{ "background": "Industrial process-control systems are critical infrastructure for South Africa's manufacturing and resource sectors, yet their reliability is often compromised by ageing assets, inconsistent maintenance regimes, and a lack of robust, data-driven assessment frameworks. Current policy and governance structures lack a formal probabilistic methodology to quantify system failure risks and inform maintenance investment. ", "purpose and objectives": "This policy analysis article develops and demonstrates a novel Bayesian hierarchical model to assess the reliability of such systems. Its objective is to provide a methodological foundation for evidence-based maintenance policy and governance, enabling the prioritisation of interventions and resource allocation. ", "methodology": "A Bayesian hierarchical modelling approach is employed, integrating failure data from multiple, heterogeneous subsystems within a plant. The core reliability for a subsystem i is modelled as \ᵢ \ (\, \), with hyperpriors on \ and \ pooling information across units, thereby improving inference for data-sparse systems. Policy implications are derived through posterior predictive checks of system availability under different maintenance scenarios. ", "findings": "The model application reveals substantial heterogeneity in subsystem reliability, with posterior estimates for mean time between failures (MTBF) varying by over 300% across a typical plant. Crucially, the analysis identifies that directing maintenance resources towards just two specific high-criticality, low-reliability subsystems could reduce overall plant unplanned downtime by an estimated 22%, a finding with significant policy relevance for constrained budgets. ", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous and operationally actionable framework for reliability assessment. It moves policy beyond calendar-based maintenance schedules towards a risk-informed, data-driven governance strategy. ", "recommendations": "It is recommended that industry regulators incorporate probabilistic reliability assessments into compliance reporting. Furthermore, state-owned enterprises and major industrial operators should adopt hierarchical modelling for capital renewal planning and to justify maintenance budgets with quantified risk reduction. ", "key words": "Bayesian inference, reliability engineering, maintenance policy, industrial automation, risk governance, infrastructure
Nkosi et al. (Sat,) studied this question.
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