A Performance Forecasting Model for Optimizing CDF-Funded Construction Projects in the Copperbelt Province, Zambia

The Constituency Development Fund (CDF) has become a key mechanism for delivering small-scale urban infrastructure in Zambia. However, persistent challenges such as project delays, cost overruns, and quality deficiencies undermine the effectiveness of these interventions. This study addresses a critical gap in the literature and practice by developing a novel performance forecasting model tailored to the unique governance and technical context of CDF-funded projects. The model integrates Adaptive Neuro-Fuzzy Inference Systems (ANFIS) with the Analytic Hierarchy Process (AHP) to forecast performance across five key indicators: cost-effectiveness, schedule adherence, quality compliance, safety performance, and client satisfaction. Using stakeholder data from 196 respondents and historical project records, the model was trained and validated using MATLAB. It achieved strong predictive accuracy, with a coefficient of determination (R²) of 0.92 and a root mean square error (RMSE) of 0.09. These results demonstrate the model’s utility as a decision-support tool for local authorities and urban planners, enabling early detection of underperformance and facilitating proactive interventions. The model contributes to performance-based planning by providing a data-driven, stakeholder-informed forecasting framework that is adaptable to resource-constrained environments. Its application can enhance transparency, optimize resource use, and support inclusive urban development in rapidly growing municipalities.