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Predictive analytics and machine learning for reducing waste in the Pharmaceutical and Retail sectors: A comprehensive model integration approach

Year: 2024

Type: article

Abstract: This paper introduces a predictive framework using advanced machine learning models to improve demand forecasting and inventory management in the pharmaceutical and retail sectors. By integrating real-time data, the approach dynamically aligns production with market conditions, reducing waste and enhancing operational efficiency. Pilot implementations showed significant gains in profitability and sustainability, demonstrating the framework's potential for data-driven, adaptive solutions in a volatile market.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13756098

Author: TALACHEERU, SHYAMSUNDER

Institution: Georgia State University

Language: English

Topic: Supply Chain and Inventory Management

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6892932136

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