The automotive parts supply chain is characterised by expectations of high levels of parts availability, as vehicles are designed to be maintained throughout their life cycles. There is, however, a significant level of unpredictability in demand, requiring suppliers to store sufficient inventory to service demand associated with planned maintenance and unplanned repair events. In this thesis, a supply chain characterisation framework is proposed and confirmed with a series of case studies. The automotive supply chain is characterised as a Class III-P supply chain. This type of supply chain has products with high complexity and long life expectancies, which is augmented through the design of maintenance and repair schedules, requiring a supporting parts distribution supply chain. Automotive part supply continues for 15 years after production of a model ceases, requiring a wide array of items to be available for a significant period of time after the end of vehicle production. The need for parts availability for such a long period results in space constraints within the supply chain. Just-In-Time (JIT) manufacturing results in lean supply chains, but it is shown that the cost for post vehicle production can be high as the volumes required can decrease significantly. To implement JIT in the automotive parts supply chain a MAX/MAX inventory strategy is most commonly followed. The MAX/MAX inventory strategy is implemented with the Maximum Inventory Position (MIP) inventory management method. Deriving the method theoretically and comparing it with the practical implementation shows clear concerns regarding the dimensional consistency of the practical implementation. Using a System Dynamics Simulation Model (SDSM), it is shown that while the theoretical version of the method (MIPTheory) may minimise inventory, it does not maximise parts availability, as measured by allocation fill rate (AFR). The actual implementation (MIPActual) improves the AFR, but increases average inventory levels significantly (as much as 100 times in some cases). While it is accepted that stock-on-hand inventory management policies are inherently unstable, a stock-on-hand policy, Stock Target Setting (STS) was developed and redesigned to be stable. The SDSM showed that the STS method could result in stable behaviour, using the supply chain lead time as a damping factor. Comparison between the three methods in a theoretical set of demand, demand variance, lead time and lead time variance scenarios showed that the STS method improves the AFR above that of MIPTheory and requires significantly less inventory than the MIPActual method. Analysis of the STS method indicates there are some areas for improving the stock target equation, but this has to be performed with sufficient care. Extending the SDSM to use vehicle sales to generate service parts demand, it is possible to evaluate the inventory management methods under non-stationary demand conditions. The STS method is shown to be the preferred method for domestic supplied parts when there is no start-up inventory. For imported parts, the STS method performs better in the long term. The MIPActual method also results in high levels of parts availability. The MIPActual method, however, requires significantly more inventory. In the case of start-up inventory, the STS method is less effective in the short term, but in the long term requires less inventory to maintain an AFR of 100. A practical analysis using actual data show that there are cases where the STS method outperforms the MIP methods, but this is dependent on the demand and lead time behaviour. The study clearly shows that stock-on-hand inventory management policies, such as the STS method developed in this study, have the potential to improve the performance of the automotive parts supply chain. With the STS method, inventory levels can be reduced, reducing the pressure on storage space requirements resulting from the MIPActual results. At the same time, the AFR levels can be maintained. The practical problem in the automotive parts supply chain has clearly been addressed and solved. Significant achievements in the study include the development of a practical supply chain characterisation framework that provides guidance on the supply chain design for specific product classes. The SDSM is a powerful generic tool that can be adjusted for alternative inventory management methods. It can be expanded to evaluate any alternative inventory management method. The STS method showed that the assumption that stock-on-hand inventory management methods are inherently stable is incorrect, opening up the potential to initiate a new research direction towards effective stock-on-hand inventory management methods. The STS method was shown to be a viable alternative for the automotive service parts supply chain.