It is now widely recognized that many important events in the life cycle of complex engineering systems cannot be foreseen in advance. From its origin in ecological systems, operating without the use of foresight, resilience theory prescribes presuming ignorance about the future, and designing systems to manage unexpected events in whatever form they may take. However, much confusion remains as to what constitutes a resilient system and the implications for engineering systems. Taking steps toward a synthesis across a fragmented body of research, this paper analyses 251 definitions in the resilience literature, aiming to clarify key distinctions in the resilience concept. Asking resilience of what , to what , and how , we first distinguish systems serving higher ends and systems that are ends in themselves, and, within these, performance variables to be minimized, preserved, or maximized. Second, we distinguish systems subject to adverse events, adverse change, turbulence, favorable events, favorable change, and variation. Finally, we distinguish systems capable of recovery, absorption, improvement, graceful degradation, minimal deterioration, and survival. Together, these distinctions outline a morphology of resilient systems and suggest answers to the principal design questions, which must be asked of any resilient engineering system.

Due to increasing turbulence in global sales and supply markets, today's supply chains are exposed to numerous risks. Companies have discovered that a systematic management of potential incidences, e.g. supplier failures and unexpected demand changes, holds a competitive advantage. However, risk management in global supply chains is very complex. This article contributes to the existing literature by making this complexity more manageable through the introduction of a system-oriented view into supply chain risk management (SCRM). Two new, interrelated modelling approaches are proposed: a Supply Chain Risk Structure Model describes the system that determines the causes and effects of supply chain risks, i.e. the factors and their relationships. The second model, the Supply Chain Risk Dynamics Model, is used to model the possible dynamics of the risk development. This article also demonstrates how these models integrate into a SCRM process framework. A case study with three companies shows the feasibility of the approach.