Randomised Field Trial of a Diagnostic Framework for Efficiency Gains in Senegal's Industrial Machinery Fleets

{ "background": "Industrial machinery fleets in developing economies often operate below optimal efficiency due to ad-hoc maintenance and diagnostic practices, leading to significant operational and financial losses. A systematic, data-driven framework for fault diagnosis and performance assessment is required.", "purpose and objectives": "This study aimed to evaluate a novel diagnostic framework for machinery fleets through a randomised field trial, quantifying its impact on operational efficiency metrics including downtime, fuel consumption, and repair costs.", "methodology": "A randomised controlled trial was conducted with 74 heavy machinery units from Senegalese industrial sites. Units were randomly assigned to a treatment group, using the new diagnostic framework, or a control group, using existing practices. Efficiency was measured over an operational period. The primary effect was estimated using a linear mixed model: Yij = \β0 + \β1 Ti + uj + \εij, where Yij is the efficiency score for unit i in site j, $Ti$ is the treatment indicator, $uj$ is a site random effect, and \εij is the error term.", "findings": "Machinery in the treatment group demonstrated a 17.3% improvement in mean operational efficiency score (95% CI: 12.1% to 22.5%) compared to the control group. This gain was primarily driven by a significant reduction in unplanned downtime and lower mean fuel consumption per output unit.", "conclusion": "The implemented diagnostic framework provides a statistically robust and practically significant method for enhancing the operational efficiency of industrial machinery fleets in this context.", "recommendations": "Fleet managers should adopt structured diagnostic protocols supported by systematic data collection. Further research should investigate the framework's scalability to other sectors and its long-term economic viability.", "key words": "diagnostic framework, operational efficiency, randomised controlled trial, industrial machinery, maintenance optimisation, field trial", "contribution statement": "This paper provides the first