Methodological Evaluation and Time-Series Forecasting for Manufacturing Systems Efficiency in Senegal: A Longitudinal Analysis from 2000–2026

"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 = \ + =1^{p\ yt-i + \ + =1^q\ -j + =1^r\ xt-k, where yt is efficiency and xₜ 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