International Journal of Management Information Systems and Data Science

ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING INVENTORY MANAGEMENT ACROSS GLOBAL INDUSTRIAL MANUFACTURING & SUPPLY CHAIN: A MULTI-COUNTRY REVIEW

Md Khyrul Islam, Hasib Ahmed, Mahboob Al Bashar & Md Abu Taher — Wed, 01 May 2024 00:00:00 +0000

This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on inventory management within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0. Through a comparative analysis across several countries, the research analyzes quantitative and qualitative data to assess the adoption and integration of these technologies and their implications for supply chain optimization. The research methodology includes a comprehensive literature review using multiple databases and expert interviews conducted within a specific timeframe. The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance. It highlights the evolution from theoretical potential to practical applications, with an increased focus on regulatory compliance and data integrity, reflecting the industry's maturation in digital integration. Furthermore, the study explores the strategic role of AI and ML in process design and the holistic adoption of Industry 4.0 principles across the supply chain. The findings contribute to the academic literature by detailing the benefits and challenges of AI and ML implementation, offering insights for future research and practical applications in the supply chain sector. The conclusion emphasizes the transformative potential of AI and ML, advocating for their strategic implementation to foster resilience and adaptability in supply chain networks.

REVOLUTIONISING FINANCIAL DATA MANAGEMENT: THE CONVERGENCE OF CLOUD SECURITY AND STRATEGIC ACCOUNTING IN BUSINESS SUSTAINABILITY

Md Mahfuzur Rahman & Zihad Hasan Joy — Wed, 01 May 2024 00:00:00 +0000

The acceleration of digital transformation in financial management necessitates an examination of the interplay between cloud security and strategic accounting, especially concerning business sustainability. This study conducts a qualitative analysis, employing semi-structured interviews and case studies, to explore the integration of cloud security measures with strategic accounting practices. The findings reveal a significant shift in organisational strategies, where cloud security is no longer seen merely as a defence mechanism but as a crucial enabler of sustainable business practices. Enhanced transparency and accountability stemming from secure cloud platforms are key to building stakeholder trust and meeting compliance. Despite persistent concerns over cyber threats, a growing trend towards adopting comprehensive cloud security strategies underpin strategic accounting efforts, highlighting a proactive stance in addressing potential risks. This study contributes to the literature by offering a nuanced understanding of how cloud security and strategic accounting coalesce to support sustainability, illustrating this integration's complexities and strategic imperatives.

ADVANCING DATA SECURITY IN GLOBAL BANKING: INNOVATIVE BIG DATA MANAGEMENT TECHNIQUES

Mahmudul Hasan, Md Mostafizur Rahman, Md Shakawat Hossain &Md Abdul Ahad Maraj — Wed, 01 May 2024 00:00:00 +0000

In the context of the rapidly evolving digital landscape, the banking sector faces unprecedented challenges in data security due to the massive volumes of sensitive information they manage. This paper examines the implementation and efficacy of innovative big data management techniques within global banking institutions to enhance data security. It discusses the integration of predictive analytics, the impact of regulatory changes, and the adoption of emerging technologies like blockchain and advanced encryption, which collectively redefine data security strategies. The study utilises a qualitative case study approach focusing on three central banks—JPMorgan Chase & Co., HSBC Holdings plc, and the Industrial and Commercial Bank of China—highlighting how each bank utilises big data techniques to address specific security challenges, comply with regulations, and enhance customer trust. The findings underscore the crucial role of innovative data management strategies in mitigating risks and safeguarding data against cyber threats, suggesting that these technologies fulfil security needs and offer competitive advantages in customer trust and regulatory compliance. The paper concludes with strategic recommendations for banks to enhance their data security measures and suggests directions for future research in data security within the banking industry.

ADVANCED BUSINESS ANALYTICS IN TEXTILE & FASHION INDUSTRIES: DRIVING INNOVATION AND SUSTAINABLE GROWTH

Mridha Younus, Shakhauat Hossen & Md Morshedul Islam — Mon, 13 May 2024 00:00:00 +0000

This research paper explores the strategic implementation of advanced business analytics within EcoFashions Ltd., a mid-sized company in the textile and fashion industry. The central question it addresses is: How can a specific company leverage advanced analytics in the fashion sector for sustainable growth? Through a qualitative case study approach, the research examines the strategic implementation of analytics at EcoFashions to enhance operational efficiency, drive innovation, and promote sustainability. The findings reveal that the integration of analytics at EcoFashions led to a 25% reduction in material waste and a 15% increase in production efficiency, while also fostering product innovation responsive to market trends. These outcomes not only highlight the potential of business analytics to transform traditional practices within the fashion industry but also suggest that other firms can adopt similar strategies to achieve substantial improvements in sustainability and operational performance. The paper concludes with reflections on the limitations of a single case study and proposes directions for future research to validate and expand upon these findings across the broader industry. This study contributes to understanding how data-driven strategies can facilitate significant sector-wide advancements in sustainability and innovation in the textile and fashion industries.

MACHINE LEARNING AND THE STUDY OF LANGUAGE CHANGE: A REVIEW OF METHODOLOGIES AND APPLICATION

Nourin Nishat, Muniroopesh Raasetti, A S M Shoaib & Basharat Ali — Mon, 13 May 2024 00:00:00 +0000

Language change, a fundamental aspect of human communication, has long been a central focus in linguistic research. Traditional methods of analysis, while valuable, have been limited by the scale and complexity of linguistic data. The advent of machine learning (ML) offers transformative potential in this field, enabling the analysis of vast datasets and the discovery of subtle patterns that may elude manual scrutiny. This review paper comprehensively examines the current state of ML methodologies in the study of language change, synthesizing findings from 67 peer-reviewed articles. We delve into diverse ML approaches, including supervised, unsupervised, and deep learning techniques, and critically evaluate their applications across various linguistic domains, such as historical linguistics, sociolinguistics, and language contact. We address challenges related to data availability, bias, and model interpretability, emphasizing the need for transparent and rigorous methodologies. By summarizing key findings and outlining future directions, this review aims to foster interdisciplinary collaboration between linguists and computer scientists, advancing our understanding of the complex dynamics of language evolution.