Quantification of risk in outsourcing contracts in manufacturing industries

Today's supply chain companies operate in a very competitive, global and rapidly changing market place, where risk assessment in the operation of their supply chains is increasingly seen as important. To cope with this emerging supply chain environment, many companies are adopting outsourcing as a core strategy. Increasing uncertainties in supply chain environments combined with lack of risk assessment when entering contracts can bring serious nancial risks to companies. Therefore, it is vital for companies to consider risk in the contracting process. This thesis researches the development of effi cient risk quanti cation methods to assist managers of supply chains in risk assessment, especially in the contracting phase of outsourcing. The focus of the study is on everyday risk. A literature review is conducted in the area of supply chain risk, supply contracts and the use of simulation. Additionally, risk measures used in nance are researched and assessed for use within the domain of supply chains. Following that, a detailed case study from the literature is selected to demonstrate the importance of risk in supply chain contracting. Using a detailed discrete event simulation model of this case study, the nancial risk measures Value at Risk (VaR) and Conditional Value at Risk (CVaR) are evaluated against measures commonly used in practice. To calculate VaR and CVaR the contemporary method, order statistics, is used. This method was found to be not effi cient enough to use in practice. Therefore, alternative statistical methods, parametric statistical distributions and bootstrap methods, were evaluated. Using the most e fficient and accurate method, the bootstrap method, a quantitative analysis of risk mitigation strategies for inventory risk was carried out. In this quantitative study experiments were carried out on quantity exibility and risk sharing contracts, supplier lead times and nished good and raw material inventory levels. The bene ts of these strategies were measured by comparing the estimated pro t and risk values against a baseline study where no risk mitigation strategy was used by companies. The ndings of this thesis include (i) using quanti ed risk measures has advantages over commonly used supply chain measures used in practice, such as, expected pro t, mean-variance, unit cost, on time delivery; (ii) nonparametric bootstrap methods is an e cient method to calculate VaR and CVaR using simulation to quantify risk in supply chains and (iii) the quantitative study on risk mitigation strategies using explicit risk measures did not always give a reduction in risk to individual partners in the supply chain and the supply chain overall