Everything you need to know about contemporary strategy analysis This accessible book concentrates on the fundamentals of value creation with an emphasis on practicality. It focuses on strategy implementation that reflects the needs of firms to reconcile scale economies with entrepreneurial flexibility, innovation with cost efficiency, and globalization with local responsiveness. Contemporary Strategy Analysis, 8th Edition also incorporates some of the key strategic issues of today including: post-financial crisis adjustment, the continuing rise of China, India and Brazil, and the increased emphasis on ethics and sustainability. It also covers strategy in not-for-profit organizations.
A strategy text on value creation with case studies The ninth edition of Contemporary Strategy Analysis: Text and Cases focuses on the fundamentals of value creation with an emphasis on practicality. Topics in this edition include: platform-based competition and ecosystems of related industries; the role of strategy making processes; mergers, acquisitions and alliances; and strategy implementation. Within the twenty case studies, students will find leading companies that are familiar to them. This strategy analysis text is suitable for MBA and advanced undergraduate students.
Robert M. Grant combines a highly accessible writing style with a concentration on the fundamentals of value creation and an emphasis on practicality in this leading strategy text. In this new edition, he includes an even greater focus on strategy implementation that reflects the needs of firms to reconcile scale economies with entrepreneurial flexibility, innovation with cost efficiency, and globalization with local responsiveness. This edition also incorporates some of the key strategic issues of today including: post-financial crisis adjustment, the continuing rise of China, India and Brazil, and the increased emphasis on ethics and sustainability. Coverage is also provided on strategy in not-for-profit organizations. Contemporary Strategy Analysis, Text and Cases 8th Edition combines the text with an updated collection of 20 case studies. It is suitable for both MBA and advanced undergraduate students. Additional teaching resources are also available for instructors, including an instructor’s manual, case teaching notes, test bank, teaching slides, case video clips and extra cases. All of these resources can be accessed via the companion website: www.contemporarystrategyanalysis.com
Robert M. Grant combines a highly accessible writing style with a concentration on the fundamentals of value creation and an emphasis on practicality in this leading strategy text. In this new edition several topics have increased emphasis including: platform-based competition and 'ecosystems' of related industries; the role of strategy making processes/practices; mergers, acquisitions and alliances; and additional emphasis on strategy implementation.
Good Strategy/Bad Strategy clarifies the muddled thinking underlying too many strategies and provides a clear way to create and implement a powerful action-oriented strategy for the real world. Developing and implementing a strategy is the central task of a leader. A good strategy is a specific and coherent response to—and approach for—overcoming the obstacles to progress. A good strategy works by harnessing and applying power where it will have the greatest effect. Yet, Rumelt shows that there has been a growing and unfortunate tendency to equate Mom-and-apple-pie values, fluffy packages of buzzwords, motivational slogans, and financial goals with “strategy.” In Good Strategy/Bad Strategy, he debunks these elements of “bad strategy” and awakens an understanding of the power of a “good strategy.” He introduces nine sources of power—ranging from using leverage to effectively focusing on growth—that are eye-opening yet pragmatic tools that can easily be put to work on Monday morning, and uses fascinating examples from business, nonprofit, and military affairs to bring its original and pragmatic ideas to life. The detailed examples range from Apple to General Motors, from the two Iraq wars to Afghanistan, from a small local market to Wal-Mart, from Nvidia to Silicon Graphics, from the Getty Trust to the Los Angeles Unified School District, from Cisco Systems to Paccar, and from Global Crossing to the 2007–08 financial crisis. Reflecting an astonishing grasp and integration of economics, finance, technology, history, and the brilliance and foibles of the human character, Good Strategy/Bad Strategy stems from Rumelt’s decades of digging beyond the superficial to address hard questions with honesty and integrity.
Cases for Contemporary Strategy Analysis has been developed to accompany Grant's best-selling strategic management text, but can also be used alongside other strategy texts. New, in-depth international cases. Can be used alongside the best-selling textbook Contemporary Strategy Analysis or as a stand alone text due to its broad view. Cases include Laura Ashley, Daimler, the Virgin Group, Eastman Kodak, Harley Davidson, The Royal Dutch/Shell Group, Amazon.Com and others.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
