Methodological Evaluation and Time-Series Forecasting for Cost-Effectiveness of Industrial Machinery Fleets in Rwanda (2000–2026)

{ "background": "The management of industrial machinery fleets represents a significant capital and operational expenditure for developing economies. In Rwanda, the lack of robust, data-driven methodologies for forecasting fleet costs and performance has hindered strategic asset management and infrastructure development planning. ", "purpose and objectives": "This study aims to develop and evaluate a novel time-series forecasting model to measure the cost-effectiveness of industrial machinery fleets, providing a predictive tool for long-term capital planning and maintenance scheduling. ", "methodology": "A methodological evaluation of fleet systems was conducted using historical operational and cost data. A seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model, specified as \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \ + \ Xt, was developed for forecasting. Model parameters were estimated using maximum likelihood, and forecasts were generated with 95% confidence intervals. ", "findings": "The SARIMAX model demonstrated strong predictive accuracy, with a mean absolute percentage error of 8. 7% for out-of-sample forecasts. A key finding was a forecasted 22% increase in total ownership cost per operating hour over the forecast horizon, driven primarily by rising maintenance expenditures. The confidence intervals for the long-term forecast remained within ±12. 5% of the point estimate, indicating robust model performance. ", "conclusion": "The developed time-series model provides a statistically robust and practical tool for forecasting the cost-effectiveness of industrial machinery fleets. It enables proactive financial and operational decision-making for asset-intensive industries. ", "recommendations": "Fleet managers and policy planners should adopt similar predictive modelling to optimise replacement cycles and capital budgets. Future research should integrate real-time telematics data to enhance model granularity. ", "key words": "asset management, time-series analysis, cost forecasting, predictive maintenance, capital planning, SARIMAX", "contribution statement": "This paper presents a novel application of the SARIMAX model for