Blockchain-IIoT-big data aided process control and quality analytics

Abstract

The literature on quality management system (QMS) assumes that product and process performance data are authentic and easily accessible. This assumption, while ideologically sound, is questionable in practise because the authenticity and accessibility of data cannot be guaranteed in many circumstances. Inaccurate, incomplete, inconsistent, and inaccessible data are common in supply chains and prevent the QMS from achieving its goal: assuring product and process quality to meet customer requirements. This study is one of the first to examine the impact of data quality and data latency on process control and quality analysis which are elemental parts of daily QMS activities, from a supply chain visibility (SCV) perspective. In this study, five propositions are made to show the relationships between technology, SCV, and data issues. More importantly, the study proposes a platform that integrates Blockchain (BC) technology, Industrial Internet of Things (IIoT), and Big Data to solve data problems in SCV and QMS. We further perform fuzzy association rule mining (FARM) to show how the platform can solve quality analysis problems and complete a closed-loop process control cycle in manufacturing. We also explain the contributions of the integrated platform to QMS from four theoretical perspectives. Finally, we discuss the limitations of the platform and provide recommendations for future research.