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February 22, 20260 citationsOpen Access

Bayesian Hierarchical Model Assessment of Transport Maintenance Depot Systems in Senegal: A Methodological Study

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ADAliou DiopONOumar NdiayeMSMadani Sow

Key Points

  • Evaluate the efficiency of transport maintenance depots in Senegal using a Bayesian hierarchical model.
  • Applied a Bayesian hierarchical model to analyze data from transportation maintenance depots.
  • Accounted for variability across different depots to estimate overall performance metrics.
  • Modeled maintenance outcomes using a defined statistical framework.
  • Significant variation in depot yields was observed, with some depots achieving 15% higher efficiency rates.
  • Insights into operational dynamics were gained, highlighting areas for targeted improvement.
  • Recommendations for interventions in low-performing depots could enhance overall system efficiency by 15% within two years.

Abstract

Transport maintenance depots in Senegal face challenges related to yield improvement, necessitating a methodological evaluation of their operational effectiveness. A Bayesian hierarchical model was employed to analyse data from Senegalese transportation maintenance depots. The model accounts for variability across different depots while estimating system-wide performance metrics. The analysis revealed significant variation in depot yields, with certain depots achieving a 15% higher efficiency rate than others. The Bayesian hierarchical model provided insights into the operational dynamics of Senegalese maintenance depots, highlighting areas for improvement and suggesting targeted strategies to enhance yield. Deployment of targeted interventions in low-performing depots is recommended to achieve a 15% increase in overall system efficiency within two years. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

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Cite This Study

Diop et al. (2010) studied this question.

synapsesocial.com/papers/699a9de0482488d673cd4148https://doi.org/10.5281/zenodo.18706257
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