What question did this study set out to answer?

The research aims to create and validate a time-series forecasting model to measure and predict manufacturing systems adoption in agro-processing in Senegal.

April 11, 2026Open Access

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

Key Points

The research aims to create and validate a time-series forecasting model to measure and predict manufacturing systems adoption in agro-processing in Senegal.
Constructed a longitudinal dataset from surveys and national statistics.
Developed a SARIMA forecasting model for adoption rates.
Performed diagnostic checks for stationarity and residual autocorrelation.
Expressed parameter uncertainty with 95% confidence intervals.
The SARIMA(1,1,1)(0,1,1)12 model showed goodness of fit, with significant parameters (p < 0.05).
Forecasts predict an increase in adoption rates by approximately 7.3 percentage points over five years.
The evaluation highlighted varying maturity degrees in system integration across different agro-processing sub-sectors.

Abstract

{ "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 \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \, 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

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