The old cliche states that not every manager is a leader, but the more important part of that sentiment is that to be a good manager, one has to be a good leader. This perception is because good managers do more than manage. They have to lead by inspiration, they have to lead by example, and they have to lead through the best times for their organizations as well as the absolute worst times. A Systematic Guide to Leadership Selection Using Total Quality Management Techniques identifies the application gap and presents a methodology based on Total Quality Management (TQM) to support the guidance of a process to select leadership (at any level of the organization). A modification to the House of Quality and a product of the Massachusetts Institute of Technology is presented and discussed as the core of a leader selection process. Two case studies are used to reinforce the concepts and applications. Finally, the book introduces some experiments for leadership development using virtual worlds and ends with a note for the future using the metaverse and digital twins for leadership. The book is intended for professionals and executives wanting to learn more about leader selection, engineering and business students, directors of human resources, and researchers in the field of leadership.

Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.