Abstract
{ "background": "The management of industrial machinery fleets is a critical component of national infrastructure development, yet there is a scarcity of robust, data-driven methodologies for evaluating their long-term cost-effectiveness in developing economies. Existing approaches often lack the temporal analysis required for strategic capital planning and maintenance budgeting.", "purpose and objectives": "This data descriptor presents a novel methodological framework and a curated dataset designed to evaluate the cost-effectiveness of industrial machinery fleets. The primary objective is to enable time-series forecasting of total ownership costs to support evidence-based asset management decisions.", "methodology": "A longitudinal dataset was constructed from national industrial surveys, maintenance logs, and procurement records. The core analytical model is a seasonal autoregressive integrated moving average (SARIMA) model, specified as $\\phi(B)\\Phi(B^s)(1-B)^d(1-B^s)^D yt = \\theta(B)\\Theta(B^s)\\epsilont$, where $y_t$ represents the cost-effectiveness index. Model parameters were estimated using maximum likelihood, with robust standard errors calculated to account for heteroskedasticity.", "findings": "The forecasting model indicates a persistent upward trend in the total cost of ownership index, with a projected mean increase of 22% over the forecast horizon. Model diagnostics, including analysis of the Ljung-Box Q-statistic on residuals, suggest the absence of significant autocorrelation, supporting the model's specification.", "conclusion": "The developed methodology provides a statistically sound framework for forecasting machinery fleet economics. The accompanying dataset offers a valuable resource for benchmarking and comparative analysis in similar industrial contexts.", "recommendations": "Implement the described forecasting model within national asset management agencies for proactive budget allocation. Future work should integrate real-time sensor data from telematics to enhance model granularity and predictive accuracy.", "key words": "asset management, total cost of ownership, SARIMA modelling, infrastructure economics, predictive maintenance, industrial engineering", "contribution statement": "This work provides the first open-access dataset and a dedicated SAR