"background": "Agri-manufacturing is a critical sector for economic development, yet persistent inefficiencies undermine its profitability and sustainability. A significant research gap exists in applying advanced econometric diagnostics to evaluate and forecast cost-effectiveness within these integrated production systems. ", "purpose and objectives": "This study aimed to develop and validate a novel time-series forecasting model to diagnose cost-effectiveness in integrated agri-manufacturing systems. The primary objective was to provide a diagnostic tool for operational efficiency and future cost projections. ", "methodology": "We constructed a dynamic econometric model using high-frequency panel data from multiple manufacturing plants. The core specification was a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model: Ct = \ + =1^{p\ Ct-i + =1^q\ -j + =1^m\ Xk, t + \, where Ct is the cost-effectiveness index. Model parameters were estimated via maximum likelihood, with inference based on robust standard errors to account for heteroskedasticity. ", "findings": "The model forecasts indicate a significant negative trend in the cost-effectiveness index, with a projected decline of approximately 8. 2% over the forecast horizon. Diagnostic checks confirmed model validity, with all exogenous variable coefficients statistically significant at the 95% confidence level. ", "conclusion": "The developed model provides a robust diagnostic and forecasting tool, revealing a concerning trajectory for systemic cost-efficiency. This necessitates targeted operational interventions to reverse the identified trend. ", "recommendations": "Plant managers should adopt the model for continuous operational diagnostics. Policymakers are advised to integrate such forecasting into sectoral support programmes to pre-empt efficiency losses. ", "key words": "agri-manufacturing, cost-effectiveness, time-series forecasting, SARIMAX, operational diagnostics", "contribution statement": "This paper introduces a novel
Thandiwe van der Merwe (Tue,) studied this question.