"background": "The adoption of industrial machinery is a critical driver of productivity and economic development, yet systematic, data-driven methodologies for forecasting its uptake in developing economies are lacking. This gap hinders effective infrastructure planning and capital investment strategies. ", "purpose and objectives": "This study aims to develop and evaluate a robust methodological framework for analysing historical trends and generating reliable forecasts of industrial machinery fleet adoption. The primary objective is to provide a predictive model to inform sectoral planning and policy. ", "methodology": "A time-series analysis was conducted on national-level fleet data. The methodology centred on an Autoregressive Integrated Moving Average (ARIMA) model, specified as \ᵈ yt = c + =1^{p\ \ᵈ yt-i + =1^q\ -j + \, where \ᵈ denotes differencing of order d. Model diagnostics included checks for stationarity and residual autocorrelation, with forecast uncertainty quantified using 95% prediction intervals. ", "findings": "The analysis reveals a consistent positive trajectory in adoption rates, with the fitted model forecasting a compound annual growth rate of approximately 4. 7% over the forecast horizon. The model's predictions are statistically robust, with narrow prediction intervals indicating high confidence in the central forecast trend. ", "conclusion": "The developed ARIMA model provides a validated, quantitative tool for forecasting machinery fleet growth, demonstrating that adoption follows a predictable, upward trend underpinned by historical patterns. ", "recommendations": "It is recommended that industry stakeholders and government planners integrate this forecasting methodology into long-term strategic planning for skills development, maintenance infrastructure, and energy demand projections. Subsequent research should incorporate multivariate analysis with economic indicators. ", "key words": "machinery fleet, adoption forecasting, time-series analysis, ARIMA modelling, infrastructure planning, developing economy", "contribution statement": "This paper presents a novel application of ARIMA modelling
Mubiru et al. (2022) studied this question.