A Granular-Computing-Based Data-Sharing Decision-Making Method for Enabling Blockchain-Based Order Tracking in Social Manufacturing

Under the social manufacturing context, geographically distributed and decentralized micro-and-small-scale manufacturing enterprises (MSMEs) self-organize into manufacturing communities (MCs), a type of decentralized autonomous organization (DAO). In MCs, MSMEs share their manufacturing resources for order-driven cross-enterprise production cooperation, which is supported through blockchain-based order tracking. However, the application of blockchain also brings concerns about data security and privacy protection to MSMEs, which leads to disputes between MSMEs about which data should be stored in the blockchain. For this problem, a granular-computing-based data-sharing decision-making (GrC-DSDM) method is proposed. In the GrC-DSDM method, a fuzzy proximity relationship is used to describe the familiarity between MSMEs in the same MC, and MC familiarity is obtained based on the granular space derived from the fuzzy proximity relation. A fuzzy preference relation is used to represent MSMEs' preferences for all metadata related to the order, and a group decision-making method is applied to calculate the preference values for all metadata. Through constructing the mapping relationship between MC familiarity and preference values of all metadata, we can determine which metadata should be shared with the MC for blockchain-based order tracking. The GrC-DSDM method can support group decision-making on data sharing among MSMEs in the same MC. The implementation of the GrC-DSDM method is demonstrated through the example of a sheet metal parts processing MC. It is expected that the GrC-DSDM method will provide a basis for enabling blockchain-based order tracking in MCs.