Accurate acquisition for customer’s requirements is the base and key in product configuration design. However, original customer’s requirements must be decomposed in order to comprehend truly and apply them to guide product configuration design, because they are usually some fuzzy, general even contradictory customized demands. In the paper, two concepts are introduced for customer’s requirement decomposition. One is the requirement element, and the other is the granularity of requirement element. Moreover, the controlling principle for granularity of requirement element is given and the method of requirement decomposition is proposed. This method means semantic segmentation, semantic translation, supplement or subdivision of human-machine-environment and semantic combination. Customer’s abstract demands could be effectively decomposed into some specific requirement elements according to the proposed method of requirement decomposition as well as by controlling the granularity of requirement element reasonably. Finally, the customized design of a money-binding machine is taken as an example to validate the effectivity of proposed method.

Product agile customization design is an effective technological measure to win the customers and improve development efficiency. It needs designer to determine product structure quickly according to customer’s customized requirements. In this paper, a novel design method of product agile customization is presented by integrating rough set (RS) theory and artificial neural network (ANN) in the design process. In the method, design demands are reduced so as to form effective decision conditions by applying RS, and on that basis ANN models between design demands of different design stages and corresponding product structures are established so as to determine product structural styles quickly by applying ANN. Finally, this method is applied to the general schematic design process of a roll plate machine’s customization, and its validity is verified.

In this paper, a new SPC based quality control process model for steelmaking industry is established, in which a Customer Requirements Weighted-Principal Component Analysis (CRW-PCA) method is proposed, the multivariate control charts based on this method can make special emphasis on the controlling of steelmaking quality characters response to customer’s special requirements. Practices show that compared with the traditional PCA-based multivariate control chart, the multivariate control charts based on CRW-PCA is more adaptive to the needs of today’s process quality control of steelmaking due to the adequate consideration of customers’ requirements.

Raw material cost control is becoming an important aspect in the steel enterprise’s cost management, as the raw material cost occupies a large proportion in the cost of product. Therefore, the raw material cost control model which ensures the minimum raw material cost with the qualified contents of chemical ingredients in the product is established. The arithmetic based on Lagrange multipliers is proposed to solve the described model. The model verification provides great support for reducing the cost and configuring resources optimally.