"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: \ () = \0 + \1 Age{it + \2 Stressit + \3 Upgradeit + ui, where is the probability of failure for system i in period t, and uᵢ 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
Akello et al. (Tue,) studied this question.
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