Springer Solving the Dynamic Complexity Dilemma: Predictive and Prescriptive Business Management: Answering the Need for a New Paradigm

Product Description This book enhances technology for dynamic complexity and business optimal control, allowing for fast and reliable impact determination. It adds a new dimension to risk management that deals with the change in behavior of cost, quality and efficiency. From the Back Cover Dynamic complexity results from hidden, unknown factors―or more precisely, interactions between factors―that can unexpectedly impact the performance of systems. When the influences of dynamic complexity are not measured and understood, new never-seen-before behaviors can come as unwelcomed surprises, which disrupt the performance of systems. Left alone, processes that were once prized for their efficiency unexpectedly begin to degrade―costs increase, while volumes and quality decline. Evidence of problems may come too late for effective resolution as technology advancements induce rapid change and compress the time available to react to that change. The results of dynamic complexity are always negative and unmanaged dynamic complexity can bring business or global systems to the point of sudden chaos. The 2009 H1N1 pandemic, 2008 Credit Crunch and 2011 Fukushima Daiichi nuclear disaster are global examples of the dangers of undiagnosed dynamic complexity.   With increasing frequency executive leaders today are discovering that their business and IT system performance levels are not meeting expectations. In most cases these performance deficiencies are caused by dynamic complexity, which lies hidden like a cancer until the symptoms reveal themselves―often when it is too late to avoid negative impacts on business outcomes. This book examines the growing business problem of dynamic complexity and presents a path to a practical solution. To achieve better predictability, organizations must be able to expose new, dangerous patterns of behavior in time to take corrective actions and know which actions will yield the optimal results. The book authors promote new methods of risk management that use data collection, analytics, machine learning and automation processes to help organizations more accurately predict the future and take strategic actions to improve performance outcomes. The presented means of achieving this goal are based upon the authors’ practical experiences, backed by scientific principles, and results achieved through consulting engagements with over 350 global organizations. About the Author Dr. Nabil Abu el Ata, Founder and CEO, Accretive Technologies as Accretive’s founder and CEO, Dr. Abu el Ata has invested over 20 years in perfecting the science behind the company’s X-Act Platform with over 15 patents. He offers a breadth of analytical skills, risk management and business intelligence expertise, as well as IT and business process management knowledge. To say Dr. Abu el Ata has a passion for mathematics, science and technology and more specifically modeling of dynamic complexity for corporate systems would be understatement. Having published two books, 15 scientific papers, and over 300 technical and management reports, he has a proven ability to absorb, process and add insight on a wide variety of technological subjects. Dr. Abu el Ata’s accomplishments include doctorate (Ph.D. and D.Sc. from Paris-Sorbonne) and bachelor’s degrees in Mathematics and a master’s degree in Physical Sciences (Royal Observatory, Cambridge University). He is a valued former Doctorate Fellow of the European Space Organization; former Data Processing Director and Advisor for the French Atomic Energy Authority; and former CTO of First Data. Dr. Abu el Ata is also an advisory board member of the European Strategic Program for Research in IT; a Steering Committee member for European Programs: Pyramid, Europicon and Itaqua; an advisor to CLS, Deutsche Bank, First Data and BNPP and an advisory board member of French Employment Organization, French Ministry of Finance, French Postal Services, one of France’s largest ba