Supply chain risk management (SCRM) is a topic that gains more and more interest from both the academic and practitioner’s perspective. The reason for this is the increased complexity in the global supply chain (SC) networks and many managers do not realize the risks they build in their SC by the continuous search to cut cost and decrease tied up capital. One problem with SCRM is that it is hard to measure the performance of it and if it is really beneficial to work with it. The objective for this master thesis is to investigate how companies can evaluate and thereby improve their SCRM efforts by connecting the field of SCRM to the field of performance measurement systems (PMS). First, a thorough literature search was conducted where the current literature about SCRM and PMS was examined to understand what the literature recommends. This was followed by a multiple case study including semi-structured interviews with SC managers at eight companies to get the practical aspect of the problem.The results of the research show that companies work with SCRM in many different ways. The companies that have advanced furthest are the ones that have connected their SCRM to existing key performance indicators (KPIs) and because of that they have been able to measure the results of their SCRM efforts. The top-performers had a comprehensive understanding of their risk drivers and risks that affected their SC, which was consistent with the literature. Connecting the SCRM to the PMS, the companies can better monitor how the SCRM affect the performance goals for the SC performance. Then the next step is then to connect key risk indicators (KRIs) to the key KPIs that will give managers longer time to react to potential risks. Only one company in the study had accomplished this, hence, there is a great space for improvements for many companies.

The thesis focuses on internal management and control systems in connection with operational risk management (ORM) process. The Basel II concept is outlined from the operational risk point of view, incl. methods for capital requirement for operational risk. Consequently, essential regulatory requirements and bank standards for effective management and control systems are specified. ORM tools that are potentially able to capture business environment and internal controls factors (to be regarded within AMA models) are disserted, specially concentrating on key risk indicators. Construction of this ORM tool is designed on a theoretical basis and also on an example related to payment systems. There is an evaluation of advantages, challenges and possible ways to use this method.

In this thesis a regression analysis of ten independent data sets is analysed in order toestimate losses and Key Risk Indicators (KRI). Each data set contains a list of objects,impacts that each object contains and revenue stream values (RSV) to each impact.The project investigates the data and simulate yearly losses as response variables in theregression modelling. The three regressors that influence the yearly losses are numberof objects, sum of revenue streams and expected aggregated losses. Given the responsevariable from each data set a percentage scale of KRI’s is determined indicating howlarge losses each set possess. / I denna avhandling analyseras en regressionsmodellering av tio oberoende mängderdata för att uppskatta förluster och Key Risk Indicators. Den givna dataupsättningeninnehåller en lista med objekt, påverkan varje objekt erhåller och vad respektiveobjekt omsätter. Projektet undersöker den givma datan och simulerar årliga förlustersom svarsvariabler i regressionsmodelleringen. De tre regressorerna som påverkarde årliga förlusterna är antalet objekt, summan av intäckterna och förvämtadesammanlagda förlusterna. Från den givna svarsvariabeln från varje datamängdbestäms en procentuell skala av KRIer som indikerar hur stora förluster varjeuppsättning har.