Enhancing Supply Chain Efficiency: A Holistic Examination of Hybrid Forecasting Models Employing Mode and PERT Technique as Deterministic Factors

Abstract

Inaccurate forecasts can cause severe financial consequences and disrupt supply chain operations for organisations. This study focuses on the pharmaceutical industry, renowned for its complex supply chain and diverse data attributes. It proposes a novel approach to identify the optimal combination of demand forecasting models that enhance accuracy by leveraging deterministic factors using Mode and PERT. By refining model selection in the pharmaceutical industry, this research aims to improve both forecasting precision and supply chain efficiency. A four-level framework based on deterministic factors is proposed to evaluate the extent of hybrid modelling in demand forecasting, empowering practitioners to make informed decisions even in challenging circumstances. The findings offer decision-makers flexibility in selecting suitable forecasting models and assist in tailoring methods to specific conditions. Furthermore, this research highlights the industry's ability to leverage digital technologies and transform existing forecasting methodologies, ensuring uninterrupted business operations during disruptions such as the COVID-19 pandemic.