AI-Driven Predictive Compliance Auditing for Manufacturing
Keywords:
Predictive compliance,Compliance auditing automation,Manufacturing compliance monitoring,Machine learning for compliance,Digital twin manufacturing,Real-time audit analytics,Anomaly detection in production,Risk-based compliance management,Quality assurance intelligence,Regulatory compliance forecasting,Smart factory governance,Process deviation detection,Industrial IoT (IIoT) data integration,Model-based compliance validation,Explainable AI (XAI) for audits.Abstract
Compliance auditing aims to assess adherence to regulations or standards, which is critical for managing operational risk. Regulatory drivers, including public expectation of sustainable practices in manufacturing, create urgency to introduce integrated compliance auditing into the industry. Although innovation in compliance auditing could significantly reduce the operational risk and increase the efficiency of businesses involved, the approach has received little attention in the literature. The current study investigates how predictive models of compliance outcomes can support auditing tasks and integrate into a digital-twin framework to provide forward-looking assurance. Research questions explore how predictive models can be developed on a specific data architecture to support compliance auditing and how a digital-twin model can provide the data needed to implement these predictive models.
The study applies a mixture of methodologies: prediction models capture compliance outcomes for a specific manufacturing plant, while the auditing process integrates machine-learning predictions into a digital-twin framework. Predictive models have been developed to support the specific industry context and inform compliance auditing. These models address both compliance auditing components of risk and capability. Risk-scoring models indicate violations of risk thresholds identified by audit procedures, and capability-scoring models indicate process capability for the assessed compliance aspect.
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