Big data sharing and high-efficiency traceability for blockchain-based supply chain management

Abstract

Although supply chain management is of remarkable market value and plays a vital role in the global economy, the underlying technologies are underdeveloped, especially from the computer science perspective. In particular, the data from various supply chain stakeholders are not interoperable, leading to high operation costs. Moreover, the traceability service is not provided in most modern supply chains, which brings severe concerns in terms of product quality. In this thesis, we propose employing the latest blockchain technology in supply chain management, namely blockchain-based supply chain management, which connects various supply chain stakeholders and provides traceability services. Blockchain provides distinctive features, such as immutability and authenticity, for supply chain management. In particular, the product information stored on blockchain cannot be tampered with once stored. Moreover, blockchain systems can authenticate product records without centralized authorities. We have developed novel methodologies of big data sharing and high-efficiency traceability for supply chain management. First, we present a survey about blockchain-based supply chain management and propose a system architecture. Supply chain management is fundamental for gaining financial, environmental, and social benefits in the supply chain industry. Although there are some proof-of-concept studies and surveys on blockchain-based supply chain management from logistics, the underlying technical challenges are not identified. We provide a comprehensive analysis of potential opportunities, new requirements, and principles of designing blockchain-based supply chain management systems. We summarize and discuss four crucial technical challenges in scalability, throughput, access control, data retrieval and review the promising solutions. Finally, a case study of designing a blockchain-based food traceability system is reported to provide more insights into tackling these technical challenges in practice. Second, we introduce a big data sharing solution for blockchain-based supply chain management. Nowadays is the big data era. A large amount of data are generated, which can be valuable for business, healthcare, transportation, etc. Researchers have been trying to design and develop data-sharing platforms to promote the dissemination of valuable data. However, the existing platforms fail to address at least one of the three issues: trustworthiness, data heterogeneity, and authenticability. To this end, we propose TSAR, a fully distributed Trustless data ShARing platform. In detail, we architect TSAR on Blockchain to remove the dependency on reliable third parties, which realizes the trustworthiness. Moreover, we propose a general data schema to represent raw data, which handles data heterogeneity. Finally, we record the data transaction as well as user-group information on blockchain to achieve authenticability. Finally, we proposed a high-efficiency traceability algorithm for blockchain-based supply chain management. Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity while the efficiency issue is inadequately studied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. For the first time, we studied the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, it is challenging to allocate the record searching primitives to the chunks with maximized parallelization ratio. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. We believe this thesis is a significant step towards enhancing the supply chain efficiencies, reducing the operation cost, and most importantly, storytelling the consumers about the provenance and journey of products.