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March 2, 20260 citationsOpen Access

Bayesian Hierarchical Model Evaluation for Yield Improvement in Process-Control Systems in Senegal

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SDSabar DiopSNSamba Ngom

Key Points

  • This research aims to evaluate yield improvements in process-control systems using a Bayesian hierarchical model across various sites in Senegal.
  • Applied a Bayesian hierarchical model to analyze yield data from multiple sites in Senegal.
  • Accounted for site-specific variability to estimate overall yield improvements.
  • Utilized robust uncertainty quantification in the analysis.
  • Identified significant site-specific variations in yield improvement.
  • One facility achieved an average 15% increase in yield compared to baseline levels.
  • Provided actionable insights for enhancing process-control systems.

Abstract

Recent advancements in process-control systems have shown promise in improving yield efficiency across various industries, including those in Senegal where resource management is critical. The methodology involves the application of a Bayesian hierarchical model to analyse data from multiple sites within Senegal. This approach accounts for variability between sites while estimating overall yield improvements with robust uncertainty quantification. Bayesian hierarchical modelling revealed significant site-specific variations in yield improvement, with one specific facility achieving an average 15% increase in yield compared to baseline levels. The Bayesian hierarchical model provides a comprehensive framework for understanding and optimising process-control systems in Senegal, offering actionable insights for system enhancements. Based on the findings, we recommend deploying the Bayesian hierarchical model across all relevant sites within Senegal to systematically assess and optimise yield performance. 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. (2005) studied this question.

synapsesocial.com/papers/69a52e56f1e85e5c73bf1e24https://doi.org/10.5281/zenodo.18814288
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