{ "background": "Maintenance systems for transport depots in Kenya have undergone several policy-driven reforms, yet the rate of adoption of new technologies and practices remains a critical, unmeasured factor in their efficacy. A diagnostic tool to quantify this adoption rate is required for robust policy evaluation.", "purpose and objectives": "This policy analysis aims to develop and evaluate a novel time-series forecasting model to diagnose the adoption rate of maintenance systems within the nation's transport depots, assessing the impact of recent infrastructure policies.", "methodology": "A quantitative analysis was conducted using depot-level panel data. The core model is an autoregressive integrated moving average (ARIMA) process with an exogenous policy intervention variable: $Yt = \\mu + \\phi1 Y{t-1} + \\theta1 \\epsilon{t-1} + \\beta It + \\epsilont$, where $It$ marks policy implementation. Model parameters were estimated using maximum likelihood, with inference based on heteroskedasticity-robust standard errors.", "findings": "The model indicates a significant positive shift in the adoption rate trajectory following a major depot modernisation policy, with a step-change increase of approximately 18 percentage points (95% CI: 12.4 to 23.6). Forecasts to the mid-2020s suggest a convergence towards a steady-state adoption level, contingent on sustained investment.", "conclusion": "The forecasting model provides a validated diagnostic tool, revealing that targeted policy interventions have substantially accelerated adoption rates, though long-term saturation is projected without further innovation.", "recommendations": "Policy should institutionalise the continuous collection of depot performance metrics to feed the diagnostic model. Future investment cycles must be informed by these adoption rate forecasts to pre-empt stagnation.", "key words": "infrastructure policy, maintenance engineering, adoption diagnostics, ARIMA modelling, transport systems", "contribution statement": "This paper introduces a novel policy diagnostic tool—a time-series forecasting model specifically for adoption rate measurement—