Journal Design Engineering Masthead
African Structural Engineering | 11 April 2016

A Difference-in-Differences Model for the Reliability Assessment of Industrial Machinery Fleets in Nigeria

I, b, r, a, h, i, m, S, u, l, e, i, m, a, n, ,, C, h, i, a, m, a, k, a, N, w, o, s, u, ,, A, d, e, b, a, y, o, O, k, a, f, o, r
Causal InferenceMaintenance OptimisationEconometric ModellingIndustrial Assets
Applies a quasi-experimental econometric model to isolate causal effects in reliability engineering.
Quantifies a significant 7.3 pp uptime increase from preventive maintenance, robust to controls.
Demonstrates the DiD framework's utility in settings with limited longitudinal data.
Provides a methodological blueprint for rigorous assessment of maintenance strategies.

Abstract

{ "background": "The reliability of industrial machinery fleets is a critical determinant of productivity and economic output. In many industrialising nations, systematic assessment of fleet-wide reliability is hindered by a lack of longitudinal data and the challenge of isolating the effect of maintenance interventions from concurrent operational changes.", "purpose and objectives": "This paper develops and applies a quasi-experimental econometric model to quantify the causal impact of a structured preventive maintenance programme on the operational reliability of heavy machinery fleets within the country's industrial sector.", "methodology": "A difference-in-differences (DiD) model is specified as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the monthly uptime percentage for unit $i$ in period $t$. The parallel trends assumption is tested, and robust standard errors are clustered at the fleet level to account for serial correlation.", "findings": "The implementation of the preventive maintenance programme was associated with a statistically significant increase in mean fleet uptime. The DiD estimator, $\\delta$, was 7.3 percentage points (95% CI: 5.1, 9.5). This effect was robust to the inclusion of control variables for machine age and utilisation hours.", "conclusion": "The DiD framework provides a rigorous methodological approach for reliability assessment in settings with limited pre-intervention data, successfully isolating the causal effect of the maintenance programme from underlying temporal trends.", "recommendations": "Industrial asset managers should adopt quasi-experimental evaluation designs to validate the efficacy of reliability-centred maintenance strategies. Further research should apply the model to different machinery types and sectors.", "key words": "reliability engineering, maintenance optimisation, causal inference, econometric modelling, industrial assets", "contribution statement": "This paper presents a