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"Existing supply chain management books focus on logistics, operations management, and purchasing. Sanders provides supply chain managers with a completely unique approach, presenting SCM from a balanced, integrative, and business-oriented viewpoint. Rather than examining SCM as an offshoot of other business functions, this book discusses it as a boundary-spanning function that is intertwined with other organizational functions. It contains extensive pedagogy and solved problems to make difficult concepts easy to understand. A rich set of current examples are also included to make the material more relevant. Supply chain managers will finally have a resource that takes the business perspective"--

ABSTRACTThe impact of forecast error magnification on supply chain cost has been well documented. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study extends research to consider the impact of forecast bias, and the complex interaction between these variables. Simulating a two‐stage supply chain using realistic cost data we test the impact of bias magnification comparing two scenarios: one with forecast sharing between retailer and supplier, and one without. We then corroborate findings via survey data. Results show magnification of forecast bias to have a considerably greater impact on supply chain cost than magnification of forecast standard deviation. Particularly damaging is high bias in the presence of high forecast standard deviation. Forecast sharing is found to mitigate the impact of forecast error, however, primarily at higher levels of forecast standard deviation. At low levels of forecast standard deviation the benefits are not significant suggesting that engaging in such mitigation strategies may be less effective when there is little opportunity for improvement in accuracy. Furthermore, forecast sharing is found to be much less effective against high levels of bias. This is an important finding as managers often deliberately bias their forecasts and underscores the importance of exercising caution even with forecast sharing, particularly for forecasts that have inherently large errors. The findings provide a deeper understanding of the impact of forecast errors, suggest limitations of forecast sharing, and offer implications for research and practice alike.

"What started as an inquiry into how executives can adopt AI to harness the best of human and machine capabilities turned into a much more profound rumination on the future of humanity and enterprise. This is a wake up call for business leaders across all sectors of the economy. Not only should you implement AI regardless of your industry, but once you do, you should fight to stay true to your purpose, your ethical convictions, indeed your humanity, even as our organizations continue to evolve. While not holding any punches about the dangers posed by AI, this book uniquely surveys where technology is limited, and where the true opportunities lie amidst all the disruptive change currently underway. As such, it is distinctively more optimistic than many of the competing titles on Big Technology. This is a compelling book that weaves together philosophical, psychological, and legal insights; organizational governance, operations, and strategy; and technological breakthroughs and limitations. The authors set out to identify where humans and machines can best complement one another to create an enterprise greater than the sum total of its parts: the Humachine. Combining the business and predictive acumen of Professor Nada R. Sanders, PhD (Distinguished Professor at Northeastern University) with the legal and philosophical perspective of John D. Wood, Esq. (Attorney and Member of the New York State Bar) the authors combine their strengths as internationally recognized experts in forecasting and sustainable business to bring us this profound yet accessible book. This is a "must read" for everyone interested in the future of human enterprise."

Cover -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- List of Figures -- List of Tables -- Preface -- Introduction: Defining the Humachine -- 1: The Fourth Industrial Revolution -- "Deep Blue" Blues -- Kasparov's Law: Triumph of Process -- The New Kid in Town -- The "Big Bang" of Modern AI -- "Robot Proof" the Workforce: Embracing Moravec's Paradox -- Machines Can't Do Everything -- There Are Worse Things than Losing Your Job -- The Humachine: People, Process, Machines -- How to Get There -- Conclusion -- Notes -- 2: Pathways to Superintelligence -- Thousand Pound Gorilla Philosopher -- What Is Superintelligence? -- Using (Not Abusing) Neo-Darwinian Accounts of Intelligence -- Competition, Evolution, and Survival -- The Evolutionary Paths of Human and Machine Intelligence Split -- Case in Point -- Biological Cognitive Enhancement -- Neural Lace: Turning Humans into Cyborgs -- Whole Brain Emulation -- Collective Superintelligence -- Bostrom's Blind Spot: The Promise of Collective Intelligence -- Why Is It a Problem? -- Collective Intentionality -- Does "Microsoft" Really "Intend" Anything? -- Collective Minds Play a Causal Role in the Real World -- Some Philosophical Points That Support Collective Intentionality -- Could a Corporation Actually Be Conscious? -- Humachines and the Noosphere -- Conclusion -- Notes -- 3: The Limits of Machine Capabilities -- When Robots Hang with the Wrong Crowd -- Let's Not Indulge in Hyperbole Here -- Big Data, Algorithms, Cloud Computing, and Dark Data -- Big Data -- Algorithms -- Cloud Computing -- Dark Data -- Data Is the Foundation for AI and Machine Learning -- AI, Machine Learning, Deep Learning, and Neural Networks -- Artificial Intelligence -- Machine Learning -- Neural Networks -- Deep Learning -- What Machines Can Do -- Pattern Recognition.

ABSTRACTThe purpose of this research is to determine if prior findings that favor simple forecasting techniques and technique combinations hold true in a short‐term forecasting environment, where demand data can be quite volatile. Twenty‐two time series of daily data from a real business setting are used to test one‐period ahead forecasts, the epitome of short‐term forecasting. The time series vary systematically as to data volatility and forecast difficulty. Forecast accuracy is measured in terms of both mean absolute percentage error (MAPE) and mean percentage error (MPE).