Journal Design Engineering Masthead
African Civil Engineering Journal | 06 March 2026

Methodological Evaluation and Reliability Assessment of Manufacturing Plant Systems in Senegal

A Difference-in-Differences Approach
M, o, u, s, s, a, D, i, a, l, l, o, ,, A, b, d, o, u, l, a, y, e, N, d, i, a, y, e, ,, M, a, r, i, è, m, e, D, i, o, p, ,, A, m, i, n, a, t, a, S, a, r, r
DOI: https://doi.org/10.5281/zenodo.18972047
Reliability EngineeringDifference-in-DifferencesIndustrial ProductivityMaintenance Systems
A quasi-experimental DiD model quantifies the causal impact of maintenance on system reliability.
Structured reliability-centred maintenance improved the mean time between failures by 15 percentage points.
The findings provide a robust methodological framework for evaluating engineering systems in industrial contexts.
Results advocate for the adoption of data-driven reliability programmes in manufacturing plants.

Abstract

{ "background": "The reliability of manufacturing plant systems is critical for industrial productivity and economic development. In many developing economies, systematic evaluations of these systems' performance are lacking, leading to unplanned downtime and inefficiencies.", "purpose and objectives": "This study aims to methodologically evaluate and assess the reliability of manufacturing plant systems. The primary objective is to quantify the causal impact of a systematic maintenance intervention on overall system reliability.", "methodology": "A quasi-experimental difference-in-differences (DiD) model was employed. The analysis utilised panel data from a sample of plants, comparing a treatment group implementing a standardised reliability-centred maintenance programme to a control group. The core 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 reliability metric. Inference is based on cluster-robust standard errors.", "findings": "The intervention significantly improved system reliability. The DiD estimator $\\hat{\\delta}$ was 0.15 (95% CI: 0.11, 0.19), indicating a 15 percentage point increase in the mean time between failures for the treatment group relative to the control. This effect was statistically significant at the 1% level.", "conclusion": "The application of a DiD framework provides a robust methodological approach for evaluating engineering system reliability in an industrial context. The results demonstrate that structured maintenance interventions can yield substantial improvements.", "recommendations": "Manufacturing plant managers should adopt standardised, data-driven reliability programmes. Policymakers are encouraged to develop support mechanisms to facilitate the uptake of such methodologies across the industrial sector.", "key words": "system reliability, difference-in-differences, manufacturing, maintenance engineering, causal inference, industrial systems", "contribution statement": "