Time-Series Forecasting Model for Cost-Efficiency Analysis in Tanzanian Manufacturing Plants Systems,

Manufacturing plants in Tanzania have been undergoing significant operational changes since the early 2000s. However, there is a need for robust methodologies to assess their cost-effectiveness over extended periods. A comprehensive time-series forecasting model was developed using an autoregressive integrated moving average (ARIMA) approach. The model was validated on historical data from to, ensuring its accuracy in projecting future trends. The ARIMA model demonstrated a strong predictive capability with a root mean square error (RMSE) of less than 5%, indicating high precision in forecasting manufacturing costs and efficiencies across different plant environments. This study underscores the effectiveness of ARIMA models for cost-efficiency analysis in Tanzanian manufacturing settings, providing actionable insights to improve operational performance. Manufacturing managers should implement periodic cost-effectiveness assessments using the developed model to optimise resource allocation and reduce costs. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.