Methodological Evaluation of Manufacturing Systems in Ethiopian Plants using Time-Series Forecasting for Yield Improvement Assessment

Manufacturing systems in Ethiopia have been identified as critical components for enhancing agricultural productivity. However, there is a need to evaluate these systems methodologically and forecast their impact on crop yields. A mixed-method approach was employed, combining qualitative interviews with quantitative data analysis using autoregressive integrated moving average (ARIMA) models. Time series analysis was used to forecast future yields based on historical data. The ARIMA model indicated a significant positive correlation between manufacturing system efficiency and yield improvement, with an estimated increase of 15% in yield for optimised systems compared to baseline conditions. The time-series forecasting models effectively predicted yield improvements under optimal manufacturing system configurations, highlighting the potential for enhancing agricultural productivity through targeted interventions. Further research should focus on implementing these findings in real-world settings and exploring additional factors affecting yield improvement. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.