{ "background": "The adoption of integrated industrial machinery fleet systems is a critical determinant of productivity and capital efficiency in heavy industries. In the South African context, a systematic analysis of adoption trends and a robust forecasting methodology have been lacking, hindering strategic infrastructure and maintenance planning.", "purpose and objectives": "This study aims to develop and validate a time-series forecasting model to measure and project the adoption rates of advanced industrial machinery fleet systems. The objective is to provide a methodological framework for evaluating technological uptake within the national industrial sector.", "methodology": "A quantitative analysis was conducted using national industry survey and sales data. The methodological evaluation centred on comparing forecasting techniques, with a seasonal autoregressive integrated moving average (SARIMA) model selected for its superior fit. The model is defined as $\\text{SARIMA}(p,d,q)(P,D,Q)s$, where parameters were optimised using maximum likelihood estimation. Forecast uncertainty was quantified using 95% prediction intervals.", "findings": "The SARIMA(1,1,1)(0,1,1)12 model provided the most accurate forecasts, with a mean absolute percentage error (MAPE) of 4.7%. The analysis projects a continued upward trend in adoption, with the forecast indicating a 22% increase in the penetration rate over the next five-year period. The prediction intervals suggest a high degree of confidence in the directional trend.", "conclusion": "The developed time-series model offers a statistically robust tool for forecasting the adoption of industrial machinery systems. The findings confirm a significant and sustained increase in technological uptake within the sector.", "recommendations": "Industry stakeholders and policymakers should utilise this forecasting methodology for long-term capital budgeting and skills development planning. Further research should integrate economic indicators to enhance model explanatory power.", "key words": "fleet management systems, technological adoption, time-series analysis, SARIMA modelling, industrial engineering, forecasting", "contribution statement": "This paper presents a novel application of SARIMA modelling to forecast the adoption of industrial machinery