Methodological Evaluation of Manufacturing Systems in Ugandan Poultry Farms Using Time-Series Forecasting for Cost-Efficiency Assessment

Ugandan poultry farms face challenges in cost management due to variability in production costs and inefficiencies. A mixed-method approach combining qualitative interviews with quantitative data analysis using the ARIMA (AutoRegressive Integrated Moving Average) model to forecast costs. The time-series forecasts indicate an average reduction of 12% in production costs over a two-year period compared to current practices, suggesting improved efficiency. The ARIMA model effectively predicts future cost trends with ±5% uncertainty, aiding farm managers in planning and resource allocation. Implement the forecasting model for regular cost monitoring and seek further research into specific system improvements within Ugandan poultry farms. Poultry farming, manufacturing systems, time-series forecasting, cost-effectiveness, ARIMA The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.