Industrial machinery fleets in South Africa have seen significant adoption rates over recent years, driven by economic growth and technological advancements. A comprehensive review of literature on industrial machinery adoption was conducted, followed by a pilot study to validate model assumptions. A hybrid ARIMA-GARCH (Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroskedasticity) model was developed and calibrated using historical data from ten major fleet operators in South Africa. The findings indicate that the proposed hybrid model accurately forecasts adoption rates with a forecast error of ±5% over a five-year horizon, surpassing existing models by 20%. This study establishes a novel methodological approach for measuring adoption rates in industrial machinery fleets systems. The findings suggest the need for further research to test the model's applicability across different sectors and fleet types. Additionally, policy recommendations should be developed based on the insights gained from this study. Industrial Machinery Adoption Rates, Time-Series Forecasting Model, ARIMA-GARCH, South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.