Customer buying behaviour analysis in mass customization

Motivated by the importance of customer buying behaviour (such as correlation among product attributes/features of products configured in the past) in planning future configurations, this paper addresses the issue that product evolution (upgrades) usually render information gathered from past buying behaviour at least partially unusable. For instance, relations among features might have been changed, thus making it difficult to configure the same products again. The proposed approach aims to (1) find associations between product attributes based on the analysis of prior customer orders (2) apply configuration rules to prune attribute association rules which are not controlled by customers, and (3) check whether derived attribute association rules from past orders also work for the new upgraded product. Attribute associations consistent with the upgraded product are then used to predict configurations for production planning. We use machine learning algorithms and optimization techniques to address these issues.