The authors offer a revolutionary solution to risk management. It's the unknown risks that keep leaders awake at night-wondering how to prepare for and steer their organization clear from that which they cannot predict. Businesses, governments and regulatory bodies dedicate endless amounts of time and resources to the task of risk management, but every leader knows that the biggest threats will come from some new chain of events or unexpected surprises-none of which will be predicted using conventional wisdom or current risk management technologies and so management will be caught completely off guard when the next crisis hits. By adopting a scientific approach to risk management, we can escape the limited and historical view of experience and statistical based risk management models to expose dynamic complexity risks and prepare for new and never experienced events.

Dynamic complexity results from hidden, un known factors-or more precisely, interactions between factors-that can unexpectedly im pact the perfor mance of systems. When the influences of dynamic complexity are not meas ured 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 effi ciency unexpectedly begin to degrade-costs increase, while volumes and quality decline. Evidence of problems may come too late for effective resolution as technology advance ments 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.

The authors offer a revolutionary solution to risk management. It's the unknown risks that keep leaders awake at night-wondering how to prepare for and steer their organization clear from that which they cannot predict. Businesses, governments and regulatory bodies dedicate endless amounts of time and resources to the task of risk management, but every leader knows that the biggest threats will come from some new chain of events or unexpected surprises-none of which will be predicted using conventional wisdom or current risk management technologies and so management will be caught completely off guard when the next crisis hits. By adopting a scientific approach to risk management, we can escape the limited and historical view of experience and statistical based risk management models to expose dynamic complexity risks and prepare for new and never experienced events.

Dynamic complexity results from hidden, un known factors-or more precisely, interactions between factors-that can unexpectedly im pact the perfor mance of systems. When the influences of dynamic complexity are not meas ured 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 effi ciency unexpectedly begin to degrade-costs increase, while volumes and quality decline. Evidence of problems may come too late for effective resolution as technology advance ments 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.

Globalization trends and the rapid pace of technological innovations have introduced unprecedented change and uncertainty. For unprepared businesses, the drivers of the Fourth Industrial Revolution will become a constant source of surprise and crises will unfold at an ever-increasing rate. To thrive under these conditions, companies must adopt new risk management technologies and practices that enable business leaders to better anticipate and adjust to changing dynamics. This book helps readers understand how algorithm-based predictive and prescriptive analytics principles can be used to control risk in today's dynamic business environment. It serves as a reference guide for business leaders and risk management practitioners of companies that are global in reach or operate dynamically complex systems. Using the technological and scientific innovations presented in this book, business leaders can gain a wider understanding of risk and prescriptively determine which actions are necessary to ensure the business is optimally positioned to meet its stated long-term goals and objectives. Case studies show how the presented methods can be practically applied to preemptively expose risks and support decisions to optimize, transform or disrupt current business models, strategies, organizational structure and information systems when necessary to maintain a market position or outperform competitors. These methods have been proven through hundreds of client cases. By using mathematical emulation to link business risks to strategic imperatives, it becomes possible to achieve a higher annual profit margin and better growth. As we enter the Fourth Industrial Revolution, companies that are able to expose risks caused by dynamic complexity and maintain the alignment between the goals of the business and operational execution will be better prepared to make the shifts necessary for long-term success and keep the business moving toward its goals.