Supply chain risk and uncertainty have been drawing much attention in the past few decades. In this thesis, a holistic risk management framework is developed to cope with such risks and uncertainty. The framework assists decision-makers first by detecting internal and/or external risks at early stages. In order to visualize the probability of risks and their impacts, risk-mapping techniques are then proposed. Risk assessment is used to determine risk occurrence and the effects in either quantitative or qualitative terms. Once risks are analyzed, risks relations should be investigated to further adopt risk management strategies for high importance items. Finally, ongoing control of existing and emerging risks requires an appropriate management strategy in order to increase supply chain efficiency. The framework is validated through an application in a small manufacturing company that struggles with outsourcing risk arising from both lead time and demand uncertainties.  We first detect uncertainties, then design a simulation model to illustrate the impact of these uncertainties on the company’s performance, where we use the number of lost customers as the company’s performance indicator. Moreover, we conduct an experimental design to investigate risk relation and their impact on the number of lost customers. The experimental design also allows for comparing various supplier’s performance and indicates which supplier would be most beneficial to work with.