Recent studies have highlighted the importance of time-series forecasting models in evaluating manufacturing plant systems across various industries, including agriculture. In Uganda, there is a need for robust methodologies to assess efficiency gains and improve operational performance. The methodology will involve literature review, data collection from selected Ugandan manufacturing plants, and the implementation of a time-series forecasting model using statistical software. Model validation will be conducted through cross-validation techniques with robust standard errors. This theoretical framework provides a solid foundation for future empirical studies and offers actionable insights for optimising manufacturing plant operations in Uganda's agricultural sector. Manufacturing plants should consider implementing time-series forecasting models with seasonal adjustments to enhance their efficiency. Policy-makers can use this framework to develop targeted interventions that promote sustainable agriculture practices. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Sserunkuwa et al. (Thu,) studied this question.