Search results

The purpose of this paper is to describe new methods to manage variation in complex manufacturing process chains and to show synergies between the variation risk management (VRM) and six‐sigma approaches.

The research methodology was experimental prototyping conducted in collaboration with industry partners. A prototype IT system was developed and tested to implement the approach. A quality cost‐based system was used to assess variation at each operation stage, for every product characteristic.

A comprehensive approach to the management of manufacturing variation is introduced, based on a new process risk matrix which can be used to specify an individual variation risk for every manufactured characteristic, throughout a manufacturing process chain. The approach has been implemented in a prototype software system and is aimed at the complex products such as those manufactured by the aerospace industry.

The IT approach described was developed during the research and is not commercially available.

Manufacturing industry should be able to use this approach, in particular the process risk matrix concept, to develop more effective management of product variation and resultant cost, in complex process chains.

The paper describes a novel approach to combine VRM and six‐sigma concepts, and introduces the process risk matrix as a structure to understand process variation.

Manufacturing companies must have a good knowledge of their products and processes to be competitive. This is increasingly important as products become more complex. There are many recent developments in product representation, but these do not currently address manufacturing process issues, or integrate with techniques such as key characteristics and Variation Risk Management. This paper describes the development of a database which models complex product specifications, down to component feature detail. It allows the representation of manufacturing operations, process chains and costs, and maps these models. Key product characteristics are flowed down to the detailed feature level, and the associated variability and capability assessed. The paper commences by outlining some of the recent significant developments in product representation. The database structure and associated applications are described. Finally, an example is provided for the use of the database, based on the representation of gas turbine engine from a major aerospace manufacturing company.