"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
Ssekamanya et al. (Wed,) studied this question.
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