Supervising Quality Environments with an Autonomic Ledger (SQuEAL)

Quality manufacturing environments must meet stringent regulatory requirements and implement an overarching Quality Management System (QMS), governing Good Manufacturing Practice (GxP), which engages in a process of Continuous Improvement (CI). This normally involves internal and external audits of the various quality systems, or when issues arise, identifying quality gaps in said systems and processes, followed by the implementation of correction(s). The QMS comprises a system of systems and can become extremely complex, requiring a high level of human effort, with multiple concurrent schedules, including system lifecycle, documentation and review. QMS supervises areas where products are held and includes logistical concerns, computerised systems, instrumentation requiring regular calibration, internal and external audits, inter-company certification of customer and vendor facilities, regulatory certification of facilities and products, printing and so on. The addition of human effort to attempt correction of human effort inevitably introduces more complexity and potential for new errors, even as improvements are being addressed. The problem of systems becoming more complex and difficult to manage is one directly addressed by the paradigm of Autonomic Computing (AC). The problem being addressed here is how we may take an autonomic computing approach to detecting quality gaps and corroborating quality systems integrity. This paper will consider the potential role that AC can play in improving QMS by augmenting the substantial human effort and corroborating the sequence of events through the implementation of a secure time-series ledger.