SmartQC: An Extensible DLT-Based Framework for Trusted Data Workflows in Smart Manufacturing
Alan McGibney, Tharindu Ranathunga, Roman Pospisil
TL;DR
This paper addresses the challenge of securely integrating distributed ledger technology into smart manufacturing workflows to ensure data integrity and automate quality processes. It proposes SmartQC, an extensible DLT-overlay architecture with a four-layer design, an extensible context-driven data model, and cross-ledger interoperability via a gateway and translator contracts. The key contributions include a novel relational transaction model (User, Context, Data), conditional transaction creation, and demonstration in a Zero Defect Manufacturing use case with initial performance evaluation on BigchainDB and Hyperledger Fabric. The results indicate that SmartQC can provide tamper-evident data provenance and automated workflow capabilities with latency profiles compatible with typical quality control operations, enabling scalable, trusted data sharing among diverse manufacturing stakeholders.
Abstract
Recent developments in Distributed Ledger Technology (DLT), including Blockchain offer new opportunities in the manufacturing domain, by providing mechanisms to automate trust services (digital identity, trusted interactions, and auditable transactions) and when combined with other advanced digital technologies (e.g. machine learning) can provide a secure backbone for trusted data flows between independent entities. This paper presents an DLT-based architectural pattern and technology solution known as SmartQC that aims to provide an extensible and flexible approach to integrating DLT technology into existing workflows and processes. SmartQC offers an opportunity to make processes more time efficient, reliable, and robust by providing two key features i) data integrity through immutable ledgers and ii) automation of business workflows leveraging smart contracts. The paper will present the system architecture, extensible data model and the application of SmartQC in the context of example smart manufacturing applications.