Abstract
In the dynamic landscape of the automotive industry, Original Equipment Manufacturers (OEMs) are confronting the challenges of manufacturing planning in the face of demand for customized vehicles, competitive pressure, shorter production cycles, and complex product designs. This paper introduces a novel methodology that leverages a multi-criteria decision support system, incorporating data from past vehicle projects to support the planning phase of new projects. Utilizing a Multi-Attribute Decision Making (MADM) method, this approach evaluates systems in manual vehicle assembly processes based on criteria such as value creation, ergonomics, and rework in the environment of the vehicle manufacturer’s product portfolio. The paper discusses the significance of product knowledge and data-driven insights in making transparent, justifiable decisions and outlines the use of existing process and workplace design data as a decision-making foundation. A detailed examination of manual assembly process planning categorizes procedures based on component type, emphasizing the dependency on existing work plans and the utility of process modeling languages and ergonomic risk assessment data. By applying a multi-criteria evaluation per system, the paper offers a novel perspective on utilizing historical and existing project data to support early-phase planning decisions, thereby elevating these decisions to a data-based level. This research was applied to a case study at a German automotive OEM, identifying best-practice concepts for manual assembly process planning. The results, visualized through a heatmap, facilitate comparisons across vehicle models at specific system levels, providing planners with actionable insights into optimizing productivity in manual assembly processes. The findings underscore the potential of this methodology to guide planners in selecting or avoiding specific product or process concepts based on productivity considerations, thus contributing to more efficient and ergonomic manual assembly process planning in the automotive industry.
Published Version
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