Nabil Abu El Ata Book order

Globalization and rapid technological advancements have ushered in significant change and uncertainty. For unprepared businesses, the forces of the Fourth Industrial Revolution can lead to unexpected challenges and crises. To navigate these complexities, companies must embrace new risk management technologies and practices that empower leaders to anticipate and adapt to evolving dynamics. This resource aids readers in understanding how algorithm-based predictive and prescriptive analytics can effectively manage risk in today’s fast-paced business landscape. It serves as a guide for business leaders and risk management professionals in global or complex operational environments. By leveraging the technological innovations discussed, leaders can enhance their understanding of risk and determine necessary actions to align with long-term goals. Case studies illustrate the practical application of these methods to identify risks proactively and inform decisions that may optimize or transform business models, strategies, and organizational structures. Proven through numerous client cases, these approaches link business risks to strategic imperatives, enabling higher profit margins and growth. As we navigate the Fourth Industrial Revolution, companies that can identify risks stemming from dynamic complexity and align their operational execution with business objectives will be better equipped for long-term success.

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, 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 basedupon the authors’ practical experiences, backed by scientific principles, and results achieved through consulting engagements with over 350 global organizations.