Data Driven Intelligent Business Sustainability

Data-driven decision making is crucial for ensuring the long-term sustainability of businesses and economic growth. While rapid technological advancements have enabled the collection and analysis of data on an unprecedented scale, businesses face challenges in adopting evidence-based decision making. Data-Driven Intelligent Business Sustainability is a comprehensive guide that examines the challenges and opportunities presented by data-driven decision making. It covers new technologies like blockchain, IoT, and AI, explores their potential for sustainable business success, and provides guidance on managing cybersecurity threats. The book also includes case studies and examples of successful implementations of data-driven decision making, making it a practical resource for those seeking to upskill or reskill in this field. Targeted at computer science and engineering professionals, researchers, and students, the book provides valuable insights into the role of data-driven decision making in business sustainability, helping businesses achieve long-term success.

In today's academic environment, the challenge of ensuring lasting commercial and economic success for organizations has become more daunting than ever before. The relentless surge in data-driven decision-making, based on innovative technologies such as blockchain, IoT, and AI, has created a digital frontier filled with complexity. Maintaining a healthy firm that can continually provide innovative products and services to the public while fueling economic growth has become a formidable puzzle. Moreover, this digital transformation has ushered in new risks, from pervasive cybersecurity threats to the ethical challenges surrounding artificial intelligence. In this evolving landscape, academic scholars face the pressing challenge of deciphering the path to long-term organizational prosperity in an era dominated by data. Data-Driven Decision Making for Long-Term Business Success serves as guidance and insights amidst this academic challenge. It is the definitive solution for scholars seeking to uncover the complexities of data-driven decision-making and its profound impact on organizational success. Each meticulously curated chapter delves into a specific facet of this transformative journey, from the implications of modern technologies and pricing optimization to the ethics underpinning data-driven strategies and the metaverse's influence on decision-making.

The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production planning and control; knowledge based engineering; intelligent diagnostics and maintenance solutions for smart manufacturing; service engineering based on smart manufacturing capabilities; smart city interoperability and cross-platform implementation; manufacturing performance management in smart factories; industry 4.0 - digital twin; industry 4.0 - smart factory; and industry 4.0 - collaborative cyber-physical production and human systems.

The ever-expanding realm of Big Data poses a formidable challenge for academic scholars and professionals due to the sheer magnitude and diversity of data types, along with the continuous influx of information from various sources. Extracting valuable insights from this vast and complex dataset is crucial for organizations to uncover market intelligence and make informed decisions. However, without the proper guidance and understanding of Big Data analytics techniques and methodologies, scholars may struggle to navigate this landscape and maximize the potential benefits of their research. In response to this pressing need, Professor Dina Darwish presents Big Data Analytics Techniques for Market Intelligence, a groundbreaking book that addresses the specific challenges faced by scholars and professionals in the field. Through a comprehensive exploration of various techniques and methodologies, this book offers a solution to the hurdles encountered in extracting meaningful information from Big Data. Covering the entire lifecycle of Big Data analytics, including preprocessing, analysis, visualization, and utilization of results, the book equips readers with the knowledge and tools necessary to unlock the power of Big Data and generate valuable market intelligence. With real-world case studies and a focus on practical guidance, scholars and professionals can effectively leverage Big Data analytics to drive strategic decision-making and stay at the forefront of this rapidly evolving field.

Organizations are facing an array of complex challenges that demand innovative solutions. From managing a diverse workforce and harnessing the power of data analytics to adapting to remote work and the pressing need for emotionally intelligent leaders, the demands on modern businesses are constantly evolving and increasing. Staying ahead of these challenges is not only essential for survival but also for thriving in an ever-changing environment. Leveraging AI and Emotional Intelligence in Contemporary Business Organizations is a compass that guides academic scholars, students, and practitioners through the turbulent seas of modern business management. It dissects the problems and offers clear, well-researched solutions. With a team of respected researchers, academicians, and professionals at the helm, this book is a beacon of knowledge, illuminating the path to success in today's business landscape.

As the interplay of environmental, social, and governance factors becomes increasingly crucial, the global business landscape faces a monumental challenge. Climate change, social inequities, and economic uncertainties loom large, demanding immediate attention. The integration of Environmental Social Governance (ESG) principles and Artificial Intelligence (AI) technologies emerges as a powerful solution to these challenges. However, navigating the complexities of this integration, understanding its implications, and harnessing its full potential is a formidable task. Academic scholars, policymakers, and practitioners are grappling with a critical question: How can we leverage the convergence of ESG and AI to drive sustainable practices and address pressing global issues? The answer lies in comprehending the multifaceted dimensions of this integration, its ethical considerations, and its transformative potential. To gain these insights, a comprehensive resource is needed, one that offers a deep dive into ESG-AI integration, delving into empirical research, practical applications, and ethical concerns.Intersecting Environmental Social Governance and AI for Business Sustainability is a book that answers the call to action for this pressing challenge with an in-depth exploration of the evolving field of ESG and AI integration.

In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.

As the digital government field continues to evolve rapidly, scholars and professionals must stay ahead of the curve by developing innovative solutions and gaining comprehensive insights. The global landscape of digital governance is undergoing transformative shifts, necessitating a deep understanding of historical developments, current practices, and emerging trends. This urgent demand for knowledge forms the crux of the problem that the book, Emerging Developments and Technologies in Digital Government, addresses with expert knowledge and insights. The book serves as an indispensable resource for academic scholars grappling with the complexities of digital government. It critically examines historical transitions from technology-centric paradigms to people-centric models, shedding light on the global impact of open data initiatives and the vital role of human-computer interaction in reshaping government websites. For professionals and researchers across disciplines such as library sciences, administrative management, sociology, and information technology, this book becomes a beacon, offering insights and tangible solutions to navigate the multifaceted dimensions of digital government.