TITLE:
Forecasting Candidate Numbers for Optimized Exam Logistics: A Naïve Method Approach to Improving BTS National Examination Performance in Douala, Cameroon
AUTHORS:
Alim Hamadou, Henri Thaddée Mba, Prosper Gopdjim Noumo, Flavian Emmanuel Sapnken, Dieudonné Emmanuel Pegnyemb, Jacques Étamé, Jean Gaston Tamba
KEYWORDS:
Forecasting Enrollment, Naïve Method, Academic Logistics, Exam Optimization, Flow Management, BTS Cameroon
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.15 No.7,
July
17,
2025
ABSTRACT: This study explores the application of a forecasting approach based on the naïve method to optimize the logistical performance of the national BTS examinations in the Douala Examination Center. Faced with recurring challenges in managing the flow of exam scripts—including processing delays and logistical cost overruns, this research demonstrates how simple demographic projections can anticipate operational needs and guide the strategic allocation of resources. Analysis of historical data (2017-2024) reveals a consistent linear increase in the number of candidates, with annual growth rates ranging between 8% and 12%. While complete data for 2024-2025 are not yet fully available at the time of writing, preliminary indicators suggest that this upward trend is continuing, underscoring the need for scalable and adaptive logistical systems. The naïve method, chosen for its robustness in data-scarce contexts, achieves a mean absolute percentage error (MAPE) of just 2.1%, thereby confirming its reliability for academic planning. These forecasts enable precise adjustment of logistical resources, particularly by optimizing collection circuits and the capacity of marking centers. The operational implications are significant. Anticipating candidate volumes allows for a reduction of up to 30% in transportation costs through improved routing planning. At the same time, integrating these projections within a Lean management framework helps eliminate time and material waste, thus radically transforming exam administration. This combined approach—both predictive and efficiency-driven—offers a replicable model for African education systems facing demographic pressure and infrastructural constraints. The methodological simplicity of the naïve approach makes it particularly well-suited for administrative contexts with limited resources.