TCMQMS System: A digital twin and blockchain-based platform for tracing the whole process of Chinese medicine quality information
DOI:
https://doi.org/10.5327/fst.122022Palavras-chave:
Traditional Chinese medicine, pharmacy, digital twin, block chain, Internet of Things, Chinese medicine supply chain, Tracking and tracingResumo
Traditional Chinese medicine is an important medicine and health food in China. In the process of making Chinese medicines, the main active ingredients of the medicines are highly dependent on the production environment due to the special physical characteristics of the medicines themselves. Any slight change in the environment can lead to changes in the active ingredients as well as the final efficacy, even affecting the patient treatment cycle, and this characteristic is not conducive to quality control by national drug quality regulators. In order to enhance the quality management of TCM, a blockchain and digital twin based whole process monitoring system for TCM quality is proposed: Traditional Chinese Medicine Quality Monitoring System (TCMQMS), in order to achieve a whole process, full scope and highly transparent TCM quality monitoring model for TCM production from the source of cultivation to the final patient. And. A blockchain platform based on Fabric blockchain data development platform as well as Sia distributed data storage was designed.
With the help of Sia distributed storage technology, the amount of data storage is significantly compressed, which achieves the purpose of unifying the huge information data flow into the TCMQMS system and quickly building the system platform using the Fabric platform. In addition, in order to ensure that data is read and written in real time during the manufacturing and transportation of Chinese medicine, environmental sensors, such as temperature and humidity sensors, are placed in the Chinese medicine processing plant and in the transport sector. The environmental sensors, such as temperature and humidity sensors, are placed inside the Chinese medicine processing plants and transport vehicles. The environmental data is combined with the production data recorded by the TCMQMS system and sent to the digital twin TCM manufacturing simulation environment in real time, and through the simulation of the TCM manufacturing environment, the TCM quality environment parameters are continuously iteratively updated to be more suitable for production. This is coupled with real-time linkage with environmental sensors to achieve real-time adjustment of environmental parameters. In addition, the TCMQMS system records the entire process data, giving each data block a unique and tamper-proof hash value and CA certificate, so that when there is a possible quality problem, the hash value and CA certificate will be used to quickly trace the person responsible and the details of the environment in which the TCM was produced in real time. Finally, we present a real-life example of the use of the TCMQMS system to validate our proposed approach.
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Referências
Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future generation computer systems, 88, 173-190.DOI: 10.1016/j.future.2018.05.046.
Lopes, M. R., Costigliola, A., Pinto, R., Vieira, S., & Sousa, J. M. (2020). Pharmaceutical quality control laboratory digital twin–A novel governance model for resource planning and scheduling. International Journal of Production Research, 58(21), 6553-6567. DOI: 10.1080/00207543.2019.1683250. [3] Schöner, M. M., Kourouklis, D., Sandner, P., Gonzalez, E., & Förster, J. (2017). Blockchain technology in the pharmaceutical industry. Frankfurt School Blockchain Center: Frankfurt, Germany.
Safkhani, M., Rostampour, S., Bendavid, Y., & Bagheri, N. (2020). IoT in medical & pharmaceutical: Designing lightweight RFID security protocols for ensuring supply chain integrity. Computer Networks, 181, 107558. DOI: 10.1016/j.comnet.2020.107558
Coito, T., Martins, M. S., Firme, B., Figueiredo, J., Vieira, S. M., & Sousa, J. M. (2022). Assessing the impact of automation in pharmaceutical quality control labs using a digital twin. Journal of Manufacturing Systems, 62, 270-285. DOI: 10.1016/j.jmsy.2021.11.014
Khlystova, O., Kalyuzhnova, Y., & Belitski, M. (2022). The impact of the COVID-19 pandemic on the creative industries: A literature review and future research agenda. Journal of Business Research, 139, 1192-1210.DOI:10.1002/kpm.1660
Zhang, D. H., Wu, K. L., Zhang, X., Deng, S. Q., & Peng, B. (2020). In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. Journal of integrative medicine, 18(2), 152-158.DOI: 10.1016/j.joim.2020.02.005
Shah, Nilay (2004) Shah N. Pharmaceutical supply chains: key issues and strategies for optimisation[J]. Computers & chemical engineering, 2004, 28(6-7): 929-941.DOI: 10.1016/j.compchemeng.2003.09.022
Rossetti, C. L., Handfield, R., & Dooley, K. J. (2011). Forces, trends, and decisions in pharmaceutical supply chain management. International Journal of Physical Distribution & Logistics Management.DOI:10.1108/09600031111147835
Yu, X., Li, C., Shi, Y., & Yu, M. (2010). Pharmaceutical supply chain in China: current issues and implications for health system reform. Health policy, 97(1), 8-15.DOI: 10.1016/j.healthpol.2010.02.010
Abdallah, A. A. (2013). Global pharmaceutical supply chain: A quality perspective. International Journal of Business and Management, 8(17), 62.DOI: 10.5539/ijbm. v8n17p62
Tat, R., & Heydari, J. (2021). Avoiding medicine wastes: Introducing a sustainable approach in the pharmaceutical supply chain. Journal of Cleaner Production, 320, 128698.DOI: 10.1016/j.jclepro.2021.128698
Papert, M., Rimpler, P., & Pflaum, A. (2016). Enhancing supply chain visibility in a pharmaceutical supply chain: Solutions based on automatic identification technology. International Journal of Physical Distribution & Logistics Management. DOI:10.1108/IJPDLM-06-2016-0151
Laurie M. Clotilde, Anthony Zografos, and Molly A. (2017), Trace the food, not the packaging! Food Science and Technology, 31: 40-43. DOI:10.1002/fsat.3104_10.x
Nofer, M., Gomber, P., Hinz, O., & Schiereck, D. (2017). Blockchain. Business & Information Systems Engineering, 59(3), 183-187. DOI:10.1007/s12599-017-0467-3
Mettler, M. (2016, September). Blockchain technology in healthcare: The revolution starts here. In 2016 IEEE 18th international conference on e-health networking, applications and services (Healthcom) (pp. 1-3). IEEE. DOI:109/HealthCom.2016.7749510
Chien, W., de Jesus, J., Taylor, B., Dods, V., Alekseyev, L., Shoda, D., & Shieh, P. B. (2020). The last mile: DSCSA solution through blockchain technology: drug tracking, tracing, and verification at the last mile of the pharmaceutical supply chain with BRUINchain. Blockchain in Healthcare Today.DOI:0953/bhty. v3.134
Huang, Y., Wu, J., & Long, C. (2018, July). Drugledger: A practical blockchain system for drug traceability and regulation. In 2018 IEEE international conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData) (pp. 1137-1144). IEEE. DOI:109/Cybermatics_2018.2018.00206
Tom Hollands, Wayne Martindale, Mark Swainson and John G. Keogh (2018). Blockchain or bust for the food industry? Food Science and Technology, 32: 40-45. DOI:10.1002/fsat.3204_12.x
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135.
DOI:10.1080/00207543.2018.1533261
Tom Hollands, Wayne Martindale, Mark Swainson and John G. Keogh (2021). Blockchain: a framework for membership and access. Food Science and Technology, 35: 50-53. DOI:10.1002/fsat.3501_13.x
Rejeb, A., Keogh, J. G., & Treiblmaier, H. (2019). Leveraging the internet of things and blockchain technology in supply chain management. Future Internet, 11(7), 161. DOI:10.3390/fi11070161
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022.DOI: 10.1016/j.ifacol.2018.08.474.
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653-661. DOI: 10.1016/j.eng.2019.01.014.
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on industrial informatics, 15(4), 2405-2415. DOI:10.1109/TII.2018.2873186.
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653-661. DOI: 10.1016/j.eng.2019.01.014.
Barykin, S. Y., Bochkarev, A. A., Dobronravin, E., & Sergeev, S. M. (2021). The place and role of digital twin in supply chain management. Academy of Strategic Management Journal, 20, 1-19.
Raza, M., Kumar, P. M., Hung, D. V., Davis, W., Nguyen, H., & Trestian, R. (2020, February). A digital twin framework for industry 4.0 enabling next-gen manufacturing. In 2020 9th international conference on industrial technology and management (ICITM) (pp. 73-77). IEEE. DOI:10.1109/ICITM48982.2020.9080395
Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Advanced Engineering Informatics, 47, 101225.DOI: 10.1016/j.aei.2020.101225
