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

"background": "The operational efficiency of industrial machinery fleets is a critical determinant of productivity in developing economies. In Nigeria, systemic underperformance and a lack of predictive maintenance frameworks lead to significant yield losses and capital expenditure waste. Current evaluations are often reactive, lacking robust, data-driven methodologies for forecasting and improvement. ", "purpose and objectives": "This paper aims to develop and evaluate a methodological framework for the systematic assessment of machinery fleets. Its core objective is to construct a time-series forecasting model to predict yield performance and quantify potential improvements from targeted interventions. ", "methodology": "A hybrid modelling approach was employed, integrating Autoregressive Integrated Moving Average (ARIMA) with exogenous variables (ARIMAX) to account for operational and maintenance factors. The model, specified as Yt = \ + =1^{p\ Yt-i + =1^q\ -i + =1^k\ Xj, t + \ₜ, was trained on high-frequency sensor data from a fleet of 47 heavy industrial machines. Model diagnostics included Ljung-Box tests and analysis of robust standard errors. ", "findings": "The ARIMAX (2, 1, 1) model demonstrated strong predictive capability, explaining 78% of the variance in yield metrics. A key finding was a forecasted 22% potential yield improvement through optimised preventive maintenance scheduling, with a 95% confidence interval of 18. 5%, 25. 3%. The integration of vibration analysis data as an exogenous variable proved statistically significant. ", "conclusion": "The proposed methodological framework provides a viable, evidence-based tool for transitioning from reactive to predictive fleet management. The forecasting model offers a quantifiable basis for strategic investment in maintenance, directly linking operational data to productivity outcomes. ", "recommendations": "Implement the ARIMAX forecasting model as a core component of fleet management systems. Establish continuous data logging protocols