{ "background": "Process-control systems are critical for industrial and infrastructure projects, yet their long-term operational reliability in sub-Saharan Africa is poorly quantified. There is a notable lack of longitudinal, system-level performance data for informed maintenance and upgrade decisions.", "purpose and objectives": "This short report aims to methodologically evaluate the reliability of such systems using a novel panel-data framework. The objective is to estimate reliability trends and identify key determinants of system failure.", "methodology": "We constructed a unique panel dataset from maintenance logs of 47 discrete systems. Reliability was modelled as a function of operational age, environmental stress, and upgrade history using a generalised estimating equations approach: $\\logit(\\pi{it}) = \\beta0 + \\beta1 Age{it} + \\beta2 Stress{it} + \\beta3 Upgrade{it} + ui$, where $\\pi{it}$ is the probability of failure for system $i$ in period $t$, and $u_i$ captures unobserved heterogeneity. Inference is based on robust standard errors clustered at the system level.", "findings": "System age was the most significant predictor of failure, with each additional year of operation increasing the odds of failure by 18% (95% CI: 12% to 24%). Systems that had received at least one major upgrade showed a markedly different degradation profile.", "conclusion": "The panel-data estimation provides a robust methodological framework for quantifying reliability, revealing a clear positive relationship between operational age and failure probability in the studied context.", "recommendations": "Asset managers should prioritise data collection for panel analysis and schedule major upgrades before systems enter the high-failure probability phase identified by the model.", "key words": "reliability engineering, panel data, process control, maintenance, infrastructure, generalised estimating equations", "contribution statement": "This study provides the first application of a panel-data econometric model to assess the reliability of engineering systems in this context, generating a