What does this research mean for the field?

The ARIMA model improves the accuracy of forecasting system reliability in Senegalese manufacturing plants by reducing mean squared error by 20%. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

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

March 2, 2026Open Access

Time-Series Forecasting Model for Evaluating System Reliability in Senegalese Manufacturing Plants: An Engineering Approach

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Authors

ISIbrahima Sow MTMahama ToureUniversité Alassane Ouattara SNSélima NdiayeUniversité Alioune Diop de Bambey

Discussion

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Implication

Engineering analysis forecasts system reliability in Senegalese manufacturing plants, suggesting improved maintenance strategies.

Key Points

The research aims to evaluate system reliability in manufacturing plants in Senegal using a time-series forecasting model.
Conducted time-series analysis using the ARIMA model.
Quantified prediction uncertainty with a 95% confidence interval.
Compared mean squared error for system reliability forecasts with previous methods.
Achieved a 20% reduction in mean squared error for forecasting.
Demonstrated improved accuracy of the ARIMA model.
Provided a framework for future research and application in manufacturing systems.

Cite This Study

Sow et al. (2005) studied this question.

synapsesocial.com/papers/69a52e56f1e85e5c73bf1ebc https://doi.org/10.5281/zenodo.18814277

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Time-Series Forecasting Model for Evaluating System Reliability in Senegalese Manufacturing Plants: An Engineering Approach

Discussion

Member takes

Implication

Key Points

Cite This Study

Time-Series Forecasting Model for Evaluating System Reliability in Senegalese Manufacturing Plants: An Engineering Approach

Discussion

Member takes

Implication

Key Points

Cite This Study

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