Efficacy of Seasonal Factor-Adjusted Naïve Forecasting in Operations
Management: Insights from a Simulation Study
Jean C. Essila and Jaideep Motwani
Seidman College of Business, Grand Valley State University, USA
Volume 18: 2024, pp. 123-152; ABSTRACT
Purpose: This study explores the effectiveness of a novel forecasting method, the
Seasonal Factor-Adjusted Naïve (SFAN) forecasting, within operations management business
education. It aims to assess the method’s ability to improve forecasting accuracy and
pedagogical effectiveness compared to traditional forecasting techniques. Design/Methodology/
Approach: Employing a quasi-experimental design, the research compares students’
performance across three operations management classes using different forecasting methods:
SFAN, a traditional method, and a control group with no specific method. The primary
performance metric used is the Mean Absolute Percentage Error (MAPE), supported by
ANCOVA controls for a comprehensive analysis. Findings: The results indicate that the SFAN
method significantly outperforms traditional methods. Students using SFAN demonstrated
lower MAPE scores, indicating higher forecasting accuracy. The effectiveness of SFAN in
scenarios with pronounced seasonal trends was particularly notable. Originality: This study is
the first to empirically test the SFAN method in an operations management educational setting,
offering novel insights into integrating advanced forecasting techniques in business education.
Research Limitations/Implications: While the study provides valuable insights, it is limited by
its focus on a specific educational setting. Future research could explore the SFAN method’s
applicability across various disciplines and cultural contexts. Practical Implications: The
findings suggest that incorporating SFAN into operations management business education
curricula could significantly enhance students’ forecasting skills, preparing them better for realworld challenges. Social Implications: By improving forecasting accuracy and pedagogical
methods in business education, this research contributes to developing more skilled and
competent business professionals, potentially impacting decision-making and strategic planning
in various industries.
Keywords: forecasting, simulation, seasonal factor, adjusted naïve method, forecasting
simulation, ANCOVA, MAPE
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