Production Line Systems (PLS) are ubiquitous in today's manufacturing industry. The need for enhanced efficiencies and higher throughput in such systems has increased their complexity and size that has made performance analysis challenging for practitioners. This thesis introduces a novel approach based on Product Service System (PSS) design and analysis which simplifies Production Line System study and identifies opportunities for performance improvement that can be quantified based on the hardware and maintenance system performance. The approach involves modelling and simulation techniques based on reliability engineering principles and systems thinking. In order to apply the principles of PSS approach to PLS, it is essential to draw a comparison between PLS and PSS, so as to take account of the differences while applying the new approach; a literature review has been carried out on PSS Design and Analysis, that identified the state of the art modelling and simulation techniques in PSS. Additionally, a separate literature review on maintenance system and production line was carried out. This enabled the PSS methodology to be applied to PLS by incorporating the differences. A systems thinking approach has been employed to create the static simulation model of the integrated production line system by means of schematic representations. Key improvement areas, identified from the static simulation model have been modelled dynamically to incorporate the stochastic behaviour of the system. All the dynamic models are developed using a Discrete Event Simulation platform. These models were supported by Monte Carlo Simulation, queuing principles, probabilistical and statistical methods pertinent to reliability engineering. The novel integrated simulation model consists of a production line model and a maintenance system model. The production line model simulates two types of failures in addition to the outages in the system: breakdowns and short stops. The maintenance system model simulates the maintenance actions in the production line by considering the resources availability, repair time, and resources travelling time amongst others. In addition, the maintenance model is capable of optimizing the preventive maintenance interval for maintainable failures for cost, availability and criticality while taking into account the maintenance effectiveness value from the failure data. The simulation model is validated using an industrial case study which consists of a large production line for beer. Sensitivity studies on the simulation model enabled the case study company to focus on strategies for throughput improvement by improving the reliability and maintainability, optimal resources allocation and maintenance interval optimization in targeted areas in the large and complex system. The model developed is generic and can easily be applied to analyse other industrial production line systems. It can also be used as a design tool for new production lines.