A BOM model transformation method for hierarchical production planning management process of complex products

The Bill of Materials (BOM) is the core data in the hierarchical production planning management of complex products. In the current competitive market environment and increasing demand for individual customization, complex product manufacturing enterprises are exploring methods that fully use common data and efficiently organize the BOM according to order requirements. However, due to the lack of a unified data modeling method for hierarchical production planning, BOM data at different levels cannot be interpenetrated. Moreover, the single-tree BOM lacks flexibility in describing the dynamic change process of data in different planning management stages (i.e., order receiving, variant design, and manufacturing execution).

This paper proposes a BOM model transformation method for hierarchical production planning management to efficiently and accurately organize the data in different planning management stages of complex products. First, the Hierarchical Bill of Materials (HBOMs) model is constructed to integrate BOM data of different levels of planning. Subsequently, to describe the data requirements in HBOMs for different plan management stages, a composite HBOMs containing three types was designed. The Generic-HBOM (G-HBOM) aims to manage the common data of the same product type. The Instance-HBOM before-manufacturing (I-HBOM (BM)) and Instance-HBOM after-manufacturing (I-HBOM (AM)) manage the personality data of single product variant design and manufacturing execution stages, respectively. The transformation process models of HBOMs are constructed by analyzing the transformation logic between three types of HBOMs. To support the transformation process of HBOMs, five transformation operators are summarized, and the node traversal algorithm logic is proposed. Finally, a steam turbine generator product is taken as an example to verify the feasibility and effectiveness of this method. This method can effectively improve the efficiency of BOM construction and reduce data redundancy while helping to ensure the integrity, consistency, and accuracy of BOM data.