{ "background": "The adoption of advanced manufacturing systems in developing economies is a critical driver of industrialisation, yet robust methodologies for forecasting adoption rates are lacking. Existing models often fail to account for the specific infrastructural and economic constraints of these contexts, leading to unreliable projections for policy and investment.", "purpose and objectives": "This article presents a novel methodological framework for generating accurate time-series forecasts of manufacturing systems adoption. The primary objective is to provide a replicable, evidence-based model tailored to the structural realities of a developing industrial sector.", "methodology": "The framework integrates an Autoregressive Integrated Moving Average (ARIMA) model with exogenous socio-economic variables. The core forecasting equation is $yt = \\mu + \\phi1 y{t-1} + \\theta1 \\epsilon{t-1} + \\beta Xt + \\epsilont$, where $Xt$ represents a vector of exogenous inputs including grid electricity reliability and access to industrial credit. Model parameters are estimated using maximum likelihood, and forecast uncertainty is quantified with 95% prediction intervals.", "findings": "The methodological evaluation, applied to a synthesised dataset, demonstrates that the integrated model reduces forecast error by an estimated 18–22% compared to a univariate benchmark. A key finding is the pronounced sensitivity of adoption forecasts to fluctuations in infrastructure quality, a variable often omitted in conventional analyses.", "conclusion": "The proposed framework provides a statistically rigorous and context-sensitive tool for forecasting technological adoption in manufacturing. It successfully captures the non-linear dynamics influenced by local constraints.", "recommendations": "Practitioners and policymakers should adopt similar integrated modelling approaches that incorporate infrastructural variables. Future methodological work should focus on validating the framework with primary longitudinal data and extending it to other industrial sectors.", "key words": "forecasting methodology, ARIMA modelling, technology adoption, industrial development, manufacturing systems, time-series analysis", "contribution statement": "This paper contributes a novel integrated forecasting framework that explicitly models the dependency of manufacturing technology adoption