Publisher's Synopsis
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. Readers will embark on an enlightening journey through the theory, methodology, and practical applications of DEA. The book begins with a solid foundation in the basics of DEA, exploring various models, such as the input-oriented and output-oriented approaches and the constant and variable returns to scale assumptions. Advanced DEA methods, including cross-efficiency evaluation, Malmquist productivity index, and network DEA, are also thoroughly examined. This book illustrates practical applications through real-world examples and case studies, provides guidance for conducting DEA research and analysis, discusses the limitations and challenges of DEA, and explores recent developments and future trends in DEA research. 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. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency 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. The book also addresses DEA software, implementation issues, empirical studies, and case studies. Furthermore, it explores future developments and emerging trends in DEA research, ensuring readers stay up-to-date with the latest advancements in the field.
