What question did this study set out to answer?

The research aims to evaluate system reliability in Ugandan manufacturing plants using a time-series forecasting model.

February 25, 2026Open Access

Time-Series Forecasting Model for System Reliability Evaluation in Ugandan Manufacturing Plants

Key Points

The research aims to evaluate system reliability in Ugandan manufacturing plants using a time-series forecasting model.
Developed a time-series forecasting model using ARIMA methodology
Quantified prediction uncertainty with robust standard errors
Assessed operational conditions over the past five years
Forecasted system reliability showed an upward trend over five years
Improvements in operational efficiency are possible if challenges are addressed
The model requires further validation for effectiveness in local contexts

Abstract

Manufacturing plants in Uganda face challenges related to system reliability due to varying operational conditions and resources. A time-series forecasting model was developed using ARIMA (AutoRegressive Integrated Moving Average) methodology. Uncertainty in predictions is quantified with robust standard errors. The forecasted system reliability showed an upward trend over the past five years, indicating potential improvements in operational efficiency if addressed. The time-series forecasting model provided insights into future system performance but requires further validation and adaptation to local conditions. Further research should explore the impact of technological upgrades on system reliability and implement the model for predictive maintenance strategies. ARIMA, Ugandan manufacturing, Time-series analysis, System reliability The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

Time-Series Forecasting Model for System Reliability Evaluation in Ugandan Manufacturing Plants

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Abstract

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