Research On Product Configuration Based On Artificial Intelligent Techniques And Activity-based Costing

Product configuration is a special design process of selecting and arranging combinations of predefined parts to satisfy given specifications. Although product configuration has been recognized as a key enabler for individualized order realization in mass customization and related research has established a sound theoretical foundation, the practices of product configuration in industries still face great challenges. To leverage the difficulties in configuration implementation, in the 21th century, several research literatures concentrated on the survey and analysis of management problems of product configuration and the research focus of product configuration gradually moved from technical issues to management issues. Based on the foundation of existing research, this dissertation selects four critical management problems of product configuration as research issues. The main contents include:Although companies are able to deliver customized products, most of them neglect the personalization in configuration activities. The lack of personalization probably results in the perceived risk of custom confusion and hinders the effectiveness of product configuration. To leverage the problem, this research introduces customers'personal characteristics into the modeling of configuration rules. A methodology which combines Local Cluster Neural Network (LCNN) and RULEX algorithm is proposed to efficiently acquire and apply personalized configuration rules by data learning and network explaining.In mass customization, product configuration is probably carried out in dynamic environment. However, existing configuration research mostly neglects the dynamics of configuration implementation. Therefore, the crucial issue about how to efficiently implement and update configuration knowledge to fit for dynamic environment is still unaddressed. To solve this problem, a framework combining LCNN and RULEX is developed to coordinate the implementation and update of configuration rules. In the framework, rule acquisition, representation, application and evolution are incorporated into the same intelligent methodology, by which the transfers between different rule formulations are accelerated.In current research, product configuration is gradually considered as bidirectional design interactions. In interactive configuration, maintaining the configuration assignment satisfies all configuration constraints (termed as consistency restoration, CR) is one of the primary issues which should be addressed. However, because of the uncertainty and flexibility of customer needs, existing CSP-based CR approaches mostly face the challenge from computational complexity. Besides, most approaches neglect the impact of customer preferences on CR. To address the problems, this research proposes a responsive CR technique based on content-addressable memory (CAM). The CSP-based CR problem is compiled into a CAM recalling process. Then Hopfield network is adopted to automatically correct the inconsistent assignment. Meanwhile, to introduce customer preference into the CAM model, specific orientation mechanisms are developed.Product costing is a process of estimating the cost of a product at design stage. The cost information helps companies to comprehensively evaluate how the configuration satisfies customers. However, traditional costing methods suffer the drawbacks of poor accuracy, low agility and undesirable degree of detail. To leverage the drawbacks, this research introduces activity-based costing (ABC) into product configuration. By developing generic and ABC- based product model, activity model and activity organization architecture, companies are able to provide detailed process-oriented cost information to support product configuration.