Quality Improvement Through Data Analysis – Qualification of Failure Management by Standardized Failure Recording in Manual Assembly

This paper aims to optimize data-based failure management in manual assembly through qualification for the application of data analytics. Nowadays, scientific approaches in data-based failure management focuses on automated and future-oriented data analysis. Data acquisition’s ability to create a required data structure and provide the necessary prerequisites for the application of data analytics is often neglected or assumed as a given. Due to a variety of influences in manual assembly, a structured acquisition of defect information is impaired. Consequently, the generated data structure and associated information content fluctuate enormously. This creates a high level of waste in companies’ knowledge and resources, which leads to competitive disadvantages in long-term action. Therefore, this paper analyzes existing requirements in terms of information relevance and data structure for relevant data analysis approaches in the context of failure management. Subsequently, an evaluation based on their requirements and manual assembly applicability is carried out. Hence, an advanced process model is developed to indicate necessary and optional data acquisition in manual assembly. Finally, the model is evaluated by using an example from a commercial vehicle manufacturer.