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This research evaluates the effectiveness of a Time-Series forecasting model for predicting maintenance costs in industrial machinery fleets in Senegal.

March 8, 2026Open Access

Time-Series Forecasting Model Evaluation for Cost-Effectiveness in Senegal's Industrial Machinery Fleets Systems

Key Points

This research evaluates the effectiveness of a Time-Series forecasting model for predicting maintenance costs in industrial machinery fleets in Senegal.
Comparative analysis using the ARIMA model for maintenance cost and usage pattern forecasting over five years.
Model parameter estimation with robust standard errors to account for data uncertainty.
Prediction accuracy assessment of the ARIMA models.
ARIMA models achieved an average prediction accuracy of 85% for forecasting equipment failure rates.
Optimally managing equipment lifecycle can significantly reduce maintenance costs.
Providing insights on optimal fleet size and replacement scheduling.

Abstract

Industrial machinery fleets play a critical role in Senegal's economic development, influencing productivity and operational costs. A comparative study using ARIMA (AutoRegressive Integrated Moving Average) model to forecast maintenance costs and usage patterns over a five-year period. Model parameters were estimated with robust standard errors accounting for uncertainty in data. The ARIMA models demonstrated an average prediction accuracy of 85% in forecasting equipment failure rates, providing insights into optimal fleet size and replacement schedules. ARIMA models offer a reliable framework for cost-effectiveness analysis in industrial machinery fleets systems, facilitating better resource allocation and operational planning. Adopting ARIMA forecasts can lead to significant reductions in maintenance costs by optimally managing the lifecycle of equipment within Senegalese industries. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

Time-Series Forecasting Model Evaluation for Cost-Effectiveness in Senegal's Industrial Machinery Fleets Systems

Key Points

Abstract

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