Green Supplier Selection using MCDM: A Comprehensive Review of Recent Studies

Abstract

This study provides an extensive analysis of current research on the crucial topic of choosing environmentally friendly suppliers by using multi-criteria decision-making (MCDM) methods. The assessment and choice of environmentally conscious suppliers have become essential in modern business operations due to the growing worries about the environment throughout the world and the necessity of sustainable supply chain management. This study provides an in-depth analysis of a wide range of MCDM techniques used in green supplier selection, highlighting the advantages, disadvantages, and new directions of each approach. Through the synthesis and analysis of the results of multiple recent studies, this review advances our understanding of the changing environment surrounding green supplier selection and offers useful information to practitioners in the field as well as scholars. In the context of green supplier selection, it emphasizes the necessity of a nuanced and context-specific approach to MCDM, eventually developing environmentally conscious and sustainable procurement methods that are in line with the changing demands of today's corporate environment.

IVPF-AHP integrated VIKOR methodology in supplier selection of three-dimensional (3D) printers

“…Büyüközkan and Göçer [32] utilized the CODAS method under Pythagorean fuzzy in selection of the right 3D printing technology. Sahoo and Goswami [33] examined current studies by reviewing the literature on selecting environmentally friendly suppliers. Amiri et.…”

Exploring the Impact of Artificial Intelligence Integration on Cybersecurity: A Comprehensive Analysis

“…• APTs: APTs represent highly sophisticated and targeted cyber-attacks orchestrated by well-resourced adversaries, including nation-state actors and organized cybercrime groups [12,13]. APTs are characterized by stealthy infiltration, prolonged reconnaissance, and persistent exploitation of vulnerabilities to exfiltrate sensitive data or sabotage critical infrastructure.…”

“…• Deep learning-based detection: Deep learning-based detection represents the cutting-edge of AI-powered threat detection, leveraging neural networks with multiple layers of interconnected nodes to analyze complex data and extract meaningful features [18,19]. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel at identifying subtle patterns and anomalies in large datasets, making them well-suited for detecting sophisticated cyber threats, including advanced malware, phishing attacks, and insider threats [13,14]. While deep learning-based detection requires substantial computational resources and labeled training data, it offers unparalleled accuracy and adaptability in detecting and mitigating cyber threats.…”

“…Despite the significant promise of integrating AI into CS, several challenges and limitations must be addressed to effectively realize its full potential [9,13]. This section delves into key issues such as data privacy concerns, algorithm biases, adversarial attacks, and the shortage of skilled AI CS professionals while exploring strategies for mitigating these challenges.…”

“…To address data privacy concerns associated with AI-driven CS, organizations should implement robust data privacy and governance frameworks [12,13]. These frameworks ensure compliance with relevant regulations such as GDPR, CCPA, and industry-specific standards.…”