Methodological Framework for Time-Series Forecasting of Industrial Machinery Fleet Yield in Ghana, 2000–2026

"background": "The sustainable management of industrial machinery fleets is critical for national infrastructure development, yet a persistent gap exists in robust, data-driven methodologies for forecasting long-term yield in emerging economies. Existing approaches often lack the temporal granularity and contextual adaptation required for accurate planning in such settings. ", "purpose and objectives": "This article presents a novel methodological framework for time-series forecasting of industrial machinery fleet yield. Its primary objective is to provide a replicable, statistically rigorous model for measuring and projecting yield improvement, thereby supporting strategic asset management and capital investment decisions. ", "methodology": "The methodology integrates an autoregressive integrated moving average (ARIMA) model with exogenous variables (ARIMAX) to account for operational and economic factors. The core forecasting equation is Yt = \ + =1^{p\ Yt-i + =1^q\ -j + =1^m\ Xk, t + \, where Yt is the yield metric. Model parameters are estimated using maximum likelihood, with robust standard errors employed to ensure inference is valid under heteroskedasticity. ", "findings": "As a methodology article, this paper presents no empirical results. However, application of the framework to a simulated dataset demonstrates its capability to generate forecasts with a 95% prediction interval. A key illustrative finding from this simulation is a projected positive, non-linear trend in normalised yield over the forecast horizon. ", "conclusion": "The proposed ARIMAX-based framework provides a technically sound and adaptable methodology for forecasting machinery fleet performance. It addresses the specific need for contextualised, quantitative tools in industrial asset management within developing economies. ", "recommendations": "Practitioners should calibrate the model with high-frequency operational data and regularly update exogenous variable selection to reflect local economic conditions. Further research should validate the framework with longitudinal data from diverse industrial sectors. ", "key words": "asset