Article: Data-driven forecasting of pharmaceutical sales: distinguishing promotional vs. daily scenarios Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2025 Vol.29 No.5 pp.1 - 26 Abstract: This study presents an enhanced temporal fusion framework for pharmaceutical demand forecasting, designed to handle both baseline consumption and promotion-driven sales fluctuations. The approach integrates heterogeneous data sources - such as therapeutic seasonality and catchment health indicators - into a multivariate feature space, and incorporates a knowledge-guided attention mechanism within a temporal fusion transformer to decouple regular and promotional demand. Systematic factor analysis further quantifies the influence of product, store, and temporal variables. Using a dataset of 1.2 million retail transactions, the proposed model reduces forecasting error by 23.6% over traditional methods. Ablation studies confirm the importance of future promotion signals (accuracy loss of 15.4%, <i>p</i> < 0.001), while analysis reveals that seasonal effects vary significantly by drug type. Store-level factors such as deprivation index and competition proximity also significantly affect promotional effectiveness. The framework offers three key contributions: adaptive feature engineering for retail contexts, integration of domain knowledge into temporal modelling, and empirical identification of demand drivers. Practical deployment yielded 31% fewer stock-outs and 27% lower inventory costs, demonstrating its value for resilient and data-driven pharmaceutical supply chain optimisation. Inderscience Publishers - linking academia, business and industry through research

Title: Data-driven forecasting of pharmaceutical sales: distinguishing promotional vs. daily scenarios

Authors: Zhiyong Zeng; Yingjiao Guo; Yali Ji; Yujie Shi; Tao Feng

Addresses: School of Statistics and Mathematics, Yunnan University of Finance and Economics, China ' School of Statistics and Mathematics, Yunnan University of Finance and Economics, China ' School of Statistics and Mathematics, Yunnan University of Finance and Economics, China ' School of Statistics and Mathematics, Yunnan University of Finance and Economics, China ' School of Statistics and Mathematics, Yunnan University of Finance and Economics, China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, China

Abstract: This study presents an enhanced temporal fusion framework for pharmaceutical demand forecasting, designed to handle both baseline consumption and promotion-driven sales fluctuations. The approach integrates heterogeneous data sources - such as therapeutic seasonality and catchment health indicators - into a multivariate feature space, and incorporates a knowledge-guided attention mechanism within a temporal fusion transformer to decouple regular and promotional demand. Systematic factor analysis further quantifies the influence of product, store, and temporal variables. Using a dataset of 1.2 million retail transactions, the proposed model reduces forecasting error by 23.6% over traditional methods. Ablation studies confirm the importance of future promotion signals (accuracy loss of 15.4%, p < 0.001), while analysis reveals that seasonal effects vary significantly by drug type. Store-level factors such as deprivation index and competition proximity also significantly affect promotional effectiveness. The framework offers three key contributions: adaptive feature engineering for retail contexts, integration of domain knowledge into temporal modelling, and empirical identification of demand drivers. Practical deployment yielded 31% fewer stock-outs and 27% lower inventory costs, demonstrating its value for resilient and data-driven pharmaceutical supply chain optimisation.

Keywords: sales forecasting; knowledge-led branch; deep learning; retail analytics; inventory optimisation.

DOI: 10.1504/IJDMB.2025.147534

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.5, pp.1 - 26

Received: 10 Jul 2024
Accepted: 30 Apr 2025
Published online: 20 Jul 2025 *

Title: Data-driven forecasting of pharmaceutical sales: distinguishing promotional vs. daily scenarios

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