PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 13, 20260 citationsOpen Access

Comparative Methodological Evaluation of Industrial Machinery Fleets in Ghana: A Multilevel Regression Analysis for Yield Optimisation

View Full Paper
KAK. Asante

Key Points

  • This study aims to evaluate different methods for assessing industrial machinery fleet performance to determine the best approach for yield improvement.
  • Comparative analysis of operational data from various industrial sites
  • Utilized a multilevel regression model for analysis
  • Model comparisons were conducted using Akaike Information Criterion and robust standard errors
  • The multilevel regression model explained 34% more variance in yield than traditional pooled regression
  • Preventive maintenance compliance showed a stronger positive effect on yield (β = 0.42, 95% CI [0.31, 0.53]) than other variables like machine age or fuel type

Abstract

"background": "The operational efficiency of industrial machinery fleets is a critical determinant of productivity in developing economies. In Ghana, a lack of robust methodological frameworks for evaluating fleet performance hinders systematic yield optimisation in key sectors such as mining, construction, and agriculture. ", "purpose and objectives": "This study conducts a comparative methodological evaluation of fleet management systems, with the primary objective of determining the most effective analytical approach for measuring and predicting yield improvements. It aims to identify key operational variables influencing output. ", "methodology": "A comparative study was performed using operational data from multiple industrial sites. A multilevel regression model, Y{ij = \0j + \1jX1ij + \2X2ij + rij, with \0j = \00 + \01Zj + u0j, was employed, where i indexes machinery and j indexes sites. Model comparisons were based on Akaike Information Criterion and robust standard errors. ", "findings": "The multilevel model significantly outperformed traditional pooled regression, explaining 34% more variance in yield. A key finding was that preventive maintenance compliance had a stronger positive effect on yield (β = 0. 42, 95% CI 0. 31, 0. 53) than machine age or fuel type. ", "conclusion": "The application of multilevel regression provides a superior methodological framework for analysing nested industrial fleet data, offering more accurate insights for yield optimisation compared to conventional single-level techniques. ", "recommendations": "Industry practitioners should adopt hierarchical modelling techniques for fleet performance analysis. Policymakers should support the development of standardised data collection protocols to facilitate such advanced analyses across sectors. ", "key words": "fleet management, multilevel modelling, regression analysis, operational efficiency, yield optimisation, industrial engineering", "contribution statement": "This paper provides a novel comparative validation of mult

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

K. Asante (2012) studied this question.

synapsesocial.com/papers/69b3ab4c02a1e69014ccc059https://doi.org/10.5281/zenodo.18964983
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

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

  1. 1Methodological Evaluation and Multilevel Regression Analysis of Industrial Machinery Fleet Systems for Yield Improvement in Kenya2005
  2. 2Methodological Assessment of Industrial Machinery Fleet Systems in Ghana: Multilevel Regression Analysis for Yield Improvement Exploration2004
  3. 3Methodological Evaluation of Industrial Machinery Fleets in Ghana Using Multilevel Regression Analysis for Efficiency Gains2008
  4. 4Methodological Evaluation and Multilevel Regression Analysis of Industrial Machinery Fleet Systems for Yield Improvement in Ethiopia (2000–2026)2011
  5. 5Methodological Evaluation of Industrial Machinery Fleets in Ghana: Multilevel Regression Analysis for Cost-Effectiveness Assessment2013