A Methodological Evaluation and Time-Series Forecasting Model for Manufacturing Systems Adoption in Senegalese Agro-Processing

{ "background": "The adoption of advanced manufacturing systems in Senegalese agro-processing is critical for enhancing productivity and value addition. However, rigorous methodological frameworks for evaluating this adoption and forecasting its trajectory are lacking, hindering evidence-based policy and investment.", "purpose and objectives": "This study aims to develop and validate a novel time-series forecasting model to measure and predict the adoption rates of manufacturing systems within the nation's agro-processing sector. A secondary objective is a methodological evaluation of existing system implementations.", "methodology": "We constructed a longitudinal dataset from plant-level surveys and national industrial statistics. The core forecasting model is a seasonal autoregressive integrated moving average (SARIMA) process, specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont$, where $yt$ is the adoption index. Model diagnostics included checks for stationarity and residual autocorrelation, with parameter uncertainty expressed via 95% confidence intervals.", "findings": "The SARIMA(1,1,1)(0,1,1)12 model provided the best fit, with all parameters statistically significant (p < 0.05). Forecasts indicate a sustained but decelerating adoption rate, predicting an increase of approximately 7.3 percentage points over the next five-year period. The methodological evaluation revealed significant heterogeneity in system integration maturity across different processing sub-sectors.", "conclusion": "The proposed model offers a robust quantitative tool for tracking technological uptake in agro-processing. The findings confirm that while adoption is progressing, the pace is insufficient to meet national agricultural transformation goals without targeted intervention.", "recommendations": "Policy should prioritise technical assistance and incentive schemes tailored to low-maturity sub-sectors. Future research should incorporate exogenous variables like energy costs into the forecasting framework.", "key words": "technology adoption, agro-processing, forecasting, SARIMA, Senegal, manufacturing systems", "contribution statement": "This paper provides the first application of