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The automotive industry is characterized by the continuous and frequent adoption of cutting edge technologies in manufacturing. The introduction of new technology often entails a system level investigation to determine whether it meets expected production requirements. Many system design alternatives are usually considered at this stage, and a fast performance evaluation tool is desired to quickly obtain the optimum system configuration. In this paper, we introduce an analytical tool that was developed for this specific purpose. The investigated manufacturing system is a multi-product, automated, assembly production line composed of unreliable machines decoupled by finite buffers. Each product is an assembly of two components that are produced in sub-assembly lines before being assembled at an assembly machine. The performance of this system needs to be evaluated for different configurations that arise mainly due to the integration of a remote laser welding technology. This new technology promises, among other advantages, a much faster assembly than existing technologies. This makes it feasible to share assembly among many products with different possible production policies. The processing machines including the assembly machine are liable to fail randomly in operation thereby necessitating an evaluation method capable of accounting for stochastic behavior.To solve this problem, we develop a decomposition-based approximate analytical method. We use the continuous material flow approximation, which allows to model machines having deterministic but inhomogeneous processing times as in the investigated real system. We also account for multiple machine failure modes and introduce new techniques to incorporate the assembly of multiple products with different policies of switching between products. Comparison with simulation results shows the good accuracy of the model in estimating system throughput and work-in-progress. The model has been successfully utilized to evaluate the performance of an automotive door assembly manufacturing system and the main results of this case study are reported.

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Solid resilient tyres frequently impact on kerbs or obstacles when they are operated in the construction and transportation sectors. These sudden impacts can generate high stresses and thereby damage the tyre. The factors which cause such failures are not easy to capture experimentally due to their complexity and high experimental cost. Hence, this study focused on using the finite element method to model the solid resilient tyre, generate stresses, and identify the failures and regions that are susceptible to damage. Initially, a tyre static model was developed and validated using laboratory experimental data obtained from the industry. The validation results showed that the numerical results are in good agreement with the experimental data. Subsequently, the model was extended to incorporate impact simulation. The simulation considered tyre motion and impact on three different types of kerbs -angular, circular and square-shaped. Simulation results showed that high stresses occur mainly in the side walls of the solid tyre while high contact pressure and high in-plane shear stresses were observed at the tread layer especially when it moves on square-type kerbs. Hence, there is a high tendency for tyre failures to occur due to the side wall cracks at the base layer. Furthermore, sudden wear and damage due to chunking can be expected on the tread layer of the solid resilient tyre.

The DYNWIP control policy is extended for the control of reentrant manufacturing systems. Based on the principles of inventory management, the DYNWIP policy utilizes real-time system information to match production with demand whilst effectively controlling inventory levels. The DYNWIP policy is generally formulated to be applied at all machines of a production line, but this increases the complexity and cost of implementation. Simulation studies show that the application of the policy at a few key machines of a reentrant system is sufficient to generate satisfactory performance. The studies also demonstrate that this new policy performs better than the established CONWIP [11] and WIPLOAD [2] control policies by providing higher service levels with lower work in process (WIP).