Toward an Automatic Reduction of Non-added Values Tasks in the Project-Product-Process Domain

To increase their competitiveness, companies need to increase the turnover of their products. They should continuously develop attractive new products efficiently in order to increase sales volume and ensure sustainability in globalized markets. The streamlining of routine engineering processes becomes necessary to improve quality and productivity in design and allows more time to develop innovative products. The main goal of our contribution is to develop a design process framework to allow a reduction in routine design processes which will thus increase time available for innovative design processes. The methodology proposed consists of integrating data management, information management, knowledge management, and decision-support methods and tools, such as PDM (product data management), PLM (product lifecycle management), and KBE (Knowledge based engineering). In the area of knowledge management, some methodologies exist to reduce non-added value tasks such as KBE models, Top down design or Design for X (Design for manufacture, Design for assembly, dis-assembly, etc...). Our methodology deals with the reduction of routine design by automatic design project initiation, including knowledge management. This methodology is tested in a firm specialized in rolling shutter components. This approach allows time reduction of “non-added value tasks” and allows justifying any specification by knowledge and know-how gained in the past. When a new design project is created, often it will require one or several manufacturing processes to create the product. So, the designer has to use his own skills and experience to define the task lengths. One issue is how to keep the information usable by every designer, not only the expert. Another issue is how to preserve the knowledge embedded into the product, the manufacturing process and also in the project. Our contribution uses automatic project initiation, stored in a PLM tool. The automatic generation is based on several models which use configuration containing parameters and knowledge. By introducing a tool using this methodology in an industrial firm, we can reduce routine design and improve design robustness. Additional research will be performed towards an ergonomic use of the tool.