"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 \ yt = \ + =1^{p\ \ yt-i + =1^q\ -j + =1^m\ Xk, t + \, 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
Diagne et al. (Sun,) studied this question.