A general cost model to assess the implementation of collaborative robots in an assembly line

In the realm of assembly processes, collaborative robots (cobots) are recognized as valuable assets for enhancing production performance, encompassing assembly time, product quality, and worker satisfaction. Nonetheless, there's a noticeable absence of models that effectively assess cobot integration and assist decision-makers in selecting the most economically efficient assembly setup. This study aims to bridge this gap by proposing an innovative cost evaluation model, facilitating a practical comparison among various assembly configurations to aid in choosing the most optimal one. The proposed model accounts for diverse cost dimensions such as manufacturing, setup, prospective, retrospective, product quality, and worker well-being. Furthermore, it incorporates the influence of learning effects on assembly time and quality, which is particularly pertinent in scenarios of low-volume and mass-customized productions. Alongside the model description, the study presents three real-world manufacturing case studies. Furthermore, this thesis conducts an in-depth examination of an industrial assembly line, scrutinizing its throughput across different production setups. Contrary to conventional wisdom, the analysis suggests that the conventional average production batch size might not always yield the most efficient balance between production costs and line throughput. The throughput analysis underscores the necessity of considering specific production attributes and assembly strategies to optimize operational efficiency and profitability