Adaptable product configuration system based on neural network

Abstract

In the rapidly diversifying and globalising market, product configuration is implemented in a dynamic environment with continuous change of configuration knowledge. The adaptability of the product configuration system, which is defined as the capability to adjust product configurator, human resources and organisational resources to fit a new environment, is becoming more and more crucial. To keep the adaptability, this research suggests an adaptable product configuration (APC) system which transforms the development of the configuration system in a dynamic environment from a straightforward process to a closed circle. In the existing research on product configuration, most issues are addressed separately by different approaches and most approaches lack a systematic view which considers the interaction among product configurator, resources and environment. The circle of APC is therefore divided into several isolated stages and involves intensive human work, consumes a lot of organisational resources and results in a long response time. To successfully implement APC, this research adopts an artificial neural network and a specific rule extraction mechanism to develop a product configuration system. The neural network is able to automatically acquire configuration knowledge from historical transaction data and then directly apply it without further knowledge programming. Rule extraction mechanism has the capability to interpret the behaviour of the trained neural network and make it comprehensible and adjustable. Finally, knowledge acquisition, representation and application in product configuration are incorporated into the same connectionist methodology. And consequently, the APC circle is accelerated and the adaptability of the product configuration system is improved. A case study of computer configuration is presented.

Keywords:

• product configuration
• knowledge acquisition
• neural network

Acknowledgements

This research is supported by the National Natural Science Foundation of China and Hong Kong Research Grants Council (No. 70418013, 70471022, and N_HKUST625/04). The authors would like to express their sincere thanks to Professor Shlomo Geva for his help with our research.