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March 13, 20260 citationsOpen Access

A Bayesian Hierarchical Model for Cost-Effectiveness in Uganda's Industrial Machinery Fleet Management: A Policy Analysis for 2000–2026

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KSKato SsekamanyaIMIsaac MugumePNPatience Nalwoga

Key Points

  • The aim is to assess the cost-effectiveness of industrial machinery fleets in Uganda using a Bayesian hierarchical model.
  • Construct a Bayesian hierarchical model to analyze fleet data.
  • Integrate operational, maintenance, and procurement costs into the model.
  • Estimate posterior distributions using Markov chain Monte Carlo methods.
  • Performance-based contracting may enhance fleet cost-effectiveness by 18-27%.
  • Maintenance strategy is identified as the most significant factor influencing cost-effectiveness with high confidence.

Abstract

"background": "The cost-effectiveness of industrial machinery fleets is a critical yet under-researched factor in national infrastructure development and industrial policy. In many developing economies, poor fleet management leads to substantial capital waste and project delays, but robust analytical frameworks for policy evaluation are lacking. ", "purpose and objectives": "This policy analysis develops and applies a novel Bayesian hierarchical model to evaluate the cost-effectiveness of industrial machinery fleet management systems. It aims to quantify the impact of different policy interventions on lifecycle costs and operational availability. ", "methodology": "A Bayesian hierarchical model is constructed to analyse fleet data, integrating operational, maintenance, and procurement costs. The model structure is y{ij \ (\ + \ Xij, \²), \\; \ \ (\\, \²), where yij represents cost-effectiveness for machine i in category j. Posterior distributions are estimated using Markov chain Monte Carlo methods, with inference based on 95% credible intervals. ", "findings": "The analysis reveals that a policy shift towards performance-based contracting could improve fleet cost-effectiveness by an estimated 18–27%. The model indicates with high posterior probability (>. 95) that maintenance strategy is the most influential hierarchical factor, outweighing machine age or initial capital cost. ", "conclusion": "The Bayesian hierarchical approach provides a statistically robust framework for policy analysis in engineering asset management. It demonstrates that significant efficiency gains are achievable through evidence-based policy reform focused on maintenance and procurement linkages. ", "recommendations": "Policy should mandate the adoption of integrated data systems for fleet monitoring. Procurement guidelines must be revised to prioritise total lifecycle cost over initial purchase price. A pilot programme for performance-based maintenance contracts is recommended. ", "key words": "Bayesian hierarchical model, cost-effectiveness, machinery management, asset lifecycle, policy analysis, industrial engineering", "contribution statement": "This paper provides a novel methodological framework for

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Cite This Study

Ssekamanya et al. (2007) studied this question.

synapsesocial.com/papers/69b3abd602a1e69014ccd091https://doi.org/10.5281/zenodo.18968195
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Bayesian Hierarchical Model for Cost-Effectiveness in South African Industrial Machinery Fleet Systems2018
  2. 2A Bayesian Hierarchical Model for the Cost-Effectiveness of Industrial Machinery Fleets in Rwanda: A Methodological Evaluation2004
  3. 3A Bayesian Hierarchical Model for Efficiency Gains in Nigeria's Industrial Machinery Fleets: A Policy Analysis2000
  4. 4A Bayesian Hierarchical Model for the Cost-Effectiveness of Industrial Machinery Fleets in Ethiopia: A Methodological Evaluation2014
  5. 5Bayesian Hierarchical Model for Cost-Effectiveness Analysis of Industrial Machinery Fleets in Kenya2003