Deviation occurrence analysis in a human intensive production environment by using MES data

Sammanfattning: Despite decades of automation initiatives, manual assembly still represents one of the most cost-effective approaches in scenarios with high product variety and complex geometry. It represents 50% of total production time and 20% of total production cost. Understanding human performance and its impact in the assembly line is key in order to improve the overall performance of an assembly line. Along this thesis work, by studying the deviations occurring in the line, it is aimed to understand how human workers are affected by certain functioning aspects of the assembly line. To do so, three different influence factors have been chosen, and then observed its impact in human performance: i. How past events occurring in the line affect the current action of the worker. ii. How do scheduled stops affect the current action of the worker. iii. How does theoretical cycle time affect the performance of the worker. In order to observe these influence relationships, it has been used data gathered in the shop floor from SCANIA's Manufacturing Execution System (MES). By applying methods of Knowledge Discovery in Database (KDD) data has been indexed and the analyzed providing the necessary results for the study. Finally, from the results shown, it can be inferred that variability on the functioning of the line does have an impact on human performance overall. However, due the complexity of the manufacturing system, impact in human performance might not be as regular as initially thought.