{ "background": "Industrial machinery fleets in Nigeria face persistent challenges with unplanned downtime and suboptimal output, yet there is a paucity of rigorous, field-based evaluations of diagnostic interventions within the local operational context.", "purpose and objectives": "This study aimed to quantify the causal impact of a structured diagnostic protocol on the operational yield of industrial machinery fleets, evaluating its efficacy as a yield optimisation strategy.", "methodology": "A quasi-experimental, difference-in-differences design was employed, comparing a treatment group (n=12 fleets) implementing the diagnostic protocol against a matched control group (n=12). The primary yield metric was Overall Equipment Effectiveness (OEE). The treatment effect was estimated using a fixed-effects panel model: $Y{it} = \\beta0 + \\beta1 (\\text{Treatment}i \\times \\text{Post}t) + \\alphai + \\deltat + \\epsilon{it}$, with inference based on cluster-robust standard errors.", "findings": "Implementation of the diagnostic protocol significantly increased aggregate OEE by 7.3 percentage points (95% CI: 4.1, 10.5; p<0.01). The improvement was driven predominantly by enhanced availability, with a noted reduction in mean time to repair.", "conclusion": "The structured diagnostic intervention proved to be a statistically significant and operationally meaningful driver of yield improvement, demonstrating the value of systematic fleet health monitoring in an industrial setting.", "recommendations": "Industrial operators should integrate systematic diagnostic protocols into routine fleet management. Policymakers and industry bodies should consider developing guidelines to standardise such practices across sectors.", "key words": "Machinery diagnostics, yield optimisation, quasi-experimental design, overall equipment effectiveness, industrial engineering, maintenance management", "contribution statement": "This paper provides novel empirical evidence from a quasi-experimental field study, establishing a causal link between diagnostic protocols and yield gains in an under-researched industrial context."