{ "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 (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
Merwe et al. (Wed,) studied this question.