http://dspace.dtu.ac.in:8080/jspui/handle/123456789/105 2026-07-02T10:03:33Z 2026-07-02T10:03:33Z SISODIYA, ABHISHEK KUMAR Kumar, Pravin (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22945 2026-06-25T05:08:50Z 2026-06-01T00:00:00Z

Title: CRITICAL FACTORS FOR GENERATIVE AI- DRIVEN GREEN VALUE CREATION IN SUPPLY CHAINS: A HIERARCHICAL FUZZY BEST WORST METHOD APPROACH Authors: SISODIYA, ABHISHEK KUMAR; Kumar, Pravin (SUPERVISOR) Abstract: Green value creation has become a central objective for modern supply chains as organizations increasingly adopt circular and sustainable practices. While prior research has identified multiple environmental and sustainability-related factors across supply chain activities, the role of advanced digital technologies—particularly Generative Artificial Intelligence (GenAI)—in shaping and prioritizing these factors remains insufficiently explored. Moreover, existing studies often rely on conventional analytical approaches and lack structured decision-making frameworks capable of addressing uncertainty and expert subjectivity. This study aims to identify and prioritize the critical factors influencing GenAI-driven green value creation in supply chains using a Hierarchical Fuzzy Best–Worst Method (HFBWM) approach. Based on an extensive review of the literature and expert consultation, five key supply chain dimensions—Supplier, Product, Packaging, Logistics, and Consumption—along with eighteen associated sub-factors are identified and validated. The HFBWM is employed to systematically capture expert judgments under uncertainty and to derive local and global priority weights. The results reveal that product-related factors, particularly design for reuse, modular design, and circular product design, are the most influential drivers of green value creation, followed by sustainable packaging and consumption-oriented factors. Scenario-based analysis further demonstrates that GenAI capabilities—through iv interactive and non-interactive knowledge search—enhance decision-making quality, reduce dependence asymmetry, and strengthen inter-organizational collaboration, thereby reshaping the prioritization of green value creation factors. The study contributes to the literature by integrating fuzzy multi-criteria decision making with GenAI-enabled supply chain capabilities and offers a practical decision support framework for managers seeking to prioritize high-impact sustainability initiatives. The proposed approach provides actionable insights for leveraging GenAI to support strategic green value creation in complex and uncertain supply chain environments.

2026-06-01T00:00:00Z SONI, PULKIT Zunaid, Mohammad (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22926 2026-06-25T04:57:31Z 2026-05-01T00:00:00Z

Title: NUMERICAL ANALSIS AND MULTI-OBJECTIVE OPTIMISATION OF THE NACA 2415 AIRFOIL USING A TAGUCHI-FUZZY FRAMEWORK Authors: SONI, PULKIT; Zunaid, Mohammad (SUPERVISOR) Abstract: Small fixed-wing unmanned aerial vehicles operating at chord Reynolds numbers of Re = 10⁵–5×10⁶ face a fundamental aerodynamic challenge: the gap between two dimensional section performance predicted by computational fluid dynamics and the drastically degraded efficiency of real, aspect-ratio-constrained three-dimensional wings. This study addresses that gap through a three-phase hierarchical investigation of the NACA 2415 aerofoil, integrating Taguchi design of experiments, Mamdani fuzzy multi-objective optimisation, steady-state Reynolds-Averaged Navier–Stokes simulation, and surrogate-assisted optimisation into a single systematic framework validated against NACA Technical Report 824 experimental data. Phase 1 deploys a Taguchi L25(5⁵) orthogonal array — reducing a 3,125-run full factorial to 25 balanced simulations — to simultaneously screen five RANS turbulence closures (Spalart–Allmaras, k-ε Realizable, k-ω SST, SST γ–Reθ, and Reynolds Stress Model), five Reynolds numbers (1–12×10⁶), five angles of attack (−4° to 16°), five turbulence intensities (0.05%–5.00%), and four surrogate optimisation strategies (RSM-Kriging, NSGA-II, Sparse RSM, and Neural Network Screening). Three conflicting aerodynamic responses — lift coefficient, drag coefficient, and lift-to-drag ratio — are unified into a scalar Multi-Performance Characteristic Index via a 27-rule Mamdani fuzzy inference system with corrected strict-inequality boundary membership evaluation, a previously unreported defect whose correction changes the turbulence model ANOVA contribution from a spurious 12.30% to the physically correct 1.83%. One-way ANOVA identifies angle of attack as the dominant factor (ρ = 80.99–85.70%), with Reynolds number second (ρ ≈ 8–10%). The k-ω SST model achieves the highest multi-objective η(MPCI) level mean (−9.202 dB) due to its Bradshaw adverse-pressure-gradient limiter and structural turbulence-intensity insensitivity via cross-diffusion. Sparse RSM achieves the highest Weighted Composite Score of 9.13/10, uniquely detecting the NACA 2415 drag-bucket interior minimum at α ≈ −0.75°, independently confirmed by Neural Network Screening at α ≈ −0.77°. Phase 2 deploys a Taguchi L9(3³) array exclusively with k-ω SST across a refined design space (Re = 6–12×10⁶, α = 4°–8°, TI = 0.05%–0.50%). The confirmed optimal configuration — Re = 12×10⁶, α = 8°, TI = 0.10° — yields CL = 1.038, CD = 0.015711, and |CL/CD| = 66.08, with a Taguchi additive model prediction error of only 0.26%, validating negligible factor interactions. Turbulence intensity contributes ρ ≈ 0.00% (F = 0.04) within the tested range, providing a practically significant result that eliminates TI as a source of CFD modelling uncertainty for this application. Phase 3 extends the Phase 2 optimum to a three-dimensional finite-wing RANS simulation at AR = 0.25 (b = 0.5 m, c = 2.0 m, A_ref = 1.0 m²). The aerodynamic outputs — CL = 0.12921, CD = 0.014899, |CL/CD| = 8.67, Lift = 607.908 N, Drag = 70.096 N — reveal an 86.9% efficiency collapse from the two-dimensional optimum, driven by a tip-vortex-induced downwash of ε ≈ 9.33° that reduces the effective angle of attack from +8° to approximately −1.43°. Three independent CFD visualisations — velocity pathlines, static pressure vectors, and velocity magnitude vectors — provide mutually corroborating topological, thermodynamic, and kinematic evidence confirming that the entire span lies within the tip-vortex induction zone and no two dimensional flow region exists. The study conclusively establishes that the binding aerodynamic limitation of the platform is planform geometry rather than section performance, motivating a redesign to AR = 6–8 to recover 75%–86% of the two dimensional efficiency ceiling.

2026-05-01T00:00:00Z TRIPATHI, AMRISH Garg, S.K. (SUPERVISOR) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22919 2026-06-25T04:56:31Z 2026-06-01T00:00:00Z

Title: DEVELOPING A PREDICTIVE MODEL FOR HYDRAULIC MACHINE MAINTENANCE Authors: TRIPATHI, AMRISH; Garg, S.K. (SUPERVISOR) Abstract: The hydraulic system is an essential infrastructure element of all kinds of heavy machinery used in several applications such as manufacturing, mining, construction, aviation, and processing industries. There are several types of degradations that occur in these hydraulic systems (for example, pump cavitation, seal leakage, valve erosion, accumulator failure, fluid contamination, among others). This leads to unexpected malfunctions accompanied by lengthy downtime periods, posing safety threats and causing costs. Traditionally, there have been no successful proactive maintenance strategies applied to address these challenges. The current research suggests a novel approach to predictive maintenance for hydraulic systems. The suggested approach relies on the Hydraulic Condition Monitoring Dataset (Hewli et al., 2015), containing 2,205 operating cycles collected via 17 sensors. Such sensors collect different types of physical variables, namely pressure, flow, temperature, vibration, and efficiency. The considered case study focuses on seven distinct machine learning algorithms, including Isolation Forest, One Class SVM, K-Means Clustering, DBSCAN, Autoencoder neural networks, 1D-CNN, and XGBoost. The analysis of predictive maintenance involves a set of steps to be taken. The first step refers to data preprocessing, including such aspects as handling missing values, normalization, and filtering out outliers. The second step relates to feature engineering. This stage is characterized by calculating rolling statistics and creating health indexes. The next step refers to data analysis, such as creating a correlation heat map and performing principal component analysis. Afterward, each of the algorithms is applied to the dataset, and a comparative analysis is conducted based on a 5 fold cross-validation. Moreover, bibliometric analysis is presented within the current research .Sources for bibliometric analysis are taken from three databases – Scopus, Web of Science, and IEEE Xplore. The obtained data provide an overview of global research trends in the area of interest, pointing at prominent researchers, popular methodologies, and certain limitations. Bibliometric analysis indicates China to be the leading contributor, generating 30.1% of all publications under consideration. Besides, it becomes evident that deep learning approaches have dominated this field starting from 2021. The experimental evaluation proves that XG Boost provides the best predictive performance: Accuracy 94.2%, Recall 92.8%, F1 Score 91.5%, and AUC-ROC0.971, with inference time of 158 milliseconds. 1D CNN iv achieves second-best accuracy (91.7%), yet its inference time is threefold higher compared to XGBoost, thus limiting its suitability for deployment at the edge. A hierarchical framework with three levels of alertness for maintenance (Normal/Alert/Critical) is proposed for translating XG Boost's predictions into a maintenance strategy. Most important sensors are Hydraulic Pressure (PS1), Volume Flow (FS1), and Motor Power, identified via a feature importance analysis. These results contribute significantly to a reproducible algorithmic benchmark for India's manufacturing sector aiming to shift from reactive to predictive maintenance practices. Keywords: predictive maintenance, hydraulic systems, machine learning, XG Boost, anomaly detection, condition monitoring, Industry 4.0, sensor fusion, fault prognosis, UCI Hydraulic Dataset, bibliometric analysis .

2026-06-01T00:00:00Z BHARDWAJ, PRIYATOSH Garg, S.K. (SUPERVISION) http://dspace.dtu.ac.in:8080/jspui/handle/repository/22918 2026-06-25T04:56:21Z 2026-06-01T00:00:00Z

Title: CRITICAL SUCCESS FACTORS TO IMPLEMENTATION OF SPEED TO RECOVERY IN SUSTAINABLE SUPPLY CHAIN DEVELOPMENT Authors: BHARDWAJ, PRIYATOSH; Garg, S.K. (SUPERVISION) Abstract: Global business today is unpredictable, and companies trying to create sustainable supply chains are struggling with many different types of catastrophe— geopolitical disruptions, climate-change-induced instability, pandemic level systemic failures and rapid changes in technology. The speed to recover (StR) is now considered an important capability of a supply chain; it measures how quickly a supply chain can get back to its baseline performance (or a better level) after a d isruption. There has been growing interest in supply chain resilience and supply chain sustainability but there is still limited knowledge about identifying and prioritizing critical success factors (CSFs) used to implement StR within the context of a sustainable supply chain; this gap in the academic literature remains largely unexplored.. In response to the gap in existing literature regarding how to develop a comprehensive criterion for CSF evaluation, this research constructs a decision making framework designed to integrate both methods of fuzzy DEMATEL (Fuzzy Decision Making Trial and Evaluation Laboratory) for mapping out the causal structure of the interrelationships of CSFs, and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for ranking or prioritizing CSFs by how close they come to the ideal performance or benchmark. In utilizing both methodologies, this research was able to provide a more holistic decision supporting framework for individuals and organizations when evaluating CSFs than could be provided by each methodology used independently. A comprehensive two-phase literature synthesis yielded an initial pool of twenty candidate CSFs, subsequently refined to thirteen factors through a structured two-round Delphi expert validation process. Primary data were collected from a purposive sample of fifteen senior supply chain professionals representing diverse industry sectors including manufacturing, logistics, e-commerce, pharmaceutical, automotive, textile, food and beverage, chemical, and humanitarian supply chain operations. The Fuzzy DEMATEL analysis classified the thirteen CSFs into cause and effect groups. Six CSFs – Top Management Commitment, Organizational Resilience Culture, Digital iv Technology Adoption, Cross-Functional Collaboration, Environmental Regulatory Compliance, and Circular Economy Practices – were identified as cause-group factors functioning as primary systemic drivers. The subsequent TOPSIS analysis yielded a priority ranking in which Top Management Commitment (Ci* = 0.610), Organizational Resilience Culture (Ci* = 0.572), and Circular Economy Practices (Ci* = 0.565) emerged as the three most critical CSFs. Notably, the sustainability-oriented factors ranked among the top four across both methods, empirically validating the proposition that sustainability and supply chain resilience are complementary rather than competing strategic objectives. Findings indicate that proactive organizational investment in leadership commitment, cultural resilience orientation, digital infrastructure, and sustainability practices simultaneously accelerates recovery speed and strengthens long-term sustainability credentials. The integrated Fuzzy DEMATEL-TOPSIS framework provides supply chain managers with an actionable, evidence-based roadmap for sequential resource allocation and strategic capability development. Keywords: Critical Success Factors, Speed To Recovery, Sustainable Supply Chain Management, Fuzzy DEMATEL, TOPSIS, Supply Chain Resilience, Industry 4.0, Multi criteria Decision Making, Causal Analysis, Circular Economy.

2026-06-01T00:00:00Z