{ "background": "The adoption of advanced manufacturing systems in West Africa is a critical driver of industrial development, yet there is a scarcity of quantitative models to forecast adoption trajectories. This gap hinders effective policy and investment planning for technological modernisation.", "purpose and objectives": "This study aimed to develop and validate a time-series forecasting model to predict the adoption rate of advanced manufacturing systems, specifically computer numerical control and industrial robotics, within the country's industrial sector.", "methodology": "A longitudinal dataset of technology deployment across major industrial zones was analysed. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as $\\Delta yt = \\alpha + \\sum{i=1}^{p}\\phii \\Delta y{t-i} + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\sum{k=1}^{m}\\betak X{k,t} + \\epsilont$, where $yt$ is the adoption level. Model robustness was assessed using heteroskedasticity-robust standard errors.", "findings": "The model forecasts a sustained positive trajectory, with the adoption rate projected to increase by approximately 60% over the forecast horizon. A key driver was identified as the cost-competitiveness of retrofitted systems. The 95% confidence interval for the long-term adoption level ranged from 54% to 67% of the potential market.", "conclusion": "The developed ARIMAX model provides a statistically robust tool for forecasting technological adoption in an emerging industrial context. The results indicate a significant, though gradual, uptake of advanced manufacturing systems.", "recommendations": "Policymakers should prioritise initiatives that reduce the financial and technical barriers to retrofitting existing machinery. Further research should integrate firm-level survey data to refine the model's explanatory variables.", "key words": "Advanced manufacturing, forecasting, time-series analysis, ARIMAX, technology adoption, industrial policy", "cont