African Post-Harvest Technology (Food Science/Technology)

Advancing Scholarship Across the Continent

Vol. 2002 No. 1 (2002)

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Time-Series Forecasting Model for Evaluating Transport Maintenance Depots Reliability in Kenya

Mbugua Keterinotino, Strathmore University Kipruto Mwihaki, Strathmore University
DOI: 10.5281/zenodo.18748985
Published: May 19, 2002

Abstract

This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in Kenya: time-series forecasting model for measuring system reliability in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of transport maintenance depots systems in Kenya: time-series forecasting model for measuring system reliability, Kenya, Africa, Engineering, working paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.

How to Cite

Mbugua Keterinotino, Kipruto Mwihaki (2002). Time-Series Forecasting Model for Evaluating Transport Maintenance Depots Reliability in Kenya. African Post-Harvest Technology (Food Science/Technology), Vol. 2002 No. 1 (2002). https://doi.org/10.5281/zenodo.18748985

Keywords

KenyanGeographic Information SystemsTime-series AnalysisMonte Carlo SimulationReliability EngineeringPredictive MaintenanceData Analytics

References