Abstract
The adoption of advanced manufacturing systems in developing economies is a critical driver of industrialisation, yet there is a paucity of robust, quantitative methodologies to forecast adoption rates and evaluate systemic integration. This gap hinders evidence-based policy and investment planning in the engineering sector. This data descriptor presents a novel methodological framework for evaluating manufacturing systems and a corresponding time-series forecasting model designed to measure and predict adoption rates. The objective is to provide a replicable analytical tool for engineers and policymakers. The methodology integrates a system readiness assessment with an autoregressive integrated moving average (ARIMA) model, specified as $\phi(B)(1-B)^d yt = \theta(B)\epsilont$, where $y_t$ is the adoption rate. Model parameters were estimated using maximum likelihood, and forecasts were generated with 95% confidence intervals to quantify uncertainty. The application of the framework indicates a positive, non-linear trajectory for systems adoption, with the forecast suggesting a potential increase in the adoption rate of computer-aided technologies to approximately 34% by the end of the forecast horizon. The model's robustness was confirmed through sensitivity analysis. The developed framework provides a statistically rigorous and practically applicable tool for forecasting technological adoption in manufacturing, filling a significant methodological gap in the engineering literature for emerging industrial contexts. It is recommended that future research applies this model to sub-sectoral analyses and that policymakers utilise such forecasts for targeted infrastructure and skills development programmes. manufacturing systems, technological adoption, time-series forecasting, ARIMA modelling, industrial policy, engineering management This paper introduces a novel hybrid methodological framework that uniquely combines a structured system evaluation with a statistical forecasting model, specifically tailored for assessing manufacturing technology uptake in industrialising economies.