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Estimating product cost at the design stage, often referred to as product costing, is vitally important in the product configuration process in the mass customization paradigm. There are three research streams in this field: analogous, statistical, and analytical methods. However, in a mass customization environment, these methods suffer the drawbacks of poor accuracy, low‐agility and an undesirable degree of detail. It is imperative to develop an efficient framework for product costing in mass customization. The paper aims to address these issues.

Activity‐based costing (ABC) becomes a new trend in product costing which alleviates the shortcomings discussed above. However, the complexity of ABC probably increases in mass customization with a high degree of variety. In this research, a framework is formalized for product costing in mass customization, in which a generic activity definition is proposed to simplify and catalyze ABC practice in high‐variety production. Moreover, an activity dictionary with a hierarchical structure is developed to bring commonality and modularity to the storage and retrieval of activity data.

A generic activity‐dictionary‐based method for product costing in mass customization is developed.

The method proposed for product costing provides valuable cost data for product specifications, as well as the activities to create them. Therefore, a product configuration solution can be financially evaluated and optimized.

Based on the methodology proposed, a prototype system has been developed and implemented in a computer‐assembling company.

This paper alleviates the negative impact of activity variety on product costing.

Configuration systems are used as a means for efficient design of customer tailored product service systems (PSS). In PSS configuration, mapping customer needs with optimal configuration of PSS components have become much more challenging, because more knowledge with personalization aspects has to be considered. However, the extant techniques are hard to be applied to acquire personalized configuration rules. The purpose of this paper is to extract the configuration rule knowledge in symbolism formulation from historical data.

Customer characteristics (CCs) are defined and introduced into the construction of configuration rules. Personalized PSS configuration rules (PCRs) are thereby proposed to collect and represent more knowledge. An approach combining Local Cluster Neural Network and Rulex algorithm is proposed to extract rule knowledge from historical data.

The personalized configuration rules with CCs are able to alleviate the burden of customers in expressing functional requirements. Furthermore, in the long-term relationship with a customer in PSS realization, PSS offerings can be reconfigured according to the changing CCs with the guide of PCRs.

The contribution of this paper lies in introducing the attribute of CCs into the antecedents of PCRs and proposing the neural networks-based approach to extracting the rule knowledge from historical data.