Methodological Evaluation and Time-Series Forecasting for Yield Improvement in Kenyan Industrial Machinery Fleets

{ "background": "Industrial machinery fleets in Kenya face persistent challenges in operational yield, with existing maintenance and scheduling systems often failing to optimise performance. A lack of robust, data-driven forecasting tools tailored to local operational conditions hinders systematic improvement.", "purpose and objectives": "This study aimed to methodologically evaluate current fleet management systems and to develop a bespoke time-series forecasting model for predicting and improving machinery yield in a Kenyan industrial context.", "methodology": "A hybrid methodology was employed, integrating a diagnostic evaluation of fleet management practices with the development of an Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model. The model, specified as Yt = \μ + \∑i=1ᵖ\ Yt-i + \∑j=1q\ \εt-j + \∑k=1ʳ\ Xt,k + \εₜ, was trained and validated using high-frequency operational data from a fleet of earth-moving equipment.", "findings": "The diagnostic evaluation revealed that 68% of fleets relied on reactive maintenance strategies. The ARIMAX model achieved a statistically significant forecast accuracy, with a mean absolute percentage error (MAPE) of 7.3% (95% CI: 6.8, 7.9) for weekly yield, outperforming benchmark models. Predictive maintenance scheduling informed by the model was shown to be the primary driver of potential yield gains.", "conclusion": "The developed forecasting model provides a quantitatively robust tool for yield prediction, demonstrating that a shift from reactive to predictive management is both feasible and advantageous for industrial machinery operations in the studied region.", "recommendations": "Fleet managers should adopt predictive, data-driven models for maintenance scheduling. Further research should focus on integrating real-time sensor data to enhance model granularity and adaptability across different machinery types.", "key words": "fleet management, predictive maintenance, ARIMAX, operational yield,