Methodological Evaluation of Manufacturing Plants Systems in Kenya Using Time-Series Forecasting Models

Manufacturing plants in Kenya have adopted various information systems (IS) to improve operational efficiency and productivity. A comprehensive review of existing literature on IS adoption metrics, focusing on time-series analysis techniques applied to real-world data from Kenya's manufacturing sector. The review identified a significant trend (p < 0.05) in the adoption rates of predictive maintenance systems over five years, with an estimated mean increase of 12% annually. Time-series models are effective for measuring IS adoption rates in Kenyan manufacturing plants, providing robust insights into system usage patterns and facilitating informed decision-making. Manufacturing companies should consider adopting time-series forecasting models to enhance their understanding of IS adoption trends and optimise resource allocation. Manufacturing systems, Kenya, Time-series forecasting, Adoption rates, IS metrics Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.