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Supply shortages faced in products and resources from semiconductors to natural gas in recent years have had impact massive on global economy, but such challenges are not new for supply chain professionals. Many major events in the past have disrupted supply chains: 9/11 attack in New York, Tsunami in Japan to name a few, but COVID19 have had the biggest and widespread impact in the modern times. Even though supply chain resilience being a term coined in early 2000’s, its usage and importance has increased since then. With the curiosity of assessing the current state of sup-ply chain resilience literature and finding a resilience measurement method which is a one-fit for all supply chains in the manufacturing industry of Vorarlberg, the following research project was undertaken. Research is carried out with mixed methods, using a systematic literature review followed by expert interviews. In the conclusion of the research the author argues that there is a significant difference in the understanding of the term resilience within industry, there is a lack on the need for a meas-ure for resilience. The ways in which the structure of an organization impacts the level of resilience, foreseen benefits of digitalization and technologies for resilience are also dis-cussed. A comparative analysis on the SCR measurement methods discovered in literature, resulted in recommending Resilience index for on-time delivery proposed by Carvalho et al for the mentioned industry.
Stress testing is part of today’s bank risk management and often required by the governing regulatory authority. Performing such a stress test with stress scenarios derived from a distribution, instead of pre-defined expert scenarios, results in a systematic approach in which new severe scenarios can be discovered. The required scenario distribution is obtained from historical time series via a Vector-Autoregressive time series model. The worst-case search, i.e. finding the scenario yielding the most severe situation for the bank, can be stated as an optimization problem. The problem itself is a constrained optimization problem in a high-dimensional search space. The constraints are the box constraints on the scenario variables and the plausibility of a scenario.
The latter is expressed by an elliptic constraint. As the evaluation of the stress scenarios is performed with a simulation tool, the optimization problem can be seen as black-box optimization problem. Evolution Strategy, a well-known optimizer for black-box problems, is applied here. The necessary adaptations to the algorithm are explained and a set of different algorithm design choices are investigated. It is shown that a simple box constraint handling method, i.e. setting variables which violate a box constraint to the respective boundary of the feasible domain, in combination with a repair of implausible scenarios provides good results.