Production planning and evaluation can be performed using various tools. This study encompasses two main topics: productivity measurement and production simulation. The study was conducted at AstraZeneca, a pharmaceutical company, where their production of inhalers was examined. One challenge mentioned both in the literature and by the project client is how productivity should be measured in a production setting, as well as how to enable comparisons between production lines with different conditions and required resources. Specific requests from the project client also included the need for the proposed productivity measure to complement the currently used measure, OEE (Overall Equipment Efficiency). The aim of the study was thus to define such a productivity measure with generic parameters. Several potential productivity measures were investigated through literature review and presented to the project client, including OPE, OFE, TEEP, Conversion Factor, ATPA, and ATPAT. Additionally, seven interviews were conducted with relevant individuals at AstraZeneca to enhance the understanding of the production process, identify factors that made OEE insufficient as a standalone productivity measure, and determine desired parameters to include in a complementary productivity measure. Together with the project client, it was decided that the ATPAT productivity measure was the most suitable for the situation. ATPAT was verified through calculations, which indicated that the integrated production was estimated to be 35,7% more productive compared to the separated production. This finding was presented to and confirmed as plausible by the project client.   Simulation of the inhaler production at AstraZeneca involved data collection, development of a simulation model, and experimentation with different scenarios in the model. Specific model objectives were designed based on one of the study's research questions. These objectives aimed to enable investigation of capacity changes in terms of rate changes and shift changes in production, as well as the impact of these changes on system throughput. Furthermore, the objectives included examining the impact of planning decisions on system throughput. The model is not intended for decision support but rather to provide initial insights into various scenarios for production planning.   The simulation model was developed and evaluated in accordance with methodology theory. Furthermore, the model was validated and verified to assess its reliability and how well it represented the real system. Validation was performed using sensitivity analysis, where two parameters, failures, and rejection rate, were examined. The validation of the simulation model revealed that the rejection rate was not a sensitive parameter, while failures contributed to the model being less reliable. Experiments were conducted with several different capacity change scenarios. The parameters that were varied during the experiments were production rate, number of shifts for each production line, and the decision point determining which order is manufactured on which type of production line. The results showed that the packaging lines were significantly less affected by capacity changes compared to bulking lines and integrated production lines. Furthermore, the results from the experiments demonstrated that an increased number of shifts per day had the greatest impact on system throughput for the integrated production lines compared to the separated production lines. Finally, the results showed that the threshold value for the number of products in each order, currently used to decide the production line, was not optimal. The system throughput was maximized when the threshold value was reduced by 15 %. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>