How the Pharma Industry Benefits From AI-Powered ERP
| International Journal of Computer Science and Engineering |
| © 2025 by SSRG - IJCSE Journal |
| Volume 12 Issue 3 |
| Year of Publication : 2025 |
| Authors : Alok Chakraborty |
10.14445/23488387/IJCSE-V12I3P103
How to Cite?
Alok Chakraborty, "How the Pharma Industry Benefits From AI-Powered ERP," SSRG International Journal of Computer Science and Engineering , vol. 12, no. 3, pp. 17-22, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I3P103
Abstract:
With the rise of AI-powered Intelligent ERP systems, the pharmaceutical industry is poised for significant transformation in the coming years. Such intelligent systems automate processes, improve operation efficiency, reduce operational costs, and help organizations comply with regulations while aiding in their growth. An AI-powered ERP solution can manage all supply chain management operational aspects by tracking demand variations, minimizing waste, and avoiding drug shortages. They can also streamline the drug development process by speeding up clinical trials, automating data analysis, and assisting in decision-making with predictive analytics.
In addition, with AI-based automation, there is minimal scope for error, ensuring strict quality controls in manufacturing and instant updates if compliance with worldwide regulations is maintained. Through the implementation of AI-driven ERP solutions, pharmaceutical organizations can realize cost reductions, increase productivity, foster innovation, and ultimately enhance patient outcomes while creating a more flexible and resilient industry. At the same time, it’s imperative to look at the ethical and privacy concerns that come along with the transformation. Businesses must look at ways to secure sensitive patients and handle research data. By balancing innovation with ethical responsibility, AI-powered systems can drive efficiency in complying with regulations.
Keywords:
AI-powered ERP, Pharmaceutical automation, Predictive analytics, Regulatory compliance, Supply chain optimization, Personalized medicine.
References:
[1] Aliya Ahmad, and Rizwan Ahmad, “Evaluating Enterprise Systems for Production, Procurement, Documentation, and Compliance in Pharmaceutical Companies,” AI, IoT and the Fourth Industrial Revolution Review, vol. 14, no. 2, pp. 39-50, 2024.
[Publisher Link]
[2] Blessing Ameh et al., “Bridging the Gap: Innovations in Supply Chain Technology Through ERP Integration and Intelligent Automation,” International Journal of Computer Applications Technology and Research, vol. 13, no. 10, pp. 1-15, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Konstantinos Chronopoulos, “Development of a Hospital Enterprise Resource Planning (ERP) System,” Master’s Thesis, University Of Piraeus, pp. 1-70, 2024.
[Google Scholar] [Publisher Link]
[4] Ulhas Desale, “The Initiation of a Pharmaceutical Drug Launch and Its Preparation within Enterprise Systems of Supply Chain,” SSRN, vol. 10, no. 9, pp. a82-a86, 2023.
[Google Scholar] [Publisher Link]
[5] Mary Jane C. Samonte, Herzon B. Chu, and Lex Anilov T. Ogaya, “Artificial Intelligence Integration and Administration into Pharmaceutical Manufacturing Execution Systems: Assessing Potentials and Challenges for Process Optimization,” 14th International Conference on Software Technology and Engineering, Macau, China, pp. 184-190, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Venkata Pavan Kumar Juturi, “Advancing Enterprise Applications with Transformative Effects of Digital Technologies,” International Journal of Research Publication and Reviews, vol. 5, no. 7, pp. 3645-3650, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Sudheer Kumar Kothuru, “AI-Driven Innovations in Healthcare: Improving Diagnostics and Patient Care,” International Journal of Machine Learning and Artificial Intelligence, vol. 4, no. 4, pp. 1-13, 2023.
[Google Scholar] [Publisher Link]
[8] Gaurav Kumar, “The Future of Enterprise Resource Planning (ERP): Harnessing Artificial Intelligence,” International Journal of Scientific Research in Computer Science Engineering and Information Technology, vol. 10, no. 4, pp. 94-99, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Gaurav Kumar, “Critical Success Factors of Adopting an Enterprise System for Pharmaceutical Drug Traceability,” Universal Journal of Pharmacy and Pharmacology, vol. 2, no. 1, pp. 3-10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Leonardo Longo, Orazio Tomarchio, and Natalia Trapani, “A Structured Approach for Enhancing Clinical Risk Monitoring and Workflow Digitalization in Healthcare,” Decision Analytics Journal, vol. 11, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Makope Tshehla-Nkuna, and Ndala Yves Mulongo, “Artificial Intelligence Tools for Early Detection of Product Defects in the Pharmaceutical Industry,” 18th International Conference on Intelligent Systems and Knowledge Engineering, Fuzhou, China, pp. 1 7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Hariprasad Mandava, “Streamlining Enterprise Resource Planning through Digital Technologies,” World Journal of Advanced Engineering Technology and Sciences, vol. 12, no. 2, pp. 719-725, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Olivia McDermott et al., “Pharma Industry 4.0 Deployment and Readiness: A Case Study within a Manufacturer,” The TQM Journal, vol. 36, no. 9, pp. 456-476, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Vasile Minea et al., “Opportunity Assessment for ERP Enhancements Based on Artificial Intelligence, IOT and Big Data,” Economic Series, vol. 23, no. 3, pp. 405-430, 2023.
[Google Scholar] [Publisher Link]
[15] Moh’d Anwer Al-Shboul, “Do Artificial Intelligence System Adoptions Foster Production Management Supply Chain Performance in Pharmaceutical Manufacturing Firms? An Empirical Exploring Study from the MENA Region,” Business Process Management Journal, vol. 30, no. 7, pp. 2427-2455, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Naga Sathya Lakshman Kumar Kanulla, “A Qualitative Examination of SAP Enterprise Resource Planning System in Pharmaceutical Distribution Companies,” Master’s Theses, University of the Cumberlands , pp. 1-271, 2021.
[Google Scholar]
[17] Marwa Naqi, Mukhtar AL-Hashimi, and Allam Hamdan, “Impact of Innovative Technologies in Healthcare Organization Productivity with ERP,” Applications of Artificial Intelligence in Business, Education and Healthcare, vol. 954, pp. 309-330, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ajay Babu Pazhayattil, and Gyongyi Konyu-Fogel, “An Empirical Study To Accelerate Machine Learning And Artificial Intelligence Adoption In Pharmaceutical Manufacturing Organizations,” Journal of Generic Medicines: The Business Journal for the Generic Medicines Sector, vol. 19, no. 2, pp. 81-91, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] K.V. Rajesh Kumar, and Tezovalli Bodhimisetty, “Transforming Pharma Supply Chains Using Data Analytics Unleashed for Enhanced Visibility and Competitiveness,” AI Impacts in Digital Consumer Behavior, pp. 271-290, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Adeel Shah et al., “Digital Supply Chain Reshaping Industrial Ecology: A Study from the Pharma Industry,” Industrial Ecology, pp. 107-123, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Lakshmy Subramanian, “Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers,” Logistics, vol. 5, no. 1, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Adarsh Vaid, and Chetan Sharma, “Leveraging SAP and Artificial Intelligence for Optimized Enterprise Resource Planning: Enhancing Efficiency, Automation, and Decision-Making,” World Journal of Advanced Research and Reviews, vol. 14, no. 3, pp. 762-769, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hassan Younis, Balan Sundarakani, and Malek Alsharairi, “Applications of Artificial Intelligence and Machine Learning within Supply Chains: Systematic Review and Future Research Directions,” Journal of Modelling in Management, vol. 17, no. 3, pp. 916 940, 2021.
[CrossRef] [Google Scholar] [Publisher Link]