A simulation model to optimise the inventory parameters of Paul Mueller with uncertain supplier lead times and demand variability

The thesis optimizes the inventory management system of Paul Mueller Company to address issues of demand variability and uncertain supplier lead times. The goal of the research is to improve the company's On-Time-In-Full (OTIF) performance, which is below the target of 92.5%. A simulation model is developed to compare three inventory configurations: the current model, one with deterministic lead times, and another with stochastic lead times. The study highlights that implementing stochastic lead times significantly improves service levels, fill rates, and reduces backorders, although it comes with higher holding costs. The deterministic model offers lower overall costs but doesn’t perform as well in terms of service levels and fill rate. We recommend the implementation of a stochastic inventory model to better align inventory with actual demand and improve OTIF performance.