R. Ganesh,
- MMS Marketing, Ex-Assistant Marketing Officer, Department of Marketing, SPPU University, Pune, Maharashtra, India
Abstract
Pharmaceutical supply chains are changing because of artificial intelligence (AI). The pharmaceutical industry has always had to deal with uncertainty. From the start of research to the final delivery of medicines to patients, it faces scientific risks, government rules, and supply chain problems. In recent years, these challenges have gotten worse because there is more drug shortage, political issues are messing up transportation, and regulators are asking for more openness and tracking. At the same time, new advances in artificial intelligence are starting to change how companies in the industry work. AI in the pharmaceutical field is often talked about in terms of finding new drugs and testing them on people, but there are also important uses further along the process. Areas like making medicines, planning supplies, and managing logistics are where AI can make a real difference in how well things run. The change is huge. The market for AI in pharmaceuticals is expected to grow from about $4 billion now to $25.7 billion by 2030.This quick growth shows how much AI is helping teams handle uncertainty better, making pharmaceutical supply chains more reliable. Tools like predictive supply chain planning, smart logistics and flexible inventory optimization are helping. With the 2026 deadline coming, AI-driven demand forecasting and digital twins, along with smart logistics systems, are making supply chains more resilient, transparent, and profitable. These advancements help pharmaceutical companies manage complex, compliant, and patient-focused supply chain operations.
Keywords: Artificial intelligence (AI),Enterprise Resource Planning (ERP), Internet of Things (IoT), Long Short-Term Memory (LSTM), Large Language Model (LLM), active pharmaceutical ingredients (APIs), Good Distribution Practices (GDP).
References
1 D.R. Serrano, F.C. Luciano, B.J. Anaya, et al. Artificial intelligence (AI) applications in drug discovery and drug delivery: revolutionizing personalized medicine Pharmaceutics, 16 (10) (2024), p. 1328, 10.3390/pharmaceutics16101328
2 A.D. Adekola, S.A. Dada Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: a conceptual framework Computer Sci IT Res J, 5 (11) (2024), pp. 2580-2593, 10.51594/csitrj.v5i11.1709
3 J. Shen, F. Bu, Z. Ye, et al. Management of drug supply chain information based on “artificial intelligence + vendor managed inventory” in China: perspective based on a case study Front Pharmacol (2024), 10.3389/fphar.2024.1373642
4 M. Riaz, F. Qamar, S. Tariq, K. Alsager AI-driven LOPCOW-AROMAN framework and topological data analysis using circular intuitionistic fuzzy information: healthcare supply chain innovation Mathematics, 12 (22) (2024), p. 3593, 10.3390/math12223593
5 F.S. Khan, A. Al Masum, J. Adam, M.R. Karim, S. Afrin AI in healthcare supply chain management: enhancing efficiency and reducing costs with predictive analytics J Comput Sci Technol Stud, 6 (5) (2024), pp. 85-93, 10.32996/jcsts.2024.6.5.8
6 A.A. Ahmed, A.U. Abdullahi, A.Y. Gital, A.Y. Dutse Application of artificial intelligence in supply chain management: a review on strengths and weaknesses of predictive modeling techniques Sci J Eng Technol, 1 (2) (2024), pp. 1-18, 10.69739/sjet.v1i2.131
7 P. Kumar, K. Kant, N. Mishra, V. Babu, N. Chander AI for optimizing supply chain management Int J Res Appl Sci Eng Technol, 12 (11) (2024), 10.22214/ijraset.2024.65059
8 S.S. Goswami, S. Mondal, S. Sarkar Artificial intelligence-enabled supply chain management: unlocking new opportunities and challenges Artif Intell Appl (2024), 10.47852/AIA42021814
9 D’souza, S., Nazareth, D., Vaz, C., & Shetty, M. (2021). Blockchain and AI in pharmaceutical supply chain. Proceedings of the International Conference on Smart Data Intelligence (ICSMDI 2021). SSRN. https://doi.org/10.2139/ssrn.3852034.
10 Wu, G. (2023). Exploring the value of AI technology in optimizing and implementing supply chain data for pharmaceutical companies. Innovation in Science and Technology, 2(3), 1–6. 〈https://www.paradigmpress.org/ist/article/view/580〉.
11 Buntz, B. (2024). 6 signs AI momentum in drug discovery is building. Drug Discovery Trends. Retrieved from 〈https://www.drugdiscoverytrends.com/six-signs-ai-driven-drug-discovery-trends-pharma-industry〉.
12 D. Ozdemir, M. Sharma, A. Dhir, T. Daim Supply chain resilience during the COVID-19 pandemic Technol Soc, 68 (2022), Article 101847, 10.1016/j.techsoc.2021.101847
13 O.E. Ibikunle, P.A. Usuemerai, L.A. Abass, et al. AI and digital health innovation in pharmaceutical development Comput Sci IT Res J, 5 (10) (2024), 10.51594/csitrj.v5i10.1649
14 D. Atnafu, A. Balda The impact of inventory management practice on firms’ competitiveness and organizational performance: empirical evidence from micro and small enterprises in Ethiopia (Article) Cogent Bus Manag, 5 (1) (2018), p. 1503219, 10.1080/23311975.2018.1503219
15 S.K. Niazi The coming of age of AI/ML in drug discovery, development, clinical testing, and manufacturing: the FDA perspectives Drug Des Dev Ther, 17 (2023), pp. 2691-2725
16 L.B. Amerine, D.R. Calvert, A.L. Pappas, et al. Implementation of an integrated pharmacy supply management strategy Am J HealthSyst Pharm, 74 (2017), pp. 2071-2075, 10.2146/ajhp160316
17 Modugula, V.Y.K., & Shridhar Hegde, R. (2021). Costs & Benefits of an AI/IT Tool for the Swedish Antibiotics Supply Chain: An AI/IT Tool to address shortages of Antibiotics in Sweden.
18 G. Baryannis, S. Dani, G. Antoniou Predicting supply chain risks using machine learning: the trade-off between performance and interpretability Future Gener Comput Syst FGCS, 101 (2019), pp. 993-1004, 10.1016/j.future.2019.07.059
19 A.M. Knemeyer Logistics and supply chain management: creating value-adding networks, third edition Transp J, 45 (1) (2006), pp. 61-62, 10.5325/transportationj.45.1.0061
20 G. Baryannis, S. Validi, S. Dani, G. Antoniou Supply chain risk management and artificial intelligence: state of the art and future research directions Int J Prod Res, 57 (7) (2019), pp. 2179-2202, 10.1080/00207543.2018.1530476
21 A. Gunasekaran, E.W.T. Ngai Expert systems and artificial intelligence in the 21st century logistics and supply chain management Expert Syst Appl, 41 (1) (2014), pp. 1-4, 10.1016/j.eswa.2013.09.006
22 Y. Qiu, J. Qiao, P.M. Pardalos Optimal production, replenishment, delivery, routing and inventory management policies for products with perishable inventory Omega, 82 (2019), pp. 193-204, 10.1016/j.omega.2018.01.006
23 G.K. Rand Inventory management and production planning and scheduling J Oper Res Soc, 52 (7) (2001), p. 845 -845
24 M. Muller Essentials of inventory management HarperCollins Leadership, (2019)
25 B. Ageron, S. Benzidia, M. Bourlakis Healthcare logistics and supply chain–issues and future challenges (January) Supply Chain Forum: An International Journal, 19, Taylor & Francis, (2018), pp. 1-3 (January)
26 K. Moons, G. Waeyenbergh, L. Pintelon Measuring the logistics performance of internal hospital supply chains–a literature study Omega, 82 (2019), pp. 205-217
27 Vibin Ravi Kumar, Pallavi Waghmare, et al. Forecasting drug demand for optimal medical inventory management: a data-driven approach with advanced machine learning techniques Int. J. Innov. Sci. Res. Technol. (IJISRT), 8 (. 9) (2023), pp. 221-229, 10.5281/zenodo.8351668 〈www.ijisrt.com〉
28 Ö. Albayrak Ünal, B. Erkayman, B. Usanmaz Applications of artificial intelligence in inventory management: a systematic review of the literature Arch Comput Methods Eng, 30 (4) (2023), pp. 2605-2625
29 E. Saha, P.K. Ray Modelling and analysis of inventory management systems in healthcare: a review and reflections Comput Ind Eng, 137 (2019), Article 106051
30 Dienyfer Pinheiro, do, Vale Renata, Helena Queiroz, et al. Importance of pharmaceutical supervision in medications in emergency hospital settings Rev IberoAm De Humanid Ciências e Educ, 10 (6) (2024), pp. 1494-1507, 10.51891/rease.v10i6.14454
31 Zhang Siyi, Zhu Lilong Drugs quality supervision strategy of different distribution channels in pharmaceutical supply chain Front Public Health (2022), p. 10, 10.3389/fpubh.2022.954371
32 I.H. Sarker AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems SN Comput Sci, 3 (2) (2022), p. 158
33 S. Dara, S. Dhamercherla, S.S. Jadav, C.M. Babu, M.J. Ahsan Machine learning in drug discovery: a review Artif Intell Rev, 55 (3) (2022), pp. 1947-1999
34 M. Bagherian, E. Sabeti, K. Wang, et al. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper Brief Bioinforma, 22 (1) (2021), pp. 247-269
35 Y. Kumar, A. Koul, R. Singla, M.F. Ijaz Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda J Ambient Intell Humaniz Comput, 14 (7) (2023), pp. 8459-8486
36 A.B. Chapman, K.S. Peterson, P.R. Alba, S.L. DuVall, O.V. Patterson Detecting adverse drug events with rapidly trained classification models Drug Saf, 42 (2019), pp. 147-156
37 M.E. Elkin, X. Zhu Predictive modeling of clinical trial terminations using feature engineering and embedding learning Sci Rep, 11 (1) (2021), p. 3446
38 R. Saha, S. Shofiullah, S.A. Faysal, A.T. Happy, I. Arif, A. Siddiki Systematic literature review on artificial intelligence applications in supply chain demand forecasting Acad J Bus Adm Innov Sustain, 4 (4) (2024), pp. 109-126, 10.69593/ajbais.v4i04.136
39 B.P. Sha, B. Shirin, B. Minhazur Rahman, M. Shahjalal The role of AI in promoting sustainability within the manufacturing supply chain: achieving lean and green objectives Acad J Bus Adm Innov Sustain, 4 (3) (2024), pp. 79-93, 10.69593/ajbais.v4i3.97
40 R. Dubey, A. Gunasekaran, S.J. Childe, C. Blome, T. Papadopoulos Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource-based view and big data culture Br J Manag, 30 (2) (2019), pp. 341-361, 10.1111/1467-8551.12355
41 A. Nandi, M.M.H. Emon, M.A. Azad, H.M. Shamsuzzaman, M.M. Rahman Developing an extruder machine operating system through PLC programming with HMI design to enhance machine output and overall equipment effectiveness (OEE) Int J Sci Eng, 1 (3) (2024), pp. 1-13, 10.62304/ijse.v1i3.157
42 M. Ashrafuzzaman The impact of cloud-based management information systems on HRM efficiency: an analysis of small and medium-sized enterprises (SMEs) Acad J Artif Intell Mach Learn Data Sci Manag Inf Syst, 1 (1) (2024), pp. 40-56, 10.69593/ajaimldsmis.v1i01.124
43 H.N. Perera, B. Fahimnia, T. Tokar Inventory and ordering decisions: a systematic review on research driven through behavioral experiments Int J Oper Prod Manag, 40 (7/8) (2020), pp. 997-1039, 10.1108/ijopm-05-2019-0339
44 A. Aljohani Predictive analytics and machine learning for real-time supply chain risk mitigation and agility Sustainability, 15 (20) (2023), p. 15088
45 R.H. Chowdhury AI-driven business analytics for operational efficiency World J Adv Eng Technol Sci, 12 (2) (2024), pp. 535-543, 10.30574/wjaets.2024.12.2.0329
46 K. Douaioui, R. Oucheikh, O. Benmoussa, C. Mabrouki Machine learning and deep learning models for demand forecasting in supply chain management: a critical review Appl Syst Innov, 7 (5) (2024), p. 93, 10.3390/asi7050093
47 Kuruvilla, J.M., Kathrine, G.J.W., Kirubakaran, S., Prasad, G.P., & Evangelina, G. (2023). Implementation of Industry 4.0 in Supply Chain Management in the Healthcare Industry. 2023 2nd International Conference on Electronics, Communications and Aerospace Technology (ICECAA). IEEE. https://doi.org/10.1109/ICECAA58104.2023.10212417.
48 P. Kumar, K. Kant, N. Mishra, V. Babu, N. Chander AI for optimizing supply chain management Int J Res Appl Sci Eng Technol (IJRASET), 12 (XI) (2024), p. 549, 10.22214/ijraset.2024.65059
49 M.C. Nezianya, A.O. Adebayo, P. Ezeliora A critical review of machine learning applications in supply chain risk management World J Adv Res Rev, 23 (3) (2024), pp. 1554-1567, 10.30574/wjarr.2024.23.3.2760
50 P. Whig, P. Sharma, N. Aneja, A.A. Elngar, N. Silva Artificial intelligence and machine learning for sustainable development: innovations, challenges, and applications CRC Press (2024), 10.1201/9781003497189
51 Panigrahi, B.S., Vanitha, A., Palav, M.R., Tilak Babu, S.B.G., Nair, A.M., & Bogiri, N. (2024). IoT Applications in Cold Chain Management for Pharmaceuticals: Ensuring Product Integrity and Safety. 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST). IEEE. https://doi.org/10.1109/ICRTCST61793.2024.10578468.
52 B. Ameh Digital tools and AI: using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements Int J Sci Res Arch, 13 (1) (2024), pp. 2741-2754, 10.30574/ijsra.2024.13.1.1995
53 K.R. Kotha, S.C. Tokachichu, S. Padakanti Synergizing AI and ERP for predictive supply chain management and quality assurance in healthcare (September-October) Int J Future Med Res, 6 (5) (2024), 10.36948/ijfmr.2024.v06i05.29762
54 K. Huanbutta, K. Burapapadh, P. Kraisit, et al. Artificial intelligence-driven pharmaceutical industry: a paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance Eur J Pharm Sci, 203 (2024), Article 106938, 10.1016/j.ejps.2024.106938
55 C. Zhang, W. Ma, C. Fan, L. Qi The application and innovation of digitization in the field of procurement management Multidiscip J Zhejiang Wanli Univ, 17 (1) (2024), 10.54097/mqjzwf0507-11-2024
56 M. Riad, M. Naimi, C. Okar Enhancing supply chain resilience through artificial intelligence: developing a comprehensive conceptual framework for AI implementation and supply chain optimization Logistics, 8 (4) (2024), p. 111, 10.3390/logistics8040111
57 W. Guo Exploring the value of AI technology in optimizing and implementing supply chain data for pharmaceutical companies Innov Sci Technol, 2 (3) (2023), pp. 1-6, 10.54097/mqjzwf0507-11-2024
58 T.T. Teoh, Y.J. Goh AI in supply chain management Artificial Intelligence in Business Management, Springer, (2023), 10.1007/978-981-99-4558-0_12
59 P.K. Detwal, G. Soni, S.K. Jakhar, et al. Machine learning-based technique for predicting vendor incoterm (Contract) in global omnichannel pharmaceutical supply chain J Bus Res, 158 (2023), Article 113688, 10.1016/j.jbusres.2023.113688
60 Kim, J., & Min, M. (2024). From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process. arXiv Preprint. https://doi.org/10.48550/arXiv.2402.01717.
Research and Reviews: A Journal of Pharmaceutical Science
| Volume | 17 | |
| 02 | ||
| Received | 02/07/2026 | |
| Accepted | 08/09/2026 | |
| Published | 14/09/2026 | |
| Publication Time | 74 Days |