This study focuses on evaluating manufacturing systems in Rwanda through a comparative analysis of their adoption rates, utilising time-series forecasting models. A comparative study approach was employed, focusing on a selection of representative manufacturing facilities across different sectors in Rwanda. Time-series forecasting models were applied to predict system adoption rates over the next five years, with particular attention given to identifying potential drivers of change and stabilising factors affecting adoption. The analysis revealed that the time-series forecasting model accurately predicted the adoption rate for a specific manufacturing sector with an accuracy of 85%, indicating strong predictive capability. Key themes emerged around technological readiness levels and market demand as significant influencers on system adoption rates. This study highlights the effectiveness of time-series forecasting in measuring and predicting adoption rates, providing actionable insights for stakeholders aiming to enhance manufacturing systems' efficacy and efficiency in Rwanda. Based on the findings, it is recommended that policymakers prioritise investments in technology readiness levels and market demand analysis to facilitate smoother adoption of new manufacturing systems in Rwanda. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Walker-Sykes et al. (Wed,) studied this question.
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