Production rate optimization through discrete-event simulation: A case study at pharmaceutical industry in Iraq

Abstract

Using Samarra Drug Industry (SDI) as a case study, this research creates and implements a discrete-event simulation (DES) to find and fix production bottlenecks in Iraq's pharmaceutical manufacturing sector.  The goal is to reduce cycle time, regulate work-in-process (WIP), and increase sustainable throughput without sacrificing regulatory compliance.  By fitting distributions and doing goodness-of-fit tests, we can predict stochastic processing, setup, failure, and cleaning behaviors using detailed time-study observations and historical production records from critical phases such as granulation, tablet compression, coating, and packing.  A two-stage experimental technique is supported by a verified baseline model that is in accordance with seen key performance metrics (throughput, WIP, cycle time, resource utilization, and overall equipment effectiveness).  Prior to that, a screening design is used to find high-leverage elements in buffer capacity, setup time reductions, personnel configurations, and preventive maintenance schedules.  Also, under realistic shift schedules and demand unpredictability, simulation-optimization (genetic algorithm/Pareto search) investigates the trade-offs between throughput maximization, work-in-progress limitations, and lead-time goals.  The reliability is tested by conducting sensitivity studies to factors including cleaning intervals, product mix, and equipment downtime.  The results show that the production rate may be significantly increased without sacrificing quality control or regulatory standards. They also show how the timing of maintenance, setup efficiency, and buffer location interact critically.  By expanding DES practice through the integration of bottleneck analytics with optimization under industry-specific operational limitations, this article presents a realistic improvement roadmap for Iraq's Samarra Drug Industry.  For quick use, we offer managerial implications and a roadmap for gradual deployment.