"background": "The operational efficiency of industrial machinery fleets is a critical determinant of productivity and economic output in developing economies. In Uganda, a lack of robust, data-driven methodologies for assessing and forecasting fleet performance hinders strategic maintenance and capital planning. ", "purpose and objectives": "This study aims to develop and validate a novel methodological framework for evaluating the efficiency of industrial machinery fleets. Its core objective is to construct a predictive time-series model to forecast key performance metrics, thereby quantifying potential efficiency gains. ", "methodology": "A hybrid methodology was employed, integrating field data collection from selected industrial sites with analytical modelling. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as \ yt = \ + =1^{p\ \ yt-i + =1^q\ -j + =1^r\ xt, k + \, where yt is the target efficiency metric. Model parameters were estimated using maximum likelihood. ", "findings": "The ARIMAX (2, 1, 1) model demonstrated strong predictive capability for machine availability. A key finding is that a 10% increase in scheduled, data-informed maintenance correlated with a 3. 2% improvement in forecasted fleet availability (95% CI: 2. 1% to 4. 3%). Diagnostic checks confirmed the model's residuals were white noise. ", "conclusion": "The proposed methodological framework provides a statistically rigorous tool for fleet efficiency evaluation. The forecasting model offers a reliable mechanism for predicting performance trends, enabling proactive management. ", "recommendations": "Industry practitioners should adopt systematic data logging of machinery operational parameters. Policymakers are encouraged to support the development of sector-wide benchmarks based on the presented methodology to foster best practice adoption. ", "key words": "machinery efficiency, time-series analysis, ARIMAX modelling,
Namugga et al. (Fri,) studied this question.