An Efficient Medicine Demand Prediction System Using LTH-SES-Based Machine Learning Technique with Pharmacy Supply Chain

Authors

  • Guna Sekhar Sajja Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA. E-mail: gsajja1524@ucumberlands.edu & Sajja.guna@gmail.com , ORCID: https://orcid.org/0000-0003-0327-2450 Author https://orcid.org/0000-0003-0327-2450
  • Mohan Kumar Meesala Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA.E-mail: mmeesala4025@ucumberlands.edu & mohanmeesala.researcher@gmail.com , ORCID: https://orcid.org/0009-0002-3585-5060 Author https://orcid.org/0009-0002-3585-5060

DOI:

https://doi.org/10.71426/jcdt.v1.i1.pp10-18

Keywords:

Demand forecasting, Machine Learning (ML), Supply Chain Management (SCM), Pharmaceutical Supply Chain (PSC), Renyi Entropy (RE)

Abstract

The Pharmaceutical Industry (PI) is deemed as one amongst the most substantial industrial sectors. Therefore, for the healthcare system, effective management of the Pharmaceutical Supply Chain (PSC) is crucial. Thus, an efficient medicine demand prediction system is proposed in this paper by using the Logistic Tanh activation adapted Single Exponential Smoothing (LTH-SES)-based Machine Learning (ML) technique with PSC. The previous medication sales data from the PSC are collected from publicly available sources to predict future medication needs. Later, to reduce the vast amount of information present in the dataset, the MapReduce model is performed. Later, the features are extracted. After that, by using the Renyi Entropy Principal Component Analysis (REPCA) technique, the dimensionality is reduced. Later, by using Min-Max Distance Centroid Fuzzy C-Means (M2DCFCM) clustering, the medications are grouped together. Lastly, for forecasting the future demand for medications, the LTH-SES technique is used. The proposed system’s performance is further validated and the outcomes exhibited that the proposed methodology forecasts the demand more effectively than other prevailing techniques.

 

 

 

 

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Published

2025-06-29 — Updated on 2025-07-01

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How to Cite

Sajja, G. S., & Meesala, M. K. (2025). An Efficient Medicine Demand Prediction System Using LTH-SES-Based Machine Learning Technique with Pharmacy Supply Chain. Journal of Computing and Data Technology, 1(1), 10-18. https://doi.org/10.71426/jcdt.v1.i1.pp10-18 (Original work published 2025)