research
type: channel
description: The International Journal of Scientific Interdisciplinary Research (IJSIR) is a prestigious, peer-reviewed, open-access international journal devoted to advancing the frontiers of interdisciplinary scholarship. IJSIR provides a dynamic platform for researchers, academicians, scientists, practitioners, and policymakers from diverse fields to collaborate and share innovative ideas that transcend conventional disciplinary boundaries. The journal is dedicated to publishing cutting-edge, original research articles, critical reviews, and case studies that offer novel insights and practical relevance in a rapidly evolving global research environment. With a focus on fostering integrative and cross-disciplinary discourse, International Journal of Scientific Interdisciplinary Research (IJSIR) covers a broad spectrum of scientific domains, including but not limited to Physical and Life Sciences, Engineering, Environmental Science, Information Technology, Social Sciences, Economics, Education, and Healthcare. The journal encourages submissions that explore the convergence of knowledge across disciplines, offering transformative solutions to complex scientific, technological, and societal challenges.
publisher: International Journal of Scientific Interdisciplinary Research
language: en
publication name: International Journal of Scientific Interdisciplinary Research
items: MITRE ATT&CK–Driven Threat Hunting in A SOC Environment: a Real-World Case Study Using SPLUNK Correlation Rules
items: Integrating Psychometric and Neurocognitive Biomarkers in Computational Models to Predict Cognitive Behavioral Therapy Outcomes in Adolescents with Anxiety and Depression
items: AI-Supported Agricultural Information Systems for National Yield Forecasting and Food Price Stabilization in the United States: A Mixed-Methods Investigation
items: AI-Driven Customer Behavior Modeling for Performance-Based Digital Marketing Systems
items: Implementation of Explainable AI-Driven Framework for Supply Chain Optimization: A Practical Case Study in Industrial Engineering
items: Artificial Intelligence Enabled Predictive Safety Analytics for Proactive Hazard Identification in Industrial Manufacturing Environments
items: The Impact of Explainable AI On EHR-Based Clinical Risk Prediction: A Quantitative Evaluation of Transparency and Diagnostic Accuracy
items: Climate Security, Water Governance, and Resilience How Climate-Induced Water Stress and Migration Challenge State Capacity
items: Artificial Intelligence Based Predictive Modeling for Structural Health Monitoring and Failure Risk Assessment in Water and Sanitation Infrastructure
items: AI-Driven Cybercrime Forensics for Predictive Threat Detection and Investigative Intelligence
items: Design and Optimization of End-To-End Artificial Intelligence and Machine Learning Pipelines in CI/CD-Enabled Cloud Infrastructures
items: Privacy-Preserving Federated Learning for Critical Infrastructure: A Systematic Review of Security Threat Models, Deployment Patterns, And Governance
items: Machine Learning Based Data Optimization of Merchant Onboarding Processes in Fintech Ecosystems: A Time-Series and Process-Mining Study
items: Machine Learning–Based Transaction Risk Scoring Models for Financial Compliance Monitoring in Foreign Exchange Operations
items: AI-Enabled Decision Support Systems for Service Operations an Analytical Modeling and IT Strategy Framework
items: Quantitative AI-Based Load-Flow and Fault-Prediction Modeling for Reliability Enhancement in Electrical Distribution Networks
items: Electrical Engineering Approaches for AI-Enabled Autonomous Robotics in U.S. Industrial and Defense Applications
items: AI-Based Revenue Leakage Detection Models Using Transaction-Level Financial Data: A Review
items: AI-Driven Change Detection Using SAR, LIDAR, And Sentinel-2 Data for Landslide Monitoring and Disaster Early Warning Systems
items: Predictive Risk Modeling and Quantitative Analysis of Microbial Contamination in Hospital Textiles
items: Risk Mitigation and Resilience Modeling for Consumer Distribution Networks During Demand Shocks: A Quantitative Stochastic Optimization and Scenario Analysis Study
items: ERP Modernization Outcomes in Cloud Migration: A Meta-Analysis of Performance and Total Cost of Ownership (TCO) Across Enterprise Implementations
items: A Meta-Analysis of OSHA Safety Training Programs and their Impact on Injury Reduction and Safety Compliance in U.S. Workplaces
items: IMPROVING PROJECT LIFECYCLE MANAGEMENT (PLM) EFFICIENCY WITH CLOUD ARCHITECTURES AND CAD INTEGRATION AN EMPIRICAL STUDY USING INDUSTRIAL CAD REPOSITORIES AND CLOUD‑NATIVE WORKFLOWS
items: CYBERSECURE DOCUMENTATION AND RECORD-KEEPING PROTOCOLS FOR SAFEGUARDING SENSITIVE FINANCIAL INFORMATION ACROSS BUSINESS OPERATIONS
items: DIGITAL TWIN–ENABLED OPTIMIZATION OF ELECTRICAL, INSTRUMENTATION, AND CONTROL ARCHITECTURES IN SMART MANUFACTURING AND UTILITY-SCALE SYSTEMS
items: PREDICTIVE ANALYTICS AS A STRATEGIC TOOL FOR FINANCIAL FORECASTING AND RISK GOVERNANCE IN U.S. CAPITAL MARKETS
items: ADVANCED ENGINEERING MATERIALS AND PERFORMANCE-BASED DESIGN FRAMEWORKS FOR RESILIENT RAIL-CORRIDOR INFRASTRUCTURE
items: QUANTITATIVE STUDY ON MACHINE LEARNING-BASED INDUSTRIAL ENGINEERING APPROACHES FOR REDUCING SYSTEM DOWNTIME IN U.S. MANUFACTURING PLANTS
items: INTEGRATING ERP, IOT, AND AI FOR SMART WAREHOUSE AUTOMATION AND LOGISTICS

MITRE ATT&CK–Driven Threat Hunting in A SOC Environment: a Real-World Case Study Using SPLUNK Correlation Rules
type: item
description: This study investigates the effectiveness of MITRE ATT&CK driven threat hunting in a Security Operations Center environment through the use of Splunk correlation rules, with the central problem being that modern SOCs often struggle with excessive alert volume, false positives, incomplete adversary visibility, and delayed response in complex cloud and enterprise security environments. The purpose of the study was to determine whether ATT&CK aligned threat hunting and better engineered Splunk correlation rules improve detection effectiveness, triage efficiency, response efficiency, and overall threat hunting success. The research adopted a quantitative, cross sectional, case-based design using a structured five-point Likert scale questionnaire administered to 120 cybersecurity professionals drawn from cloud and enterprise SOC cases, including SOC analysts, threat hunters, incident responders, detection engineers, and SOC managers. The key independent variables were MITRE ATT&CK alignment, Splunk correlation rule quality, technique coverage adequacy, and precision noise balance, while the dependent variables were threat detection effectiveness, triage efficiency, response efficiency, and overall threat hunting success. Data analysis was conducted using descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression. The findings showed that all major constructs recorded positive mean scores above 3.00, including MITRE ATT&CK alignment (M = 4.18, SD = 0.61), Splunk rule quality (M = 4.09, SD = 0.66), threat detection effectiveness (M = 4.16, SD = 0.58), and overall threat hunting success (M = 4.12, SD = 0.60). Reliability values were satisfactory, with Cronbach’s alpha ranging from 0.78 to 0.86. Significant positive relationships were found between ATT&CK alignment and threat detection effectiveness (r = .68, p < .001), Splunk rule quality and triage efficiency (r = .63, p < .001), technique coverage adequacy and SOC performan…
subject: Articles
  topic: section
subject: MITRE ATT&CK
  topic: keywords
subject: Threat hunting
  topic: keywords
subject: Security Operations Center
  topic: keywords
subject: SPLUNK correlation rules
  topic: keywords
subject: Detection effectiveness
  topic: keywords
creator: MD Zahedul Islam
creator: Aditya Dhanekula
rights: Copyright (c) 2024 MD Zahedul Islam, Aditya Dhanekula (Author)
date: 2024-06-30
publication date: 2024-06-30
starting page: 678
ending page: 719
doi: 10.63125/9gf98485

Integrating Psychometric and Neurocognitive Biomarkers in Computational Models to Predict Cognitive Behavioral Therapy Outcomes in Adolescents with Anxiety and Depression
type: item
description: This study investigated the predictive relationships between psychometric indicators and neurocognitive biomarkers in determining Cognitive Behavioral Therapy (CBT) outcomes among adolescents diagnosed with anxiety and depressive disorders. A quantitative prospective longitudinal research design was employed to examine how psychological and cognitive factors contributed to variability in treatment response. The study sample consisted of 120 adolescents aged between 12 and 18 years who were receiving structured CBT interventions in outpatient clinical settings. Data were collected at pretreatment, midpoint, and posttreatment stages using standardized psychometric scales and neurocognitive assessment tasks. Psychometric variables included anxiety severity, depressive symptoms, cognitive distortions, behavioral avoidance, emotional regulation difficulties, and resilience, while neurocognitive variables included attention bias, executive control, working memory performance, cognitive flexibility, emotional reactivity, and reward sensitivity. Descriptive analysis indicated substantial reductions in symptom severity over the course of treatment, with mean anxiety scores decreasing from 31.42 (SD = 6.85) at pretreatment to 17.63 (SD = 5.27) at posttreatment, while depressive symptoms declined from 28.73 (SD = 7.11) to 16.84 (SD = 5.89). Approximately 65% of participants were classified as treatment responders, demonstrating clinically significant improvement following CBT. Correlation analysis revealed significant relationships between psychological variables and treatment outcomes. Behavioral avoidance (r = -0.37, p < 0.01) and emotional regulation difficulties (r = -0.33, p < 0.01) were negatively associated with treatment improvement, whereas resilience demonstrated a positive correlation with treatment outcomes (r = 0.36, p < 0.01). Hierarchical regression analysis indicated that psychometric variables explained 39% of the variance in CBT outcomes (R² = 0.39, p < 0.001…
subject: Articles
  topic: section
subject: Cognitive Behavioral Therapy
  topic: keywords
subject: Adolescent Mental Health
  topic: keywords
subject: Psychometric Predictors
  topic: keywords
subject: Neurocognitive Biomarkers
  topic: keywords
subject: Treatment Outcomes
  topic: keywords
creator: Amena Begum Sumi
creator: Mst Kaniz Fatema
rights: Copyright (c) 2024 Amena Begum Sumi, Mst Kaniz Fatema (Author)
date: 2024-06-30
publication date: 2024-06-30
starting page: 632
ending page: 677
doi: 10.63125/7t7wmp27

AI-Supported Agricultural Information Systems for National Yield Forecasting and Food Price Stabilization in the United States: A Mixed-Methods Investigation
type: item
description: AI-supported agricultural information systems have emerged as critical tools for improving crop yield forecasting and strengthening food price monitoring in modern agricultural economies. This study investigated the effectiveness of artificial intelligence–driven predictive analytics in enhancing national yield forecasting accuracy and examining its relationship with food price stability in the United States. A quantitative longitudinal design was applied using integrated datasets consisting of historical crop yield records, climatic indicators, satellite-derived vegetation indices, and national food price series. Machine learning models including Random Forest, Gradient Boosting, and Support Vector Regression were compared with conventional regression-based forecasting models. The analysis incorporated cross-validation and out-of-sample testing to evaluate predictive reliability and model generalization across multiple production seasons. The findings demonstrated that AI-supported forecasting models significantly outperformed traditional statistical approaches. Regression models produced prediction errors ranging between 4.0% and 5.0% across the analyzed years, whereas machine learning models reduced prediction errors to approximately 1.1%–1.2%, indicating a substantial improvement in forecasting precision. Model comparison results showed that Gradient Boosting achieved the highest predictive performance with an R² value of 0.88 and the lowest root mean square error (RMSE) of 4.02 compared with 6.84 in linear regression models. Environmental predictors played a critical role in forecasting performance, with satellite-derived vegetation indices demonstrating the strongest standardized effect size (β = 0.47, p < 0.001), followed by rainfall variability (β = 0.32, p < 0.001) and temperature variability (β = 0.21, p < 0.05). Regional analysis further indicated that AI-based forecasting systems improved predictive accuracy by approximately 25%–34% across major agricult…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Crop Yield Forecasting
  topic: keywords
subject: Agricultural Information Systems
  topic: keywords
subject: Food Price Stability
  topic: keywords
subject: Remote Sensing
  topic: keywords
creator: Md. Mehedi Hasan
rights: Copyright (c) 2025 Md. Mehedi Hasan (Author)
date: 2025-06-30
publication date: 2025-06-30
starting page: 536
ending page: 575
doi: 10.63125/wtx8g375

AI-Driven Customer Behavior Modeling for Performance-Based Digital Marketing Systems
type: item
description: This study addresses a practical problem in performance-based digital marketing: organizations increasingly deploy AI models to predict and influence customer behavior, yet reported improvements in conversion efficiency and ROI are inconsistent because data signals, activation decisions, and measurement logic vary widely across cloud marketing stacks and enterprise platforms. The purpose of this research is to quantify, compare, and explain what AI-driven customer behavior modeling approaches work best, under what data conditions, and with which evaluation designs, using a quantitative cross-sectional, case-based synthesis. The sample comprises N = 45 peer-reviewed cloud and enterprise cases (2005–2023) drawn from marketing systems implemented across contexts such as e-commerce conversion optimization, subscription and SaaS retention, mobile funnels, and omnichannel operations. Key variables include AI technique family (supervised ML, deep learning or sequence models, recommenders, causal or uplift models, bandits or reinforcement learning), signal strategy (RFM and value features, clickstream and session features, exposure intensity, context and creative features, multi-source integration), activation lever (targeting, bidding and budget allocation, creative selection, timing and frequency control, lifecycle messaging), and measurement approach (observational attribution vs quasi-experimental incrementality vs experimental lift tests). The analysis plan applies descriptive frequency statistics, structured vote-counting of KPI direction, and a 5-point Likert evidence-support scoring to test hypotheses about performance lift, multi-source advantage, incrementality alignment, and governance effects. Headline findings show supervised ML as the most prevalent technique (31/45, 68.9%), while deep learning or sequence models appear in 19/45 (42.2%) and causal or uplift modeling in 12/45 (26.7%); overall, 33/45 studies (73.3%) report positive KPI movement attributable to A…
subject: Articles
  topic: section
subject: AI-Driven Customer Behavior Modeling
  topic: keywords
subject: Performance-Based Digital Marketing
  topic: keywords
subject: Incrementality Measurement
  topic: keywords
subject: Multi-Source Data Integration
  topic: keywords
subject: Uplift and Causal Modeling
  topic: keywords
creator: Khairum Nahar Pinky
rights: Copyright (c) 2026 Khairum Nahar Pinky (Author)
date: 2026-03-08
publication date: 2026-03-08
starting page: 453
ending page: 492
doi: 10.63125/d3dx1b71

Implementation of Explainable AI-Driven Framework for Supply Chain Optimization: A Practical Case Study in Industrial Engineering
type: item
description: Supply chain optimization remains one of the most complex and data-intensive challenges in contemporary industrial engineering, requiring decision-making frameworks capable of processing high-dimensional operational data while maintaining the transparency and interpretability necessary for practitioner trust and organizational adoption. This empirical study presents the design, implementation, and evaluation of an Explainable Artificial Intelligence (XAI)-driven framework for supply chain performance optimization within a real-world industrial engineering context, addressing the critical gap between advanced predictive model accuracy and actionable operational decision support. A case study methodology was employed within a mid-sized manufacturing organization operating across multiple supply chain tiers, where historical operational data comprising 24 months of procurement, inventory, logistics, and demand records were collected, preprocessed, and used to train a hybrid machine learning model combining XGBoost and Random Forest algorithms for supply chain performance prediction. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) techniques were systematically integrated into the predictive framework to generate transparent, human-understandable explanations of model outputs, enabling supply chain managers and industrial engineers to identify the operational variables most significantly influencing inventory inefficiency, delivery delays, and procurement cost overruns. The implemented framework achieved a supply chain disruption prediction accuracy of 94.3 percent, a demand forecasting mean absolute percentage error of 6.7 percent, and an inventory optimization improvement of 28.4 percent compared to the organization's existing rule-based planning system, demonstrating the substantial operational performance benefits of XAI-driven analytical approaches in industrial supply chain environments. Qualitative evaluation throug…
subject: Articles
  topic: section
subject: Keywords
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Supply Chain Optimization
  topic: keywords
subject: XGBoost
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Industrial Engineering
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Sayeda Sufia Sumi
rights: Copyright (c) 2025 Sayeda Sufia Sumi (Author)
date: 2025-12-16
publication date: 2025-12-16
starting page: 143
ending page: 172
doi: 10.63125/pacw7437

Artificial Intelligence Enabled Predictive Safety Analytics for Proactive Hazard Identification in Industrial Manufacturing Environments
type: item
description: This study quantitatively evaluated the effectiveness of an artificial intelligence–enabled predictive safety analytics system for proactive hazard identification in an industrial manufacturing environment. A quasi-experimental design combining interrupted time-series analysis, difference-in-differences estimation, hierarchical regression, and multilevel modeling was employed to assess whether AI-generated predictive risk scores contributed to measurable improvements in both leading and lagging safety outcomes. The analytic dataset included 2,184 line–shift observations and 312 workforce respondents across intervention and comparison production units. Descriptive results indicated that AI-generated risk scores decreased from a pre-implementation mean of 0.52 (SD = 0.16) to 0.41 (SD = 0.15) post-implementation (t = 8.74, p < .001). Exposure duration in high-risk zones declined from 81.2 to 69.3 minutes per shift (p < .001), and anomaly frequency decreased from 3.91 to 2.96 events per shift (p < .001). Near-miss frequency was reduced from 2.94 to 2.21 per 200,000 exposure hours (p = .002), while recordable incident rates declined from 3.81 to 3.05 (p = .015). Hierarchical regression analysis demonstrated that AI-generated risk scores significantly increased explained variance in leading safety indicators from R² = .055 to R² = .269. Multilevel modeling further showed that exposure duration (β = .29, p < .001) and anomaly frequency (β = .34, p < .001) significantly predicted near-miss outcomes. Interrupted time-series analysis revealed an immediate post-intervention incident reduction of −0.68 incidents (p = .004), with a sustained downward trend thereafter. Reliability testing confirmed strong internal consistency across safety constructs (Cronbach’s alpha range = .81–.89). Collectively, these findings provided statistically robust evidence that AI-enabled predictive safety analytics significantly enhanced proactive hazard detection and contributed to measurable reduc…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Safety Analytics
  topic: keywords
subject: Proactive Hazard Identification
  topic: keywords
subject: Industrial Manufacturing
  topic: keywords
subject: Occupational Safety
  topic: keywords
creator: Md Abubakar Siddique Akash
rights: Copyright (c) 2026 Md Abubakar Siddique Akash (Author)
date: 2026-03-02
publication date: 2026-03-02
starting page: 416
ending page: 452
doi: 10.63125/vp0mpx51

The Impact of Explainable AI On EHR-Based Clinical Risk Prediction: A Quantitative Evaluation of Transparency and Diagnostic Accuracy
type: item
description: This study quantitatively evaluated the impact of explainable artificial intelligence (XAI) on electronic health record (EHR)-based clinical risk prediction by comparing diagnostic accuracy, probability reliability, transparency stability, and clinician-centered utility within a controlled paired design. A retrospective cohort of 12,480 index patient-encounter observations was constructed from a hospital-based EHR system, with 3,744 encounters allocated to the independent test set. Identical feature engineering pipelines, cohort definitions, and data splits were applied to both non-explainable baseline models and explainable models augmented with structured explanation mechanisms. Diagnostic performance was assessed using discrimination, precision-oriented metrics, calibration summaries, and threshold-based error profiles. Transparency was evaluated using global and local explanation concentration indices and stability measures across 20 repeated training runs and controlled perturbations. The explainable model demonstrated higher discrimination performance (mean 0.826, SD 0.016) compared to the baseline model (mean 0.812, SD 0.018), along with improved precision-sensitive performance (0.447 vs. 0.421). Calibration slope improved from 0.91 to 0.97, and false-negative rate decreased from 18.6% to 16.8% at the prespecified operating threshold. Explanation stability was high, with a mean rank correlation of 0.91 across repeated runs. Clinician-centered evaluation (n = 64) showed strong internal reliability (Cronbach’s alpha range: 0.86–0.91) and high comprehension accuracy (84.3%, SD 6.7). Regression analysis indicated that the explainable condition significantly predicted improved discrimination (B = 0.014, 95% CI 0.006–0.022, p = 0.001) and increased odds of correct classification (OR = 1.28, p = 0.002). Explanation clarity significantly predicted clinician adoption proxy scores (B = 0.37, p < 0.001). Overall, findings demonstrated that explainability integration was…
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Clinical Risk Prediction
  topic: keywords
subject: Diagnostic Accuracy
  topic: keywords
subject: Transparency Metrics
  topic: keywords
creator: Hisham Mahmood
creator: Khairum Nahar Pinky
rights: Copyright (c) 2024 Hisham Mahmood, Khairum Nahar Pinky (Author)
date: 2024-06-30
publication date: 2024-06-30
starting page: 593
ending page: 631
doi: 10.63125/vepxg976

Climate Security, Water Governance, and Resilience How Climate-Induced Water Stress and Migration Challenge State Capacity
type: item
description: This study examined how climate-induced water stress and migration pressures jointly influenced state capacity, and whether water governance quality and resilience capacity buffered institutional strain. A longitudinal panel dataset covering 148 sovereign states over 20 years (N = 2,960 country-year observations) was analyzed using fixed-effects regression models with cluster-robust standard errors. Climate-induced water stress was found to have a statistically significant negative association with state capacity (β = −0.21, p < .001), particularly within fiscal and service delivery domains. Migration pressure also demonstrated a significant negative effect (β = −0.17, p = .005), with stronger impacts observed in administrative coordination indicators. The interaction between water stress and migration was negative and significant (β = −0.11, p = .006), indicating that institutional strain intensified under compound stress conditions. Marginal effects analysis showed that the impact of water stress increased from −0.18 at low migration levels to −0.29 at high migration levels. Moderation analysis revealed that stronger water governance reduced the magnitude of the water stress effect (β = 0.09, p = .025), while resilience capacity significantly buffered compound pressure (β = 0.12, p = .003). The within-model R² increased from 0.47 in the baseline model to 0.57 in moderation models. Findings demonstrated that environmental scarcity and demographic redistribution operated both independently and interactively to challenge state capacity, while governance quality and resilience functioned as measurable institutional buffers.
subject: Articles
  topic: section
subject: Climate Security
  topic: keywords
subject: Water Governance
  topic: keywords
subject: Resilience
  topic: keywords
subject: Migration
  topic: keywords
subject: State Capacity
  topic: keywords
creator: FNU Zulqarnain
rights: Copyright (c) 2026 FNU Zulqarnain (Author)
date: 2026-02-16
publication date: 2026-02-16
starting page: 371
ending page: 415
doi: 10.63125/28gejw48

Artificial Intelligence Based Predictive Modeling for Structural Health Monitoring and Failure Risk Assessment in Water and Sanitation Infrastructure
type: item
description: Artificial intelligence–based predictive modeling has increasingly been adopted to enhance structural health monitoring and failure risk assessment in water and sanitation infrastructure systems characterized by aging assets, heterogeneous environmental exposure, and rare-event failure dynamics. This study developed and evaluated an integrated quantitative framework incorporating asset registry attributes, historical failure indicators, telemetry-derived operational metrics, inspection-based condition severity measures, and environmental exposure covariates. The analytical dataset included 1,248 infrastructure assets observed over a 10-year period across multiple operational districts. Hierarchical regression modeling demonstrated that baseline registry variables explained 28.6% of the variance in failure likelihood, while the inclusion of historical break frequency increased explanatory power to 52.4%. The addition of telemetry variability metrics and inspection-based severity indices further improved model performance, resulting in a final integrated model explaining 68.2% of outcome variance. Asset age and material class showed significant standardized effects of 0.31 and 0.27, respectively, while historical recurrence exhibited the strongest influence with a coefficient of 0.39. Telemetry-derived pressure variability improved predictive discrimination by 11.5% in monitored zones. Composite risk prioritization based on integrated modeling captured 74% of observed failures within the top 20% of ranked assets, representing a 31.4% improvement over age-based heuristics. Reliability analysis confirmed internal consistency of multi-item constructs, with Cronbach’s alpha values exceeding 0.80 for telemetry and inspection indices. The findings demonstrate that integrated AI-driven predictive modeling substantially enhances structural condition estimation accuracy and failure risk prioritization efficiency within complex water and sanitation infrastructure systems.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Structural Health Monitoring
  topic: keywords
subject: Failure Risk Assessment
  topic: keywords
subject: Water Infrastructure
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Palash Chandra Das
rights: Copyright (c) 2026 Palash Chandra Das (Author)
date: 2026-02-16
publication date: 2026-02-16
starting page: 325
ending page: 370
doi: 10.63125/njg70d76

AI-Driven Cybercrime Forensics for Predictive Threat Detection and Investigative Intelligence
type: item
description: AI-Driven Cybercrime Forensics has become increasingly important for predictive threat detection and investigative intelligence due to the rising complexity and volume of digital evidence. This quantitative study examined how AI-driven forensic capabilities predicted key investigative outcomes, including investigative efficiency, decision accuracy, and case documentation quality. Data were obtained from 210 valid respondents working in cybercrime-related roles. The sample included digital forensics analysts (24.8%), incident response specialists (21.0%), SOC analysts or threat hunters (18.1%), and cybersecurity managers (13.3%). Most respondents reported high familiarity with AI-based security tools (43.8%) and high exposure to multi-source forensic evidence (45.7%). Descriptive results showed high agreement for predictive threat detection effectiveness (M = 4.12, SD = 0.61) and investigative intelligence quality (M = 4.05, SD = 0.64). Workflow efficiency also scored strongly (M = 3.98, SD = 0.69). Explain ability and trust calibration produced the lowest mean (M = 3.74, SD = 0.78), while evidence traceability and documentation integrity remained moderate-high (M = 3.81, SD = 0.73). Reliability analysis confirmed acceptable-to-strong internal consistency, with Cronbach’s alpha values ranging from 0.78 to 0.89. Multiple regression results indicated that investigative intelligence quality was the strongest predictor of investigative efficiency (β = .41, t = 7.82, p < .001), decision accuracy (β = .34, t = 6.11, p < .001), and documentation quality (β = .29, t = 5.02, p < .001). Workflow efficiency significantly predicted investigative efficiency (β = .33, p = .001) and decision accuracy (β = .21, p = .018). The regression models explained 62% of the variance in investigative efficiency (R² = .62), 55% in decision accuracy (R² = .55), and 49% in documentation quality (R² = .49). Overall, findings confirmed that AI-supported correlation and intelligence generation most …
subject: Articles
  topic: section
subject: AI Forensics
  topic: keywords
subject: Cybercrime Detection
  topic: keywords
subject: Predictive Intelligence
  topic: keywords
subject: Threat Analytics
  topic: keywords
subject: Digital Investigation
  topic: keywords
creator: Atif Khan
rights: Copyright (c) 2026 Atif Khan (Author)
date: 2026-02-15
publication date: 2026-02-15
starting page: 280
ending page: 324
doi: 10.63125/mggyg703

Design and Optimization of End-To-End Artificial Intelligence and Machine Learning Pipelines in CI/CD-Enabled Cloud Infrastructures
type: item
description: This quantitative study examined the design and optimization of end-to-end artificial intelligence and machine learning (AI/ML) pipelines deployed in CI/CD-enabled cloud infrastructures by evaluating measurable relationships between pipeline engineering maturity and operational outcomes. A total of 214 valid responses from AI/ML, DevOps/Mops, and cloud engineering professionals were analyzed across technology, finance, healthcare, manufacturing, and logistics sectors. Descriptive results showed high maturity in component separation (M = 4.12, SD = 0.63), schema validation (M = 4.18, SD = 0.54), and automated test coverage (M = 4.05, SD = 0.58), while greater variability was observed in dependency complexity (M = 3.41, SD = 0.72) and drift detection (M = 3.78, SD = 0.75). Operational indicators revealed an average training runtime of 142.6 minutes (SD = 38.4), GPU utilization of 81.7% (SD = 6.8), scaling efficiency of 78.3% (SD = 9.5), and inference latency of 84.2 MS (SD = 15.6). Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.83 to 0.91. Regression results indicated that modularity, automation depth, and validation effectiveness significantly reduced pipeline runtime (R² = 0.528), while monitoring readiness, test coverage, and gate pass rate significantly reduced change failure rate (R² = 0.496). Autoscaling responsiveness, caching maturity, and serving optimization significantly improved tail latency stability (R² = 0.551). Cost models showed scaling efficiency and GPU utilization significantly reduced training cost per run (R² = 0.467), while caching maturity reduced inference cost per 1,000 predictions (R² = 0.419). Overall, the findings demonstrated that measurable pipeline optimization outcomes were strongly associated with architectural modularity, CI/CD automation depth, validation rigor, and observability maturity in cloud-native AI/ML delivery systems.
subject: Articles
  topic: section
subject: AI/ML Pipelines
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Cloud Optimization
  topic: keywords
subject: Mops
  topic: keywords
subject: Distributed Training
  topic: keywords
creator: Mohammad Robel Miah
rights: Copyright (c) 2026 Mohammad Robel Miah (Author)
date: 2026-02-15
publication date: 2026-02-15
starting page: 234
ending page: 279
doi: 10.63125/sdvder84

Privacy-Preserving Federated Learning for Critical Infrastructure: A Systematic Review of Security Threat Models, Deployment Patterns, And Governance
type: item
description: This study presented a quantitative systematic synthesis of privacy-preserving federated learning research in critical infrastructure contexts by examining how security threat models, privacy mechanisms, deployment architectures, and governance controls were jointly represented and evaluated across the empirical literature. A total of 120 peer-reviewed and high-quality technical studies were coded and analyzed using structured extraction protocols that transformed heterogeneous experimental reports into comparable variables. Descriptive analysis showed that malicious-client and colluding-client threat models dominated the evidence base (approximately 55% and 32%, respectively), while availability-focused threats were reported in fewer than 20% of studies. Passive inference attacks were evaluated more frequently (67.5%) than active attacks (40.8%), with confidentiality emerging as the most frequently targeted security property (65.8%). In terms of privacy mechanisms, secure aggregation (50.8%) and differential privacy (45.0%) were the most prevalent, whereas cryptographic computation approaches (21.7%) and trusted execution techniques (15.8%) appeared less often and were associated with higher reported overhead. Deployment analysis indicated strong dominance of centralized aggregation (70.8%) and cross-silo regimes (60.0%), reflecting institutional coordination patterns typical of critical infrastructure systems. Governance reporting was uneven, with role definitions (55.8%) and access rules (59.2%) appearing more frequently than consent artifacts (27.5%) and accountability procedures (17.5%). Regression analysis demonstrated that active threat exposure and cryptographic or hybrid privacy mechanisms were positively associated with standardized overhead burden, while cross-silo deployment and higher governance maturity scores were negatively associated with both overhead and utility loss. Governance maturity and auditability indices achieved acceptable-to-strong inter…
subject: Articles
  topic: section
subject: Privacy-Preserving Federated Learning
  topic: keywords
subject: Critical Infrastructure Security
  topic: keywords
subject: Threat Models
  topic: keywords
subject: Deployment Architecture
  topic: keywords
subject: Governance Analytics
  topic: keywords
creator: Istiaq Ahmed
rights: Copyright (c) 2026 Istiaq Ahmed (Author)
date: 2026-02-13
publication date: 2026-02-13
starting page: 204
ending page: 235
doi: 10.63125/12hayf30

Machine Learning Based Data Optimization of Merchant Onboarding Processes in Fintech Ecosystems: A Time-Series and Process-Mining Study
type: item
description: This study examined how machine learning–based data optimization and process analytics influence merchant onboarding performance within a FinTech ecosystem by integrating supervised modeling, process mining, and time-series evaluation. Using a retrospective dataset of 3,200 onboarding cases observed across 26 operational weeks, the analysis reconstructed event-driven workflows and quantified relationships between data quality, workflow behavior, and decision outcomes. Descriptive results showed moderate data missingness (mean = 7.8%), high median document completeness (92%), and substantial operational heterogeneity reflected in manual-touch intensity (41.7%) and escalation rate (9.6%). Median onboarding cycle time was 24.7 hours, although the mean reached 38.4 hours due to delay-heavy exception cases, and waiting time concentration averaged 63.2%, indicating that queue-related idle time constituted the majority of total duration. Logistic regression results demonstrated that the Data Quality Optimization Index significantly increased approval likelihood (OR = 1.65, p < .001), while Document Risk Intensity (OR = 0.72, p < .001) and Workflow Friction (OR = 0.66, p < .001) significantly reduced approval probability. Linear regression analysis further showed that Workflow Friction (+12.1 hours, p < .001) and Operational Effort (+9.3 hours, p < .001) substantially increased onboarding cycle time, whereas improved data quality reduced processing duration (−6.8 hours, p < .001). The final approval model achieved a McFadden pseudo R² of 0.29, and the cycle-time model explained 42% of duration variance (R² = 0.42; RMSE = 28.9 hours), with coefficient stability confirmed across rolling time-window robustness checks. Overall, the findings demonstrated that onboarding performance was systematically shaped by measurable data conditions and trace-level workflow characteristics rather than solely by merchant risk segmentation. By linking temporal KPI shifts with process variants …
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Process Mining
  topic: keywords
subject: Onboarding
  topic: keywords
subject: Fintech
  topic: keywords
subject: Time-Series
  topic: keywords
creator: Iftekhar Ahmed
rights: Copyright (c) 2025 Iftekhar Ahmed (Author)
date: 2025-06-30
publication date: 2025-06-30
starting page: 506
ending page: 535
doi: 10.63125/kn2ycp72

Machine Learning–Based Transaction Risk Scoring Models for Financial Compliance Monitoring in Foreign Exchange Operations
type: item
description: This study provided a quantitative cross-study synthesis of machine learning–based transaction risk scoring models for financial compliance monitoring in foreign exchange operations, with emphasis on measurable modeling practices, evaluation rigor, and governance instrumentation. A total of 124 analytic records derived from 89 publications were coded using a structured extraction protocol that converted heterogeneous reporting into standardized variables across model family, feature construction, validation design, labeling strategy, evaluation metrics, and governance controls. Descriptive results showed that ensemble models were the most frequently evaluated approach (44.4%), followed by logistic regression and generalized linear models (37.1%), decision tree models (33.9%), neural architectures (29.8%), and unsupervised or semi-supervised methods (26.6%). Customer-profile variables (69.4%), geographic corridor indicators (62.1%), and temporal aggregation features (57.3%) were the most commonly engineered feature groups, while network-based variables appeared in 41.9% of records. Evaluation practices were dominated by discrimination metrics (82.3%), with lower reporting of ranking metrics (61.3%), calibration measures (34.7%), and cost-sensitive analyses (28.2%). Governance and auditability constructs were underreported, with access control indicators documented in 29.8% of records and traceability artifacts in 22.6%. Reliability testing demonstrated strong internal consistency for governance maturity (α = 0.86) and documentation completeness (α = 0.84) indices. Logistic regression analysis showed that ensemble models (OR = 2.27, p = 0.008), neural models (OR = 1.99, p = 0.041), and out-of-time validation (OR = 2.83, p = 0.004) were significantly associated with high predictive performance reporting. Linear regression indicated that operational studies were strongly associated with higher governance maturity scores (β = 1.12, p < 0.001). Overall, the findings indic…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: FX Compliance
  topic: keywords
subject: Risk Scoring
  topic: keywords
subject: Transaction Monitoring
  topic: keywords
subject: Governance
  topic: keywords
creator: Rukaiya Khatun Moury
rights: Copyright (c) 2026 Rukaiya Khatun Moury (Author)
date: 2026-02-09
publication date: 2026-02-09
starting page: 172
ending page: 203
doi: 10.63125/0nbg6w69

AI-Enabled Decision Support Systems for Service Operations an Analytical Modeling and IT Strategy Framework
type: item
description: This study examined the quantitative relationships among AI-enabled decision support system (AI-DSS) capability, IT strategy alignment, and measurable service operations performance using unit-level data from a data-intensive service organization. A cross-sectional explanatory design was applied with structured retrospective extraction of operational KPIs and AI-DSS system log indicators, combined with survey-based measurement of IT strategy alignment dimensions. The final dataset included 52 operational service units and 198 valid survey responses retained after screening from an initial pool of 214 responses (92.5% retention). AI-DSS capability was operationalized as a composite index (0–100) supported by indicators such as system-use frequency, recommendation viewing rate, decision latency, and forecasting accuracy. IT strategy alignment was measured using a composite index (1–5) based on data integration maturity, interoperability, governance strength, and workflow embedding. Service operations performance was measured through unit-level KPIs including average waiting time, service-level attainment, cost per transaction, and abandonment rate. Descriptive results indicated relatively high AI-DSS capability (M = 71.6, SD = 11.4) and moderate-to-high IT strategy alignment (M = 3.84, SD = 0.52), with the greatest dispersion observed in decision latency (M = 26.4 minutes, SD = 13.2). Regression analysis showed that AI-DSS capability was significantly associated with lower waiting time (B = -0.021, p = 0.001), higher service-level attainment (B = 0.142, p = 0.005), lower cost per transaction (B = -0.016, p = 0.002), and lower abandonment rate (B = -0.071, p = 0.001). IT strategy alignment demonstrated significant direct effects on waiting time (B = -0.118, p = 0.025), service-level attainment (B = 1.87, p = 0.004), and cost per transaction (B = -0.091, p = 0.044). Moderation analysis indicated significant interaction effects for waiting time (B = -0.0062, p = 0.005), …
subject: Articles
  topic: section
subject: AI-Enabled Decision Support
  topic: keywords
subject: Service Operations Performance
  topic: keywords
subject: IT Strategy Alignment
  topic: keywords
subject: Operational Analytics Modeling
  topic: keywords
subject: Quantitative Regression Analysis
  topic: keywords
creator: Tanjina Binte Sohrab
rights: Copyright (c) 2026 Tanjina Binte Sohrab (Author)
date: 2026-02-09
publication date: 2026-02-09
starting page: 129
ending page: 171
doi: 10.63125/am81ns14

Quantitative AI-Based Load-Flow and Fault-Prediction Modeling for Reliability Enhancement in Electrical Distribution Networks
type: item
description: This study examined an integrated AI-based framework for load-flow estimation and fault prediction to enhance reliability in electrical distribution networks. A quantitative design was applied using operational data from 12 medium-voltage feeders over an 18-month period, comprising 104,832 operating snapshots collected at 15-minute resolution and 286 confirmed fault events. Feeder sizes ranged from 48 to 137 buses, with topology depth varying from 6 to 18 levels. Descriptive results showed an overall mean voltage magnitude of 0.984 p.u. (SD = 0.031), with a minimum observed value of 0.901 p.u. and a maximum of 1.067 p.u. Mean feeder loading was 62.8% of rated capacity, with peak loading reaching 118.4%. Outage duration averaged 58.6 minutes (SD = 49.8) and customer interruptions ranged from 14 to 4,320 per event. Load-flow regression models indicated that measurement density significantly reduced voltage prediction error (β = –0.41, p < 0.001), while topology depth increased estimation error (β = 0.28, p = 0.002), producing adjusted R² values of 0.64 for voltage error and 0.58 for branch current error. Fault occurrence modeling demonstrated that environmental disturbance severity (β = 0.44, p < 0.001), electrical stress exposure (β = 0.37, p < 0.001), and asset vulnerability (β = 0.29, p = 0.004) significantly increased fault probability, with Nagelkerke R² = 0.52. Fault location accuracy improved significantly when load-flow-derived stress features were included (β = 0.35, p = 0.001). Reliability-linked regression results showed that improved fault localization reduced restoration time (β = –0.38, p < 0.001), and higher predicted fault probability increased interruption duration (β = 0.26, p = 0.009). Robustness testing under measurement perturbation reduced model explanatory power by less than 6%, confirming stability. Overall, the integrated framework demonstrated statistically significant relationships between state estimation accuracy, fault prediction performa…
subject: Articles
  topic: section
subject: AI Load-Flow
  topic: keywords
subject: Fault Prediction
  topic: keywords
subject: Distribution Reliability
  topic: keywords
subject: State Estimation
  topic: keywords
subject: Outage Analytics
  topic: keywords
creator: Md. Sultan Mahamud
rights: Copyright (c) 2026 Md. Sultan Mahamud (Author)
date: 2026-02-09
publication date: 2026-02-09
starting page: 111
ending page: 128
doi: 10.63125/taejr363

Electrical Engineering Approaches for AI-Enabled Autonomous Robotics in U.S. Industrial and Defense Applications
type: item
description: This quantitative study examined how electrical engineering approaches influenced the performance of AI-enabled autonomous robotics in U.S. industrial and defense applications. A total of 220 valid responses were analyzed, including 112 industrial (50.9%) and 108 defense (49.1%) system cases, ensuring balanced cross-domain representation. Six electrical engineering constructs were evaluated as predictors of autonomy performance: sensor system performance, embedded computing efficiency, power electronics and energy stability, control and actuation accuracy, communication system reliability, and safety and reliability engineering effectiveness. Descriptive results indicated that safety and reliability engineering demonstrated the highest consistency (M = 4.12, SD = 0.49), followed by control and actuation accuracy (M = 3.94, SD = 0.55) and sensor system performance (M = 3.82, SD = 0.61). Embedded computing efficiency (M = 3.56, SD = 0.74) and power stability (M = 3.41, SD = 0.79) showed the greatest variability across systems. Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha coefficients ranging from 0.842 to 0.926. Multiple regression analysis demonstrated that the combined model significantly predicted autonomy performance (R² = 0.659, Adjusted R² = 0.647, F = 67.83, p < .001). Embedded computing efficiency (β = 0.338, p < .001) and sensor system performance (β = 0.301, p < .001) were the strongest predictors. Power stability (β = 0.256, p < .001) and control accuracy (β = 0.213, p < .001) also significantly contributed. Communication reliability showed a moderate effect (β = 0.158, p = .001) and was stronger in defense contexts, while safety engineering provided a smaller but significant stabilizing influence (β = 0.109, p = .014). Findings confirmed that electrical subsystem performance significantly explained variation in AI-enabled autonomy outcomes across both domains.
subject: Articles
  topic: section
subject: AI Robotics
  topic: keywords
subject: Electrical Engineering
  topic: keywords
subject: Autonomy
  topic: keywords
subject: Industrial Defense
  topic: keywords
subject: Performance
  topic: keywords
creator: Shamsul Arifeen
rights: Copyright (c) 2026 Shamsul Arifeen (Author)
date: 2026-02-09
publication date: 2026-02-09
starting page: 72
ending page: 110
doi: 10.63125/h255cq52

AI-Based Revenue Leakage Detection Models Using Transaction-Level Financial Data: A Review
type: item
description: AI-based revenue leakage detection using transaction-level financial data has gained importance due to increasing pricing complexity, automated billing processes, and high-volume digital transactions. This study quantitatively examined four detection constructs—pricing compliance detection, authorization integrity, temporal anomaly identification, and adjustment behavior monitoring—and evaluated their relationships with overall revenue leakage detection effectiveness. A structured survey design was applied, and responses were collected from 210 participants across transaction-intensive industries, including telecommunications (22.4%), e-commerce/retail (21.0%), healthcare/insurance (18.6%), and financial services (17.1%). Most respondents reported direct involvement in revenue-cycle activities (61.0%), and 75.7% reported intermediate-to-advanced familiarity with analytics and AI tools. Descriptive results indicated consistently positive construct scores, with mean values of 4.12 (SD = 0.61) for pricing compliance detection, 4.08 (SD = 0.65) for authorization integrity, 3.94 (SD = 0.70) for temporal anomaly identification, 3.89 (SD = 0.74) for adjustment behavior monitoring, and 4.05 (SD = 0.63) for leakage detection effectiveness. Reliability analysis confirmed strong internal consistency, with Cronbach’s alpha values ranging from 0.81 to 0.88 across constructs. Multiple regression analysis demonstrated that the predictors jointly explained substantial variance in leakage detection effectiveness (R² = 0.62; adjusted R² = 0.61; F = 83.40; p < .001). Pricing compliance detection produced the strongest standardized effect (β = 0.38; t = 6.52; p < .001), followed by authorization integrity (β = 0.29; t = 5.11; p < .001), adjustment behavior monitoring (β = 0.21; t = 3.88; p < .001), and temporal anomaly identification (β = 0.17; t = 3.09; p = .002). Multicollinearity remained acceptable (VIF = 1.41–1.72). Hypothesis testing supported 4 of 5 hypotheses (80%), with one in…
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Revenue Leakage
  topic: keywords
subject: Transactions
  topic: keywords
subject: Detection
  topic: keywords
subject: Analytics
  topic: keywords
creator: Md. Fardous
rights: Copyright (c) 2026 Md. Fardous (Author)
date: 2026-02-09
publication date: 2026-02-09
starting page: 37
ending page: 71
doi: 10.63125/5h2n0g69

AI-Driven Change Detection Using SAR, LIDAR, And Sentinel-2 Data for Landslide Monitoring and Disaster Early Warning Systems
type: item
description: This study addressed the problem that many AI-driven landslide early-warning pipelines generate technically strong change maps but lack auditable, decision-aligned evidence products that stakeholders can trust and act on consistently under uncertainty. The purpose was to evaluate, in a quantitative cross-sectional, case-based design, whether multi-sensor AI change evidence (SAR, LiDAR, Sentinel-2) and socio-technical decision constructs jointly explain perceived Early Warning Effectiveness (EWE). Data were collected from a professional sample of N = 162 screened-valid respondents drawn from operational roles (disaster management 31.5%, GIS/remote sensing 27.8%, engineering 22.2%, planning 18.5%; mean experience 8.6 years) evaluating the case outputs and decision readiness constructs. Key variables included Perceived AI Performance (PAI), Interpretability (INT), Trust in Alerts (TRU), Decision Confidence (DCF), sensor contribution perceptions (PSC_SAR, PSC_LiDAR, PSC_S2), and the fused Multi-Sensor Change Evidence Score (MSCES) as predictors, with EWE as the main dependent variable. The analysis plan applied descriptive statistics, reliability testing (Cronbach’s α), Pearson correlations, and hierarchical regressions comparing single-sensor baselines to fusion and a full decision-aligned model, plus threshold sensitivity and agreement/conflict diagnostics. Construct means were high (PAI M = 4.21, INT 4.02, TRU 4.08, DCF 4.16, EWE 4.12) with strong reliabilities (α = 0.83–0.90). Correlations supported the hypothesized pathway (PAI–EWE r = 0.68, TRU–EWE 0.62, INT–EWE 0.59, all p < .001). Regression results showed LiDAR-only (R² = 0.31) exceeded SAR-only (0.24) and Sentinel-2-only (0.18), while MSCES fusion improved explanatory power (R² = 0.46, β = 0.68, p < .001). In the full model, explained variance increased to R² = 0.62 (Adj. 0.61), with significant unique effects for PAI (β = 0.34), TRU (β = 0.29), MSCES (β = 0.27) and INT (β = 0.18). Operationally, the case pipe…
subject: Articles
  topic: section
subject: Landslide early warning
  topic: keywords
subject: Multi-sensor fusion
  topic: keywords
subject: Change detection
  topic: keywords
subject: Trust and interpretability
  topic: keywords
subject: MSCES
  topic: keywords
creator: Ratul Debnath
creator: Aditya Dhanekula
rights: Copyright (c) 2023 Ratul Debnath, Aditya Dhanekula (Author)
date: 2023-09-28
publication date: 2023-09-28
starting page: 153
ending page: 188
doi: 10.63125/4y740y95

Predictive Risk Modeling and Quantitative Analysis of Microbial Contamination in Hospital Textiles
type: item
description: This quantitative observational study examined microbial contamination in hospital textiles using predictive risk modeling and multivariable statistical analysis to identify determinants of contamination across textile categories, operational pathways, and clinical contexts. A cross-sectional analytic design was applied in a multi-unit hospital setting, and a total of 360 reusable textile items were sampled, including bed linens (40.0%), staff uniforms (25.0%), towels (20.0%), and privacy curtains (15.0). Items were collected from both high-acuity units (35.0%) and general wards (65.0%) and were linked to structured metadata describing exposure duration, contact density proxies, handling and transport conditions, laundering characteristics, storage duration, and textile reuse indicators. Microbial contamination outcomes were measured as microbial burden and contamination positivity, with positivity observed in 46.5% of linens, 48.6% of towels, 34.4% of uniforms, and 29.6% of curtains. Descriptive analysis demonstrated highly skewed contamination distributions, with median microbial burden values ranging from 2.0 to 3.4 units across textile categories and higher exposure duration observed for curtains and uniforms compared with linens and towels. Multivariable regression analysis showed that textile category, exposure duration, contact density, ward acuity, handling pathway quality, laundering quality, and post-laundering storage time were independently associated with contamination outcomes after adjustment for clustering by ward and laundering batch. In the microbial burden model, towels (adjusted estimate = 1.35, p < 0.001) and linens (1.28, p = 0.001) exhibited higher burden relative to uniforms, while curtains showed lower burden (0.82, p = 0.041). In the contamination positivity model, towels (adjusted odds ratio = 1.84, p = 0.009) and linens (1.62, p = 0.019) demonstrated higher odds of positivity. High-acuity wards were associated with increased contamination…
subject: Articles
  topic: section
subject: Hospital Textiles
  topic: keywords
subject: Microbial Contamination
  topic: keywords
subject: Risk Modeling
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
subject: Infection Control
  topic: keywords
creator: Amjad Hossain
creator: Md Tahmidul Islam
rights: Copyright (c) 2026 Amjad Hossain, Md Tahmidul Islam (Author)
date: 2026-02-03
publication date: 2026-02-03
starting page: 01
ending page: 36
doi: 10.63125/zv13d155

Risk Mitigation and Resilience Modeling for Consumer Distribution Networks During Demand Shocks: A Quantitative Stochastic Optimization and Scenario Analysis Study
type: item
description: This study investigated risk mitigation and resilience modeling for consumer distribution networks operating under demand shock conditions using a quantitative stochastic optimization and scenario-based analysis framework. A multi-echelon distribution network was evaluated under a structured set of baseline and stress demand scenarios to examine how mitigation policies and resilience-oriented capabilities influenced operational performance. The analytical dataset comprised 1,520 scenario-level network observations, balanced across four service regions and essential and non-essential product segments. Descriptive results indicated a mean service continuity index of 0.81 with observable right-skewness, while the unmet demand ratio averaged 0.18, reaching a maximum of 0.61 under severe stress scenarios. Cost escalation exhibited the greatest variability, with a mean increase of 22.4% and a maximum observed value of 78.6%, confirming strong tail sensitivity in economic outcomes. Reliability analysis of composite constructs demonstrated acceptable to excellent internal consistency, with Cronbach’s alpha values ranging from 0.85 to 0.93, supporting measurement validity prior to inferential testing. Regression analysis showed that resilience capability (β = 0.421, p < 0.001) and robustness stability (β = 0.276, p < 0.001) were statistically significant predictors of performance outcomes after controlling for demand volatility, utilization intensity, and network scale. Inclusion of resilience constructs improved explanatory power, increasing adjusted R² from 0.241 in the baseline model to 0.318 in the extended specification. Segment-level analysis revealed that essential goods achieved higher service continuity (0.86) and lower unmet demand (0.13) than non-essential goods, albeit with higher average cost escalation (26.9% versus 17.1%). Robustness testing using out-of-sample and segmented evaluations confirmed stability of coefficient direction and significance across alter…
subject: Articles
  topic: section
subject: Distribution Resilience
  topic: keywords
subject: Demand Shocks
  topic: keywords
subject: Stochastic Optimization
  topic: keywords
subject: Risk Mitigation
  topic: keywords
subject: Scenario Analysis
  topic: keywords
creator: Md Shahab Uddin
creator: Aditya Dhanekula
rights: Copyright (c) 2025 Md Shahab Uddin, Aditya Dhanekula (Author)
date: 2023-06-06
publication date: 2023-06-06
starting page: 01
ending page: 30
doi: 10.63125/jkevvq84

ERP Modernization Outcomes in Cloud Migration: A Meta-Analysis of Performance and Total Cost of Ownership (TCO) Across Enterprise Implementations
type: item
description: Enterprise ERP modernization through cloud migration is promoted as a route to higher performance and lower total cost of ownership (TCO), yet enterprises often experience mixed results and hidden cost leakage. To address this problem, the study measured post-migration performance and TCO and modeled drivers of both outcomes. A quantitative, cross-sectional, case-based design synthesized survey data from N = 210 stakeholders across six cloud-migration cases (SaaS 56.2%, hybrid 43.8; phased 67.1%, big-bang 32.9%). Independent variables were cloud migration readiness, implementation quality, governance and risk management maturity, change management effectiveness, and vendor or partner support; dependent variables were ERP performance outcomes and TCO outcomes, decomposed into visible/direct and hidden/indirect components. Analysis began with Cronbach’s alpha to confirm scale consistency (readiness α = 0.89; implementation quality α = 0.91; governance α = 0.87; change management α = 0.90; vendor support α = 0.88; performance α = 0.93; TCO α = 0.92), then applied descriptive statistics, Pearson correlations, and multiple regression, with a Cloud Migration Footprint Index (CMFI) and a Performance and TCO Tradeoff Map. Results showed performance improvement (M = 3.94, SD = 0.62) and TCO improvement (M = 3.62, SD = 0.66), with visible benefits (M = 3.76, SD = 0.71) exceeding hidden cost pressure (M = 3.41, SD = 0.78) by 0.35 (HCR = 0.91). Performance correlated most with implementation quality (r = 0.68) and change management (r = 0.63), and the performance regression explained 61% of variance, F(5,204) = 64.22, p < .001, led by implementation quality (β = 0.34) and change management (β = 0.26). For TCO, the regression explained 47% of variance, F(5,204) = 35.89, p < .001, with governance maturity strongest (β = 0.29) and change management not significant (p = .074). High-footprint cases (CMFI M = 0.67) reported higher performance (M = 4.08) but higher hidden-cost pressur…
subject: Articles
  topic: section
subject: ERP Cloud Migration
  topic: keywords
subject: ERP Performance Outcomes
  topic: keywords
subject: Total Cost of Ownership (TCO)
  topic: keywords
subject: Governance Maturity
  topic: keywords
subject: Implementation Quality
  topic: keywords
creator: B. M. Taslimul Haque
creator: Md. Arifur Rahman
rights: Copyright (c) 2021 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
date: 2021-07-13
publication date: 2021-07-13
starting page: 168
ending page: 203
doi: 10.63125/vrz8hw42

A Meta-Analysis of OSHA Safety Training Programs and their Impact on Injury Reduction and Safety Compliance in U.S. Workplaces
type: item
description: This study addresses the gap between mandated OSHA aligned safety training and measurable safety outcomes in high exposure enterprise workplaces, where injuries and near misses persist despite formal programs. The purpose was to quantify how OSHA training exposure and perceived quality relate to safety compliance and injury reduction indicators, and to test whether compliance is the pathway through which training improves outcomes. Using a quantitative cross sectional, case-based survey design, data were collected from 312 employees across workplace cases in construction, manufacturing, and healthcare; multivariate models used listwise deletion, yielding N = 306. Key variables were OSHA Training, Safety Compliance, and Injury Reduction Indicators. Analysis followed a plan of data screening, reliability and construct checks (Cronbach alpha, KMO, Bartlett, factor loadings), then descriptive statistics, Pearson correlations, and hierarchical regression to evaluate direct and mediation consistent patterns. Missing data were low (1.8%) and Harman single factor variance was 32.4%, reducing common method bias concerns. Respondents reported above midpoint scores for training (M = 3.84, SD = 0.62), compliance (M = 3.92, SD = 0.58), and injury reduction (M = 3.67, SD = 0.60), with strong reliability (alpha = 0.91, 0.88, 0.86). Training correlated with compliance (r = 0.62) and injury reduction (r = 0.49), and compliance correlated with injury reduction (r = 0.56), all p < .001. In regression, training predicted injury reduction (beta = 0.49, R2 = 0.24) and predicted compliance (beta = 0.62, R2 = 0.39); when both predictors were entered, compliance remained stronger (beta = 0.41) while training attenuated but stayed significant (beta = 0.24), improving fit to R2 = 0.38, consistent with partial mediation. Case pattern summaries converged with the model, with high training groups reporting higher compliance and injury reduction than low training groups. Implications are that org…
subject: Articles
  topic: section
subject: OSHA Safety Training
  topic: keywords
subject: Safety Compliance
  topic: keywords
subject: Injury Reduction
  topic: keywords
subject: Mediation Pathway
  topic: keywords
subject: Workplace Safety Performance
  topic: keywords
creator: Jahangir Shekh
creator: Hammad Sadiq
rights: Copyright (c) 2024 Jahangir Shekh, Hammad Sadiq (Author)
date: 2024-06-30
publication date: 2024-06-30
starting page: 559
ending page: 592
doi: 10.63125/8zxw0h59

IMPROVING PROJECT LIFECYCLE MANAGEMENT (PLM) EFFICIENCY WITH CLOUD ARCHITECTURES AND CAD INTEGRATION AN EMPIRICAL STUDY USING INDUSTRIAL CAD REPOSITORIES AND CLOUD‑NATIVE WORKFLOWS
type: item
description: This empirical quantitative study examined how cloud architectures and CAD–PLM integration mechanisms related to Project Lifecycle Management (PLM) efficiency using trace evidence from industrial CAD repositories and cloud-native workflows. A retrospective longitudinal, multi-case design was applied to linked event logs spanning CAD artifact histories, PLM change and release records, and workflow telemetry. The final analytic dataset comprised 12 projects, 4,860 engineering change objects, 1,140 release packages, 98,420 CAD artifacts, and 312,775 workflow job instances (numerical findings as summarized in the descriptive results). PLM efficiency was operationalized with governance-aligned event boundaries and distribution-focused metrics, including engineering change lead time, release cycle time, workflow throughput, workflow success rate, tail-delay prevalence, and rework proxies derived from reopened changes, repeated short-window edits, repeated packaging attempts, and retry cascades. Descriptive results indicated heavy-tailed timing behavior: engineering change lead time exhibited a median of 14.6 days with a 90th percentile of 42.0 days, and release cycle time exhibited a median of 6.2 days with a 90th percentile of 18.5 days. Workflow operations showed a mean throughput of 1,360 jobs/day and an overall workflow success rate of 93.8%, while exception clusters were concentrated in translation and packaging stages. Rework signals were nontrivial, with reopened changes averaging 8.4% and repeated edits within short windows averaging 20.2% across change cases. These findings supported a measurement-based interpretation of PLM efficiency as a coupled outcome shaped by propagation timeliness, integrity preservation, execution reliability, product structure complexity, and collaboration concurrency, with tail delays and rework emerging as central stability risks alongside average speed.
subject: Articles
  topic: section
subject: Project Lifecycle Management
  topic: keywords
subject: Cloud Architecture
  topic: keywords
subject: CAD Integration
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: Repository Analytics
  topic: keywords
creator: Md Syeedur Rahman
rights: Copyright (c) 2025 Md Syeedur Rahman (Author)
date: 2025-06-30
publication date: 2025-06-30
starting page: 452
ending page: 505
doi: 10.63125/8ba1gz55

CYBERSECURE DOCUMENTATION AND RECORD-KEEPING PROTOCOLS FOR SAFEGUARDING SENSITIVE FINANCIAL INFORMATION ACROSS BUSINESS OPERATIONS
type: item
description: Cybersecure documentation and record-keeping gaps in enterprises can expose sensitive financial information to unauthorized access, alteration, loss, and weak audit defensibility, especially when records circulate through shared repositories, email workflows, line-of-business applications, and third-party cloud services. The purpose of this study was to quantify whether cybersecure documentation and record-keeping protocol maturity strengthens safeguarding across business operations. A quantitative, cross-sectional, case-based design used a 5-point Likert survey in cloud and enterprise cases. After screening, 210 valid responses were analyzed, representing Finance/Accounting (38.6%), Procurement and AP-AR (22.4%), Payroll and HR finance (14.8%), Internal audit and compliance (12.9%), and IT or security support (11.3%). Predictors were six protocol domains: Access Control and Authorization, Encryption and Secure Storage, Audit Trails and Monitoring, Retention and Secure Disposal, Policy Enforcement and Training, and Incident Response and Recoverability; the outcome was Safeguarding Effectiveness (confidentiality, integrity, availability, and audit defensibility). The analysis plan used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression, supported by Spearman correlations and hierarchical regression. Results showed moderate-to-high protocol adoption (M = 3.74, SD = 0.62) and high safeguarding effectiveness (M = 3.79, SD = 0.58), with reliable constructs (α = 0.81 to 0.90). All protocol domains correlated positively with safeguarding (r = .46 to .62, all p < .001), with Policy Enforcement and Training strongest (r = .62, p < .001). The regression model was significant (F(6,203) = 46.21, p < .001) and explained 58% of safeguarding variance (R² = .58; Adjusted R² = .57); Policy Enforcement and Training was the strongest unique predictor (β = .28, p < .001), while Retention and Secure Disposal was weaker and not significant at 0.05 (β …
subject: Articles
  topic: section
subject: Cybersecure Documentation
  topic: keywords
subject: Record-Keeping Protocols
  topic: keywords
subject: Safeguarding Effectiveness
  topic: keywords
subject: Financial Information Security
  topic: keywords
subject: Protocol Maturity Index
  topic: keywords
creator: Faysal Khan
creator: Tahmina Akter Bhuya Mita
rights: Copyright (c) 2025 Faysal Khan, Tahmina Akter Bhuya Mita (Author)
date: 2023-09-28
publication date: 2023-09-28
starting page: 117
ending page: 152
doi: 10.63125/cz2gwm06

DIGITAL TWIN–ENABLED OPTIMIZATION OF ELECTRICAL, INSTRUMENTATION, AND CONTROL ARCHITECTURES IN SMART MANUFACTURING AND UTILITY-SCALE SYSTEMS
type: item
description: This quantitative, multi-site study examined whether digital twin–enabled optimization was associated with measurable improvements in electrical, instrumentation, and control (EIC) architecture performance across smart manufacturing and utility-scale systems. The study used a longitudinal, multilevel panel design with clustered observations at asset, system, and site levels. The final analytic sample included 18 cases (10 manufacturing plants and 8 utility-scale segments), 96 systems, and 1,842 assets observed over 180 days, yielding 17,280 system-days after data coverage screening. Composite indices representing observability, synchronization quality, integration readiness, and data integrity demonstrated acceptable internal consistency (α = 0.78–0.88). Multilevel regression results indicated that optimization exposure was associated with lower downtime and improved continuity. Downtime decreased by 0.74 hours per system-month (95% CI −1.05 to −0.43, p < 0.001) and availability increased by 1.9 percentage points (95% CI 1.1 to 2.7, p < 0.001). Efficiency outcomes improved, with normalized loss intensity decreasing by 3.6% (95% CI −5.2 to −2.0, p < 0.001) and voltage deviation indices decreasing by 4.1% (95% CI −6.8 to −1.4, p = 0.003). Integrity outcomes improved, as interruption rates decreased (IRR = 0.86, 95% CI 0.79 to 0.94, p = 0.001) and misoperation-related events declined (IRR = 0.81, 95% CI 0.70 to 0.94, p = 0.006). Data quality improved, with completeness increasing by 1.5 percentage points (95% CI 0.9 to 2.1, p < 0.001) and alignment issue rates decreasing by 0.8 percentage points (95% CI −1.2 to −0.4, p < 0.001). Domain moderation indicated stronger downtime reductions in manufacturing and stronger integrity gains in utilities. Mediation evidence suggested that observability and synchronization quality explained a meaningful portion of reliability and integrity improvements. These findings establish digital twin–enabled optimization as a critical lever …
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: EIC Optimization
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Utility Systems
  topic: keywords
creator: Tasnim Kabir
rights: Copyright (c) 2025 Tasnim Kabir (Author)
date: 2025-06-22
publication date: 2025-06-22
starting page: 404
ending page: 451
doi: 10.63125/pqfdjs15

PREDICTIVE ANALYTICS AS A STRATEGIC TOOL FOR FINANCIAL FORECASTING AND RISK GOVERNANCE IN U.S. CAPITAL MARKETS
type: item
description: This study addresses a persistent problem in U.S. capital-markets organizations: even with rapid adoption of predictive analytics, many enterprises still struggle to translate analytics investments into consistently accurate, decision-ready financial forecasts and stronger risk governance. The purpose was to quantify how Predictive Analytics Capability (PAC) influences Financial Forecasting Performance (FFP) and Risk Governance Effectiveness (RGE) within cloud-enabled and enterprise analytics cases. Using a quantitative cross-sectional, case-based design, data were collected from professionals working in enterprise finance and risk functions across multiple organizational cases; after removing incomplete responses (n = 18) and low-quality response patterns (n = 6), the final analytic sample was N = 240 respondents from forecasting/finance (34.6%), risk management (28.8%), compliance and governance (18.3%), analytics/quant teams (12.1%), and audit/controls (6.2%). Key variables were PAC as the independent variable, FFP and RGE as dependent variables, with role level and years of experience treated as controls. The analysis plan included descriptive statistics, reliability testing, Pearson correlations, and multiple regression with model diagnostics. Descriptive results indicated moderately high levels on a 1–5 Likert scale: PAC M = 3.87 (SD = 0.61), FFP M = 3.74 (SD = 0.66), and RGE M = 3.79 (SD = 0.63). Measurement reliability was strong (PAC α = 0.89; FFP α = 0.87; RGE α = 0.90). Correlation results supported the hypothesized relationships: PAC was strongly associated with FFP (r = 0.61, p < .001) and with RGE (r = 0.58, p < .001), while FFP was also positively related to RGE (r = 0.55, p < .001). Regression analyses reinforced the headline findings: PAC was the dominant predictor of FFP (B = 0.68, β = 0.59, p < .001) in a significant model (F(3,236) = 56.21, p < .001; R² = 0.42). For RGE, both PAC (B = 0.38, β = 0.33, p < .001) and FFP (B = 0.41, β = 0.37, p < .00…
subject: Articles
  topic: section
subject: Predictive Analytics Capability
  topic: keywords
subject: Financial Forecasting Performance
  topic: keywords
subject: Risk Governance Effectiveness
  topic: keywords
subject: Capital Markets
  topic: keywords
subject: Quantitative Cross-Sectional Case Study
  topic: keywords
creator: Md. Towhidul Islam
creator: Alifa Majumder Nijhum
creator: Mst. Shahrin Sultana
rights: Copyright (c) 2022 Md. Towhidul Islam, Alifa Majumder Nijhum, Mst. Shahrin Sultana (Author)
date: 2022-06-30
publication date: 2022-06-30
starting page: 238
ending page: 273
doi: 10.63125/2rpyze69

ADVANCED ENGINEERING MATERIALS AND PERFORMANCE-BASED DESIGN FRAMEWORKS FOR RESILIENT RAIL-CORRIDOR INFRASTRUCTURE
type: item
description: This study addresses a persistent problem in rail corridor infrastructure: service disruptions and accelerated asset deterioration reduce reliability, yet decision makers often lack quantitative evidence showing how advanced engineering materials and performance-based design governance jointly translate into resilience outcomes. The purpose was to test whether Advanced Engineering Materials Integration (AEMI) and Performance Based Design Maturity (PBDM) predict Rail Corridor Resilience Performance (RCRP) using a quantitative, cross sectional, case-based design across enterprise rail projects. Data were collected from 212 rail engineering and operations professionals (mean experience 9.7 years, SD 4.8), and 83.0 percent reported direct involvement in the studied corridor cases. Measures showed strong reliability: AEMI (8 items, Cronbach alpha 0.86), PBDM (8 items, alpha 0.88), and RCRP (8 items, alpha 0.84). Descriptively, respondents reported moderate to high implementation (AEMI M 3.68, SD 0.59; PBDM M 3.74, SD 0.63) and comparatively higher resilience performance (RCRP M 3.81, SD 0.56); durability improvement was the strongest AEMI facet (M 3.92, SD 0.71), while restoration time target integration was the weakest PBDM facet (M 3.41, SD 0.83). The analysis plan applied descriptive statistics, internal consistency testing, Pearson correlations, and multiple regression with controls for perceived hazard exposure severity and asset age or condition pressure, plus an interaction term. Correlations supported the hypothesized relationships (AEMI to RCRP r 0.58, PBDM to RCRP r 0.62, AEMI to PBDM r 0.55; all p < .001). In regression, AEMI (beta 0.28, p < .001) and PBDM (beta 0.39, p < .001) jointly explained 49 percent of variance in RCRP (R2 0.49, Adj. R2 0.48); adding controls improved fit to R2 0.52 and effects remained significant (AEMI beta 0.25; PBDM beta 0.36; both p < .001). The AEMI by PBDM interaction was positive but small (beta 0.10, p = .031), indicating that …
subject: Articles
  topic: section
subject: Rail Corridor Resilience
  topic: keywords
subject: Advanced Engineering Materials Integration
  topic: keywords
subject: Performance Based Design Maturity
  topic: keywords
subject: Reliability and Recovery
  topic: keywords
subject: Regression Modeling
  topic: keywords
creator: Hammad Sadiq
creator: Md Sarwar Hossain Shuvo
rights: Copyright (c) 2025 Hammad Sadiq, Md Sarwar Hossain Shuvo (Author)
date: 2025-06-28
publication date: 2025-06-28
starting page: 368
ending page: 403
doi: 10.63125/c3g3sx44

QUANTITATIVE STUDY ON MACHINE LEARNING-BASED INDUSTRIAL ENGINEERING APPROACHES FOR REDUCING SYSTEM DOWNTIME IN U.S. MANUFACTURING PLANTS
type: item
description: This study addresses the problem that unplanned equipment downtime continues to erode productivity and maintenance budgets in smart manufacturing, yet many organizations implement machine learning solutions without clear evidence about which capability mix most strongly translates analytics into measurable downtime reduction. The purpose of the study was to quantify how machine learning enabled industrial engineering capabilities influence downtime reduction and to identify the most influential predictors that organizations should prioritize. Using a quantitative, cross sectional, case-based design, data were collected from manufacturing plant cases operating enterprise and shop floor environments that typically integrate CMMS, SCADA, and ERP workflows. The final sample included 214 usable respondent cases from U.S. plants. Key variables included predictive maintenance capability, monitoring and anomaly detection capability, data quality and availability, integration capability across maintenance and production systems, workforce readiness, and management support, with downtime reduction as the dependent outcome measured as a composite Likert 1 to 5 index capturing reductions in downtime frequency and duration and faster response and recovery. The analysis plan applied descriptive statistics, reliability testing, Pearson correlation analysis, and multiple regression to estimate unique effects while controlling for overlap among predictors. Reliability was acceptable to strong, including downtime reduction (Cronbach alpha = 0.88). Descriptive results indicated moderate to high implementation levels (grand mean M = 3.62, SD = 0.71) and moderate perceived downtime reduction (M = 3.59, SD = 0.72). Correlations showed that downtime reduction was positively associated with all predictors (r range = 0.44 to 0.61, p < .01), with the strongest relationships observed for predictive maintenance (r = 0.61) and integration capability (r = 0.58). In the multivariate model, the pr…
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Downtime Reduction
  topic: keywords
subject: Workflow Integration
  topic: keywords
subject: Manufacturing Analytics
  topic: keywords
subject: Management Support
  topic: keywords
creator: Shofiul Azam Tarapder
creator: Md. Al Amin Khan
rights: Copyright (c) 2024 Shoflul Azam Tarapder, Md. Al Amin Khan (Author)
date: 2024-06-30
publication date: 2024-06-30
starting page: 526
ending page: 558
doi: 10.63125/kr9r1r90

INTEGRATING ERP, IOT, AND AI FOR SMART WAREHOUSE AUTOMATION AND LOGISTICS
type: item
description: Smart warehouse automation and logistics performance were examined in this study through the lens of closed-loop integration among enterprise resource planning (ERP), Internet of Things (IoT), and artificial intelligence (AI). A quantitative multi-site panel design was applied using 28 warehouse sites tracked across 52 weekly periods, producing 1,456 site–week observations after excluding 20 outage-flagged weeks. Data completeness was high across systems, with ERP 99.3%, WMS/WCS 98.8%, IoT 96.2%, and AI 93.5% field coverage. Integration maturity was measured on a 0–100 scale with a mean of 67.4 and a standard deviation of 11.2, and data quality averaged 0.92 (SD 0.04). Descriptive outcomes indicated inventory record accuracy of 97.1% (SD 1.6), pick errors of 3.4 per 1,000 lines (SD 1.5), order cycle time of 21.8 hours (SD 7.9), exception volume of 148.0 tickets/week (SD 71.0), trailer dwell time of 74.6 minutes (SD 38.2), and on-time shipment rate of 92.3% (SD 4.8). Fixed-effects panel regression models with time-period controls and clustered standard errors showed that higher integration maturity was associated with improved warehouse execution, including higher inventory accuracy (β = 0.031, 95% CI 0.011 to 0.051, p = 0.004), fewer pick errors (β = −0.028, 95% CI −0.045 to −0.011, p = 0.001), shorter order cycle time (β = −0.184, 95% CI −0.239 to −0.129, p < 0.001), and lower exception ticket volume (β = −1.62, 95% CI −2.15 to −1.09, p < 0.001). Logistics outcomes also improved, with higher on-time shipment (β = 0.052, 95% CI 0.026 to 0.078, p < 0.001), reduced trailer dwell (β = −0.92, 95% CI −1.41 to −0.43, p < 0.001), and reduced dispatch delay (β = −0.38, 95% CI −0.57 to −0.19, p < 0.001). Mediation tests indicated partial mediation by data quality; after mediator inclusion, the integration effect decreased for cycle time from −0.184 to −0.081 (p 0.008) and for exceptions from −1.62 to −0.74 (p 0.012), while data quality remained significant for cycle time (β …
subject: Articles
  topic: section
subject: Erp Integration
  topic: keywords
subject: IOT Visibility
  topic: keywords
subject: Ai Analytics
  topic: keywords
subject: Smart Warehousing
  topic: keywords
subject: Logistics Performance
  topic: keywords
creator: Md Jahidul Islam
creator: Md Newaz Shorif
rights: Copyright (c) 2025 Md Jahidul Islam, Md Newaz Shorif (Author)
date: 2025-12-15
publication date: 2025-12-15
starting page: 102
ending page: 142
doi: 10.63125/13zhmy64