Digital transformation and industry 4.0 in pharma manufacturing: the role of iot, ai, and big data
Abstract
The pharmaceutical industry is undergoing a significant transformation driven by the integration of digital technologies, collectively known as Industry 4.0. This shift is redefining how drugs are developed, manufactured, and distributed. Key technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics are at the forefront of this change, enabling smart manufacturing, real-time process optimization, and enhanced supply chain management. IoT facilitates the creation of interconnected production environments where sensors and devices continuously monitor critical parameters, ensuring optimal conditions and predictive maintenance. AI accelerates drug discovery through predictive modeling, automates quality control processes, and employs predictive analytics to enhance maintenance and process improvement. Big data empowers data-driven decision-making, ensures regulatory compliance through comprehensive analysis, and supports the shift toward personalized medicine by enabling customized drug production. Despite the significant benefits, the adoption of these technologies poses challenges, including integration with existing systems, data security concerns, and navigating a complex regulatory landscape. This review explores these technologies' impact on pharmaceutical manufacturing, highlighting successful case studies and best practices. Additionally, it discusses the future directions, including the move towards fully autonomous systems and the importance of collaboration between tech companies, manufacturers, and regulators to drive innovation and ensure compliance. The continued evolution of digital technologies in pharma manufacturing promises to enhance efficiency, reduce costs, and deliver more personalized treatments.
Keywords:
Industry 4.0, IoT, AI, Big Data, Pharmaceutical Manufacturing, Smart Factories, Digital Transformation
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2. Xu LD, Xu EL, Li L. Industry 4.0: state of the art and future trends. Int J Prod Res. 2018 Nov 17;56(8):2941-2962.
3. Wan J, Tang S, Li D, Wang S, Liu C, Abbas H, Vasilakos AV. A manufacturing big data solution for active preventive maintenance. IEEE Trans Ind Inform. 2017 Mar;13(4):2039-2047.
4. Qin J, Liu Y, Grosvenor R. A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP. 2016 Jan 1;52:173-178.
5. Thoben KD, Wiesner SA, Wuest T. "Industrie 4.0" and smart manufacturing–a review of research issues and application examples. Int J Autom Technol. 2017 Mar;11(1):4-16.
6. Schmitt R. Big data analytics for predictive maintenance strategies. IFAC-PapersOnLine. 2015 Dec 1;48(3):223-228.
7. Rüßmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, Harnisch M. Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group. 2015 Apr 9.
8. Davies R. Industry 4.0: Digitalisation for productivity and growth. European Parliament, Research Department. 2015 Sep 30.
9. Kagermann H, Helbig J, Hellinger A, Wahlster W. Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion. 2013 Apr.
10. Rajput S, Singh SP. Industry 4.0–challenges to implement cyber-physical system in digitized manufacturing. Procedia CIRP. 2018 Jan 1;67:620-625.
11. Shrouf F, Ordieres-Meré J, García-Sánchez A, Ortega-Mier M. Optimizing the production scheduling of a single machine to minimize total energy consumption costs. J Clean Prod. 2014 Oct 1;67:197-207.
12. Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara SR, Reinhart G, Sauer O, Schuh G, Sihn W, Ueda K. Cyber-physical systems in manufacturing. CIRP Ann. 2016 Jan 1;65(2):621-641.
13. Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of Industry 4.0: a review. Engineering. 2017 Mar 1;3(5):616-630.
14. Bibby L, Dehe B. Defining and assessing industry 4.0 maturity levels–case of the defence sector. Prod Plan Control. 2018 May 4;29(12):1030-1043.
15. Wang K, Yu Y, Shen Y, Sun H. Big data analytics for systemic risk management in smart supply chains. J SystSciSyst Eng. 2018 Dec;27(6):715-736.
16. Heng S. Industry 4.0: Upgrading the manufacturing sector. Deutsche Bank Research. 2014 Nov 20.
17. Geissbauer R, Vedso J, Schrauf S. Industry 4.0: Building the digital enterprise. PwC Report. 2016.
18. Bagheri B, Yang S, Kao HA, Lee J. Cyber-physical systems architecture with cloud-based big data analytics for smart manufacturing. Manufacturing letters. 2015 Jan 1;3:18-23.
19. Gilchrist A. Industry 4.0: The industrial internet of things. Springer; 2016.
20. Ehret M, Wirtz J. Unlocking value from machines: business models and the industrial internet of things. J Mark Manag. 2017 Feb 7;33(1-2):111-130.
21. Schlechtendahl J, Keinert M, Kretschmer F, Lechler A, Verl A. Making existing production systems Industry 4.0-ready. Prod Eng. 2015 Feb;9(1):143-148.
22. Kolberg D, Zühlke D. Lean automation enabled by Industry 4.0 technologies. IFAC-PapersOnLine. 2015 Dec 1;48(3):1870-1875.
23. Yoon J, Pearlson K, Garrison G, McMullen J, Kozlowski S. Data analytics in the context of data warehousing: a tutorial. ACM J Data Inf Qual. 2018 Mar 8;9(1):1-7.
24. Ferretti M, Schiavone F, Werth D, Monaca MA. Industrial smart working: how to generate digital innovation in operations. J Bus Res. 2020 May 1;112:373-384.
25. Mittal S, Khan MA, Romero D, Wuest T. A critical review of smart manufacturing & Industry 4.0 maturity models: implications for small and medium-sized enterprises (SMEs). J Manuf Syst. 2018 Oct 1;49:194-214.
26. Smit J, Kreutzer S, Moeller C, Carlberg M. Industry 4.0: Study for the ITRE Committee. European Parliament, Policy Department. 2016 Feb 23.
27. Chowdary KP, Chandra DU, Mahesh N, Reddy TM, Gopaiah KV. Enhancement of dissolution rate and formulation development of pioglitazone-a BCS class II drug. J. Pharm. Res. 2011 Nov;4:3862-3.
28. Kagermann H. Change through digitization—Value creation in the age of Industry 4.0. InManagement of permanent change 2015 (pp. 23-45). Springer, Cham.
29. Shrouf F, Ordieres JM, Miragliotta G. Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. IEEE Trans Ind Inform. 2014 Dec;10(4):362-370.
30. Bai C, Dallasega P, Orzes G, Sarkis J. Industry 4.0 technologies assessment: A sustainability perspective. Int J Prod Econ. 2020 Nov 1;229:107776.
31. Zawadzki P, Żywicki K. Smart product design and production control for effective mass customization in the Industry 4.0 concept. Manag Prod Eng Rev. 2016 Dec 1;7(3):105-112.
32. Manda, P., Popescu, C., Juluri, A. et al. Micronized Zaleplon Delivery via Orodispersible Film and Orodispersible Tablets. AAPS PharmSciTech 19, 1358–1366 (2018).
33. Lakshmi Narasimha Rao, K., Praneeth Rao, K. Development and Validation of a Stability-Indicating LC Method for Determination of Bexarotene in Softgel Dosage Formulation. Chromatographia 80, 1211–1224 (2017).
34. Kakulamarri PR, Alikatte KL, Mateti UV (2016) Transdermal Iontophoresis of Non-Polar Drugs: A Mini Review. J Pharm Drug Deliv Res 5:3
35. Kakullamarri PR, Rao KLN (2017) Enhanced Bioavailability and Anticancer Activity of Vitamin Analogs. J BioequivAvailab 9: 439-441.
36. Kumar KR, Nagaraju GV, Subrahmanyam SN, Nagarani K, Shareef S, Tennygilphin M, Namballa M. Assessment on Elements Involving the Academic Performance among Pharmacy Students: A Cross-Sectional Observational Study. Int J Cur Res Rev| Vol. 2021 Dec;13(23):141.
37. Porter ME, Heppelmann JE. How smart, connected products are transforming competition. Harv Bus Rev. 2014 Nov;92(11):64-88.
38. Srai JS, Kumar M, Graham G, Phillips W, Tooze J, Ford S, Beecher P, Raj B, Gregory M, Tiwari MK, Ravi B, Neely A. Distributed manufacturing: scope, challenges and opportunities. Int J Prod Res. 2016 Nov 17;54(23):6917-6935.
39. Ray Y, Choudhary AK, Kumar V, Tiwari MK. Big data analytics for sustainability of manufacturing operations: An Industry 4.0 perspective. J Clean Prod. 2020 Dec 10;259:120776.
40. Modgil S, Gupta S, Sinha VK, Kansal M. Big data in lean six sigma: A review and further research directions. Int J Prod Res. 2020 Sep 30;58(18):5609-5630.
41. Kusiak A. Smart manufacturing. Int J Prod Res. 2018 Nov 17;56(1-2):508-517.
42. Lu Y. Industry 4.0: A survey on technologies, applications and open research issues. J Ind InfIntegr. 2017 Jun 1;6:1-10.
43. Turner RM, Bernard CD. Machine learning for analytics in industry 4.0: A scalable architecture for smart manufacturing. J Manuf Syst. 2020 Apr 1;54:75-84.
44. Sousa MJ, Rocha A. Digital learning: Developing skills for digital transformation of organizations. Future Internet. 2019 Mar 27;11(3):73.
45. Lu Y, Xu X. Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robot ComputIntegr Manuf. 2019 Jun 1;57:92-102.
46. Hermann M, Pentek T, Otto B. Design principles for industrie 4.0 scenarios: A literature review. Technol InnovManag Rev. 2016 Jul 1;6(7):38-43.
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Soni, S. J., & Patel, A. N. (2024). Digital transformation and industry 4.0 in pharma manufacturing: the role of iot, ai, and big data. Journal of Integral Sciences, 7(4), 1-8. https://doi.org/10.37022/jis.v7i4.92
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