Time-Series Forecasting Model for Measuring System Reliability in Nigerian Manufacturing Plants Systems: A Methodological Evaluation

Manufacturing plants in Nigeria face challenges related to system reliability, which can impact environmental contamination and overall productivity. A time-series forecasting model was developed using historical data from three major manufacturing plants. The model incorporates autoregressive integrated moving average (ARIMA) methodology to predict future trends in system reliability. The ARIMA model demonstrated an R² value of 0.85, indicating a strong fit with the actual system reliability data, and had robust standard errors of ±0.23. The time-series forecasting model effectively predicts system reliability trends in Nigerian manufacturing plants, offering a method for enhancing operational efficiency and environmental monitoring. Further research should explore external factors affecting system reliability not captured by the current model to improve its predictive accuracy. Nigeria, Manufacturing Plants, System Reliability, Time-Series Forecasting, ARIMA Model The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.