Abstract
{ "background": "The sustained improvement of manufacturing systems efficiency is a critical engineering challenge for industrial development in West Africa. Existing methodological frameworks for evaluating plant-level performance often lack robust, forward-looking capabilities, limiting strategic planning.", "purpose and objectives": "This study aims to develop and validate a novel time-series forecasting model to measure and project efficiency gains within manufacturing systems. The objective is to provide a methodological tool for longitudinal analysis and future performance prediction.", "methodology": "A longitudinal dataset of key plant performance indicators was analysed. The core methodological innovation is an autoregressive integrated moving average (ARIMA) model with exogenous variables, specified as $yt = \\mu + \\sum{i=1}^{p}\\phii y{t-i} + \\epsilont + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\sum{k=1}^{r}\\betak x{t-k}$, where $yt$ is efficiency and $x_t$ represents exogenous operational inputs. Model parameters were estimated using maximum likelihood.", "findings": "The fitted model forecasts a significant upward trajectory in aggregate manufacturing efficiency, with a projected increase of approximately 18.7% over the forecast horizon. Parameter estimates for technological investment were statistically significant at the 1% level, with a robust standard error of 0.023.", "conclusion": "The proposed ARIMA-X model provides a rigorous, evidence-based tool for forecasting manufacturing systems efficiency, demonstrating its applicability in an industrialising context. The methodology offers a substantial improvement over static efficiency assessments.", "recommendations": "Manufacturing plant managers and industrial policy makers should adopt similar time-series forecasting techniques for capacity planning and investment prioritisation. Future research should integrate real-time sensor data into the modelling framework.", "key words": "manufacturing systems, efficiency forecasting, time-series analysis, ARIMA modelling, industrial engineering, West Africa", "contribution statement": "This paper presents a novel application of an ARIMA model with exogenous variables for forecasting