In Tanzania, there is a growing need to enhance process-control systems in critical maritime industries to mitigate risks and improve efficiency. A comprehensive review of existing literature on process control and risk management was conducted, followed by the application of advanced statistical methods including ARIMA (AutoRegressive Integrated Moving Average) models. The dataset comprised historical data from five major maritime industries in Tanzania over a period of ten years. The analysis revealed that temperature fluctuations have a significant impact on equipment failure rates with an average reduction potential of up to 30% when predictive maintenance strategies are implemented. This study validates the efficacy of ARIMA models for forecasting risk in process-control systems, providing actionable insights for industry stakeholders. Based on the findings, it is recommended that maritime industries in Tanzania implement proactive maintenance schedules and invest in advanced monitoring technologies to reduce equipment downtime and operational costs. Risk Management, Process Control Systems, Time-Series Forecasting, ARIMA Models, Maritime Industries The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Mwinyi et al. (Mon,) studied this question.