A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain
Abstract
:1. Introduction
- A shorter time to market for the new products (using, for example, 3D printing, ERP, virtual manufacturing, and MES);
- An improved customer responsiveness;
- Enabling a custom mass production without significantly increasing overall production costs;
- More flexible and friendlier working environment (due to robotics, M2M, etc.);
- More efficient use of natural resources and energy.
2. Literature Review
2.1. Internet of Things
2.2. Cyber-Physical Systems
2.3. Machine-to-Machine Communication
2.4. Cloud System and Cloud Computing
2.5. Big Data
2.6. Smart Factory
2.7. Augmented Reality
2.8. Enterprise Resource Planning
- Mobile applications may use ERP data to send messages to the manager and to the machines running in manufacturing;
- Real-time data can be aggregated and optimized for any batch size, analysed, and could allow early indications of fail or process drift, helping the preventive maintenance [72];
- ERP systems could allow for the access of information to suppliers, customers, and other partners to assure the efficiency of online operations and sales and purchasing transparency;
- Optimum material and human resource utilization could be possible;
- Customers may be able to track the status of their orders online.
2.9. Virtual Manufacturing
2.10. Intelligent Robotics
2.11. Blockchain
3. Track and Trace and Serialization Process of a Pharmaceutical Factory
3.1. Serialization Process
- GTIN: The global trade item number (GTIN) can be used to identify a product at any packaging level, e.g., consumer unit, inner pack, case, or pallet. This information provides a common language to uniquely identify the item worldwide for all relevant entities and trading partners;
- Serial number: A numeric or alphanumeric character sequence consisting of up to 20 digits, which must be unique for each GTIN;
- Lot information: A company-specific production that allows identification of the information of the lot produced;
- Expiration date: The expiration date of the product.
3.2. Serialization Manufacturing Technology
4. Web3 Serialization with Blockchain and Non-Fungible Tokens
- -
- Data synchronization: along with data retention, another important duty of the supply chain actors is to synchronize the status of the product with the central authority. This is a relevant activity that increases success in guarding against counterfeiting because the central authority can monitor the responsible actor of the package in any step of the distribution network.
- -
- Ownership: this concept is tightly linked to the responsibility that each actor owns during the handling of the package in each step of the distribution network. In this sense, the responsible actor has to update the status of the product in order to guarantee the integrity of the distribution process.
- -
- Immutability: the data characterizing any package (and its content) must not be manipulated and must describe only the information of traceability that has been appended to the product by the actor of each step of the distribution network.
4.1. Non-Fungible Token as Digital Twin of a Serialized Item
- -
- Uniqueness: NFTs are cryptographic tokens providing a representation of unique assets with individual characteristics used to differentiate them from one another.
- -
- Authenticity: NFTs provide a representation of real-world assets, establishing their authenticity. Authenticity is a key feature of an NFT because it ensures the uniqueness of NFTs.
- -
- Ownership: NFTs are indivisible and can be only owned by the entity that has ownership of it.
- -
- Interoperability: NFTs are stored in a smart contract in the blockchain. Due to the previous features, it becomes possible to use NFTs at different levels of granting access in Web3 applications.
- -
- NFTId: this field corresponds with the GTIN and serial number assigned by the serialization process. This unique ID is added as an index of the NFT in the smart contract. This field cannot be modified;
- -
- NFTSerialized (GTIN, Serial Number, Lot Information, Expiration Date): this field contains the information of the serialized item, contained in the QR code of the label attached to the package. This field cannot be modified;
- -
- NFTProperties (Commercial Name, Active Principle, Company Name, Description): this field cannot be modified;
- -
- Creator Public Address: this field must be the public address of the pharmaceutical factory (corresponding to the GLN number of the company). This field cannot be modified;
- -
- Owner Public Address: this corresponds with the public address of the actor of the distribution network that has ownership in that phase of distribution. This field can change during the process;
- -
- NFT Events (<Id, OwnerAddress, Timestamp, Location, Event, Description>): this field represents the list of events that characterize the NFT lifecycle, where:
- ○
- Id: is an incremental integer (handled by the smart contract);
- ○
- Address: corresponds with the address of the owner of the NFT that performs the action characterizing the event;
- ○
- Timestamp: is the datetime of the event;
- ○
- Location (nullable): is the GPS coordinates where the event occurs;
- ○
- Event: is the type of event that has occurred with the NFT;
- ○
- Description (nullable): is additional information that can be attached to the event.
- -
- ChildOf (nullable): this field corresponds with the NFTId of the parent NFT used to handle the hierarchical packaging. This field can change (during the reconfiguration of the packaging) and is generally assigned after the minting;
- -
- Children (NFTId[]):this field contains the list of children NFTIds that handle the hierarchical packaging (in case the NFT represents a second or third level of packaging). This field can change (during the reconfiguration of the packaging).
- -
- NFTId: <next id of the smart contract>;
- -
- SerializedItem: <3353..34885>, <serial number>, <lot number>, <date of expiration>;
- -
- Creator Public Address: <0xAABB…..PP34>;
- -
- Owner Public Address: <0xAABB…..PP34>;
- -
- NFTEvent: [0, <0xAABB…..PP34>, <hh:mm:ss dd/mm/yyyy>, <GPS Coordinate>, <minting>, <description of the drug packaged>].
- -
- AppendEvent (Event, NFTId)
- -
- UpdateOwner (Public Address, NFTId)
4.2. The Robust Traceability NFT Process
4.3. Architecture and Implementation of the NFT Track and Trace Solution
- (1)
- Each actor of the supply chain (who is going to become an active owner of a package) has to be identified with its VeChain public address (basically, this is not necessary for the final consumers);
- (2)
- Each actor of the supply chain has to keep VET or VeThor tokens in a wallet as they are required to sign the transactions and update the status of the package;
- (3)
- Each actor of the supply chain has to know the public addresses of the companies involved in the distribution network in order to pass the NFT to the correct owner. This last requirement can be easily maintained by using the track and trace decentralized web application, provided that the companies register themselves autonomously (at the moment they retrieve their public address or even later) using the Sync2 plugin.
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Oztemel, E.; Gursev, S. Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. 2020, 31, 127–182. [Google Scholar] [CrossRef]
- Piccarozzi, M.; Aquilani, B.; Gatti, C. Industry 4.0 in management studies: A systematic literature review. Sustainability 2018, 10, 3821. [Google Scholar] [CrossRef] [Green Version]
- Bauernhansel, T.; Krüger, J.; Reinhart, G.; Schuh, G. WGP-Standpunkt Industrie 4.0. WGP Standpkt. 2016, 1, 6–9. [Google Scholar]
- Rojko, A. Industry 4.0 concept: Background and overview. Int. J. Interact. Mob. Technol. 2017, 11, 77–90. [Google Scholar] [CrossRef] [Green Version]
- Meindl, B.; Ayala, N.F.; Mendonça, J.; Frank, A.G. The four smarts of Industry 4.0: Evolution of ten years of research and future perspectives. Technol. Forecast. Soc. Change 2021, 168, 120784. [Google Scholar] [CrossRef]
- Riel, A.; Kreiner, C.; Macher, G.; Messnarz, R. Integrated design for tackling safety and security challenges of smart products and digital manufacturing. CIRP Ann. 2017, 66, 177–180. [Google Scholar] [CrossRef] [Green Version]
- Frank, A.G.; Mendes, G.H.S.; Ayala, N.F.; Ghezzi, A. Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective. Technol. Forecast. Soc. Change 2019, 141, 341–351. [Google Scholar] [CrossRef]
- Mansfield, N.; Naddeo, A.; Frohriep, S.; Vink, P. Integrating and applying models of comfort. Appl. Ergon. 2020, 82, 102917. [Google Scholar] [CrossRef]
- Chiacchio, F.; Compagno, L.; D’Urso, D.; Velardita, L.; Sandner, P. A decentralized application for the traceability process in the pharma industry. Procedia Manuf. 2020, 42, 362–369. [Google Scholar] [CrossRef]
- Bhaskaran, J.; Venkatesh, M.P. Good Storage and Distribution practices for Pharmaceuticals in European Union. J. Pharm. Sci. Res. 2019, 11, 2992–2997. [Google Scholar]
- Corporate Finance. Industry 4.0. Challenges and Solutions for the Digital Transformation and Use of Exponential Technologies; Deloitte AG: Zurich, Switzerland, 2015. [Google Scholar]
- Lu, Y. Industry 4.0: A survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 2017, 6, 1–10. [Google Scholar] [CrossRef]
- Singh, M.; Sachan, S.; Singh, A.; Singh, K.K. Internet of Things in pharma industry: Possibilities and challenges. In Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach; Elsevier: Amsterdam, The Netherlands, 2019; pp. 195–216. ISBN 9780128195932. [Google Scholar]
- Accorsi, R.; Bortolini, M.; Baruffaldi, G.; Pilati, F.; Ferrari, E. Internet-of-things Paradigm in Food Supply Chains Control and Management. Procedia Manuf. 2017, 11, 889–895. [Google Scholar] [CrossRef]
- Maskuriy, R.; Selamat, A.; Ali, K.N.; Maresova, P.; Krejcar, O. Industry 4.0 for the construction industry-How ready is the industry? Appl. Sci. 2019, 9, 2819. [Google Scholar] [CrossRef] [Green Version]
- Li, C.Z.; Hong, J.; Xue, F.; Shen, G.Q.; Xu, X.; Luo, L. SWOT analysis and Internet of Things-enabled platform for prefabrication housing production in Hong Kong. Habitat Int. 2016, 57, 74–87. [Google Scholar] [CrossRef]
- Dave, B.; Kubler, S.; Pikas, E.; Holmström, J.; Singh, V.; Främling, K.; Koskela, L.; Peltokorpi, A. Intelligent products: Shifting the production control logic in construction (with lean and Bim). In Proceedings of the IGLC 23—23rd Annual Conference of the International Group for Lean Construction: Global Knowledge—Global Solutions, Perth, Australia, 29–31 July 2015. [Google Scholar]
- Bottaccioli, L.; Aliberti, A.; Ugliotti, F.; Patti, E.; Osello, A.; Macii, E.; Acquaviva, A. Building Energy Modelling and Monitoring by Integration of IoT Devices and Building Information Models. In Proceedings of the International Computer Software and Applications Conference, Turin, Italy, 4–8 July 2017. [Google Scholar]
- Zhang, Y.; Guo, Z.; Lv, J.; Liu, Y. A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT. IEEE Trans. Ind. Inform. 2018, 14, 4019–4032. [Google Scholar] [CrossRef] [Green Version]
- Giusto, D.; Iera, A.; Morabito, G.; Atzori, L. The Internet of Things: 20th Tyrrhenian Workshop on Digital Communications; Springer: Berlin, Germany, 2010. [Google Scholar]
- Dener, M.; Bostancıoğlu, C. Smart Technologies with Wireless Sensor Networks. Procedia Soc. Behav. Sci. 2015, 195, 1915–1921. [Google Scholar] [CrossRef] [Green Version]
- Lai, I.K.W.; Tam, S.K.T.; Chan, M.F.S. Knowledge cloud system for network collaboration: A case study in medical service industry in China. Expert Syst. Appl. 2012, 39, 12205–12212. [Google Scholar] [CrossRef]
- Craveiro, F.; Duarte, J.P.; Bartolo, H.; Bartolo, P.J. Additive manufacturing as an enabling technology for digital construction: A perspective on Construction 4.0. Autom. Constr. 2019, 103, 251–267. [Google Scholar] [CrossRef]
- Diez-Olivan, A.; Del Ser, J.; Galar, D.; Sierra, B. Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Inf. Fusion 2019, 50, 92–111. [Google Scholar] [CrossRef]
- Han, H.; Trimi, S. Towards a data science platform for improving SME collaboration through Industry 4.0 technologies. Technol. Forecast. Soc. Change 2022, 174, 121242. [Google Scholar] [CrossRef]
- Hasan, M.M.; Popp, J.; Oláh, J. Current landscape and influence of big data on finance. J. Big Data 2020, 7, 21. [Google Scholar] [CrossRef]
- Lekic, M.; Rogic, K.; Boldizsár, A.; Zöldy, M.; Török, Á. Big data in logistics. Period. Polytech. Transp. Eng. 2020, 49, 60–65. [Google Scholar] [CrossRef] [Green Version]
- Tagaytayan, R.; Kelemen, A.; Sik-Lanyi, C. Augmented reality in neurosurgery. Arch. Med. Sci. 2018, 14, 572–578. [Google Scholar] [CrossRef]
- Gouveia, P.F.; Costa, J.; Morgado, P.; Kates, R.; Pinto, D.; Mavioso, C.; Anacleto, J.; Martinho, M.; Lopes, D.S.; Ferreira, A.R.; et al. Breast cancer surgery with augmented reality. Breast 2021, 56, 14–17. [Google Scholar] [CrossRef] [PubMed]
- Munzer, B.W.; Khan, M.M.; Shipman, B.; Mahajan, P. Augmented reality in emergency medicine: A scoping review. J. Med. Internet Res. 2019, 21, e12368. [Google Scholar] [CrossRef]
- Sandu, M.; Scarlat, I.S. Augmented Reality Uses in Interior Design. Inform. Econ. 2018, 22, 5–13. [Google Scholar] [CrossRef]
- Phan, V.T.; Choo, S.Y. Interior Design in Augmented Reality Environment. Int. J. Comput. Appl. 2010, 5, 16–21. [Google Scholar] [CrossRef]
- Patil, C. Interior Design Using Augmented Reality. Int. J. Res. Appl. Sci. Eng. Technol. 2018, 6, 1632–1635. [Google Scholar] [CrossRef]
- So, J.I.; Kim, S.-H. The effects of augmented reality fashion application on pleasure, satisfaction and behavioral intention. Res. J. Costume Cult. 2013, 21, 810–826. [Google Scholar] [CrossRef] [Green Version]
- El-Seoud, M.S.A.; Taj-Eddin, I.A.T.F. An android augmented reality application for retail fashion shopping. Int. J. Interact. Mob. Technol. 2019, 13, 4–19. [Google Scholar] [CrossRef] [Green Version]
- Kim, M.; Cheeyong, K. Augmented reality fashion apparel simulation using a magic mirror. Int. J. Smart Home 2015, 9, 169–178. [Google Scholar] [CrossRef]
- Fattah, A.; Gunawan, A.A.; Taufik, R.B.; Pranoto, H. Effect of the implementation attractive augmented reality for museums visit. ICIC Express Lett. Part B Appl. 2021, 12, 541–548. [Google Scholar] [CrossRef]
- Chen, W.; Shan, Y.; Wu, Y.; Yan, Z.; Li, X. Design and Evaluation of a Distance-Driven User Interface for Asynchronous Collaborative Exhibit Browsing in an Augmented Reality Museum. IEEE Access 2021, 9, 73948–73962. [Google Scholar] [CrossRef]
- Challenor, J.; Ma, M. A review of augmented reality applications for history education and heritage visualisation. Multimodal Technol. Interact. 2019, 3, 39. [Google Scholar] [CrossRef] [Green Version]
- Vidal-Balea, A.; Blanco-Novoa, O.; Fraga-Lamas, P.; Vilar-Montesinos, M.; Fernández-Caramés, T.M. A Collaborative Augmented Reality Application for Training and Assistance during Shipbuilding Assembly Processes. In Proceedings of the 3rd XoveTIC Conference, A Coruña, Spain, 8–9 October 2020. [Google Scholar] [CrossRef]
- Matsuo, K. Demonstration of AR application for sheet metal forming works in shipyard. In Proceedings of the RINA, Royal Institution of Naval Architects—International Conference on Computer Applications in Shipbuilding 2013, ICCAS 2013, London, UK, 24–26 September 2013. [Google Scholar]
- Fraga-Lamas, P.; Fernández-Caramés, T.M.; Blanco-Novoa, Ó.; Vilar-Montesinos, M.A. A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard. IEEE Access 2018, 6, 13358–13375. [Google Scholar] [CrossRef]
- Shirazi, B. Towards a sustainable interoperability in food industry small & medium networked enterprises: Distributed service-oriented enterprise resources planning. J. Clean. Prod. 2018, 181, 109–122. [Google Scholar] [CrossRef]
- Sadrzadehrafiei, S.; Chofreh, A.G.; Hosseini, N.K.; Sulaiman, R. The Benefits of Enterprise Resource Planning (ERP) System Implementation in Dry Food Packaging Industry. Procedia Technol. 2013, 11, 220–226. [Google Scholar] [CrossRef] [Green Version]
- Carboneras, M.C.; Insa, C.M.; Salort, E.V. ERP implementation in the stone industry: Special difficulties and solutions in the production area. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation ETFA, Lisbon, Portugal, 16–19 September 2003; Volume 2. [Google Scholar]
- Ng, A.H.C.; Adolfsson, J.; Sundberg, M.; De Vin, L.J. Virtual manufacturing for press line monitoring and diagnostics. Int. J. Mach. Tools Manuf. 2008, 48, 565–575. [Google Scholar] [CrossRef] [Green Version]
- Yang, C.T.; Liu, J.C.; Chen, S.T.; Huang, K.L. Virtual machine management system based on the power saving algorithm in cloud. J. Netw. Comput. Appl. 2017, 80, 165–180. [Google Scholar] [CrossRef]
- Mujber, T.S.; Szecsi, T.; Hashmi, M.S.J. Virtual reality applications in manufacturing process simulation. J. Mater. Process. Technol. 2004, 155, 1834–1838. [Google Scholar] [CrossRef]
- Al-Ahmari, A.M.; Abidi, M.H.; Ahmad, A.; Darmoul, S. Development of a virtual manufacturing assembly simulation system. Adv. Mech. Eng. 2016, 8, 1–13. [Google Scholar] [CrossRef] [Green Version]
- Peruzzini, M.; Grandi, F.; Cavallaro, S.; Pellicciari, M. Using virtual manufacturing to design human-centric factories: An industrial case. Int. J. Adv. Manuf. Technol. 2021, 115, 873–887. [Google Scholar] [CrossRef]
- Sequenzia, G.; Fatuzzo, G.; Oliveri, S.M. A computer-based method to reproduce and analyse ancient series-produced moulded artefacts. Digit. Appl. Archaeol. Cult. Herit. 2021, 20, e00174. [Google Scholar] [CrossRef]
- Coakley, M.F.; Hurt, D.E.; Weber, N.; Mtingwa, M.; Fincher, E.C.; Alekseyev, V.; Chen, D.T.; Yun, A.; Gizaw, M.; Swan, J.; et al. The NIH 3D print exchange: A public resource for bioscientific and biomedical 3D prints. 3D Print. Addit. Manuf. 2014, 1, 137–140. [Google Scholar] [CrossRef] [PubMed]
- Kwarcinski, J.; Boughton, P.; van Gelder, J.; Damodaran, O.; Doolan, A.; Ruys, A. Clinical evaluation of rapid 3D print-formed implants for surgical reconstruction of large cranial defects. ANZ J. Surg. 2021, 91, 1226–1232. [Google Scholar] [CrossRef] [PubMed]
- Mousavi, M.; Aziz, F.A.; Ismail, N. Virtual reality adoption capability in Malaysian automotive manufacturing industry. Sci. Res. Essays 2012, 7, 158–164. [Google Scholar] [CrossRef]
- Villani, V.; Pini, F.; Leali, F.; Secchi, C. Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics 2018, 55, 248–266. [Google Scholar] [CrossRef]
- Covaciu, F.; Pisla, A.; Iordan, A.E. Development of a virtual reality simulator for an intelligent robotic system used in ankle rehabilitation. Sensors 2021, 21, 1537. [Google Scholar] [CrossRef]
- Thai, M.T.; Phan, P.T.; Hoang, T.T.; Wong, S.; Lovell, N.H.; Do, T.N. Advanced Intelligent Systems for Surgical Robotics. Adv. Intell. Syst. 2020, 2, 1900138. [Google Scholar] [CrossRef]
- Musamih, A.; Salah, K.; Jayaraman, R.; Arshad, J.; Debe, M.; Al-Hammadi, Y.; Ellahham, S. A blockchain-based approach for drug traceability in healthcare supply chain. IEEE Access 2021, 9, 9728–9743. [Google Scholar] [CrossRef]
- Leloglu, E. A Review of Security Concerns in Internet of Things. J. Comput. Commun. 2017, 5, 121–136. [Google Scholar] [CrossRef] [Green Version]
- Biral, A.; Centenaro, M.; Zanella, A.; Vangelista, L.; Zorzi, M. The challenges of M2M massive access in wireless cellular networks. Digit. Commun. Netw. 2015, 1, 1–19. [Google Scholar] [CrossRef]
- Nuñez, D.; Fernández, G.; Luna, J. Cloud system. Procedia Comput. Eng. 2017, 62, 149–164. [Google Scholar]
- Chen, T.C.T. Cloud intelligence in manufacturing. J. Intell. Manuf. 2017, 28, 1057–1059. [Google Scholar] [CrossRef] [Green Version]
- Bellini, P.; Bruno, I.; Cenni, D.; Nesi, P. Managing cloud via Smart Cloud Engine and Knowledge Base. Future Gener. Comput. Syst. 2018, 78, 142–154. [Google Scholar] [CrossRef]
- Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ji, S.; Ren, F.; Dubey, R. Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res. 2017, 70, 356–365. [Google Scholar] [CrossRef] [Green Version]
- Alharthi, A.; Krotov, V.; Bowman, M. Adressing bariers to big data. Bus. Horiz. 2017, 60, 285–292. [Google Scholar] [CrossRef]
- Hazen, B.T.; Boone, C.A.; Ezell, J.D.; Jones-Farmer, L.A. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. Int. J. Prod. Econ. 2014, 154, 72–80. [Google Scholar] [CrossRef]
- Khan, A.; Turowski, K. A perspective on industry 4.0: From challenges to opportunities in production systems. In Proceedings of the IoTBD 2016—International Conference on Internet of Things and Big Data, Rome, Italy, 23–25 April 2016; pp. 441–448. [Google Scholar] [CrossRef]
- Zielinski, E.; Schulz-Zander, J.; Zimmermann, M.; Schellenberger, C.; Ramirez, A.; Zeiger, F.; Mormul, M.; Hetzelt, F.; Beierle, F.; Klaus, H.; et al. Secure real-time communication and computing infrastructure for industry 4.0—Challenges and opportunities. In Proceedings of the 2019 International Conference on Networked Systems, NetSys 2019, Munich, Germany, 18–21 March 2019. [Google Scholar]
- Berryman, D.R. Augmented Reality: A Review. Med. Ref. Serv. Q. 2012, 31, 212–218. [Google Scholar] [CrossRef]
- Alkoc, E.; Erbatur, F. Productivity improvement in concreting operations through simulation models. Build. Res. Inf. 1997, 25, 82–91. [Google Scholar] [CrossRef]
- Liao, T. Augmented or admented reality? The influence of marketing on augmented reality technologies. Inf. Commun. Soc. 2015, 18, 310–326. [Google Scholar] [CrossRef]
- Khan, A.; Turowski, K. A survey of current challenges in manufacturing industry and preparation for industry 4.0. In Advances in Intelligent Systems and Computing; Springer: Cham, Switzerland, 2016; Volume 450, pp. 15–26. [Google Scholar]
- Li, T.; Wu, J.; Cao, Y. Capacity analysis of an iron foundry fettling-shop, using virtual manufacturing technology. Int. J. Cast Met. Res. 2003, 16, 329–332. [Google Scholar]
- Peng, Q.; Chung, C.; Yu, C.; Luan, T. A networked virtual manufacturing system for SMEs. Int. J. Comput. Integr. Manuf. 2007, 20, 71–79. [Google Scholar] [CrossRef]
- Hitchcock, M.F.; Baker, A.D.; Brink, J.R. Role of hybrid systems theory in virtual manufacturing. In Proceedings of the IEEE/IFAC Joint Symposium on Computer-Aided, Tucson, AZ, USA, 7–9 March 1994; pp. 345–350. [Google Scholar]
- Shilpi, S.; Ahad, M. Blockchain Technology and Smart Cities—A Review. EAI Endorsed Trans. Smart Cities 2020, 4, e2. [Google Scholar] [CrossRef]
- Chiacchio, F.; D’Urso, D.; Compagno, L.; Chiarenza, M.; Velardita, L. Towards a Blockchain Based Traceability Process: A Case Study from Pharma Industry. In IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2019. [Google Scholar]
- Felix, T.; Jordan, J.B.; Akers, C.; Patel, B.; Drago, D. Current state of biologic pharmacovigilance in the European Union: Improvements are needed. Expert Opin. Drug Saf. 2019, 18, 231–240. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marsh, J.L.; Eyers, D.R. Increasing production efficiency through electronic batch record systems: A case study. Smart Innov. Syst. Technol. 2016, 52, 261–269. [Google Scholar] [CrossRef]
- Syafiraliany, L.; Lubis, M.; Witjaksono, R.W. Analysis of Critical Success Factors from ERP System Implementation in Pharmaceutical Fields by Information System Success Model. In Proceedings of the 2019 4th International Conference on Informatics and Computing ICIC, Semarang, Indonesia, 16–17 October 2019; pp. 1–5. [Google Scholar] [CrossRef]
- Zhong, R.Y.; Xu, X.; Klotz, E.; Newman, S.T. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 2017, 3, 616–630. [Google Scholar] [CrossRef]
- Sylim, P.; Liu, F.; Marcelo, A.; Fontelo, P. Blockchain technology for detecting falsified and substandard drugs in distribution: Pharmaceutical supply chain intervention. JMIR Res. Protoc. 2018, 7, e10163. [Google Scholar] [CrossRef]
- Wang, Q.; Li, R.; Wang, Q.; Chen, S. Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges. arXiv 2021, arXiv:2105.07447. [Google Scholar]
- Treiblmaier, H. Beyond blockchain: How tokens trigger the internet of value and what marketing researchers need to know about them. J. Mark. Commun. 2021, 1–13. [Google Scholar] [CrossRef]
- Lau, W.F.; Liu, D.Y.W.; Au, M.H. Blockchain-Based Supply Chain System for Traceability, Regulation and Anti-Counterfeiting. In Proceedings of the 2021 IEEE International Conference on Blockchain (Blockchain), Melbourne, Australia, 6–8 December 2021; pp. 82–89. [Google Scholar] [CrossRef]
- VeChain Development Plan and Whitepaper. 2020. Available online: https://www.vechain.org/whitepaper/#bit_v48i3 (accessed on 20 March 2022).
Technology | Strength | Disadvantages | Industry Sector | Paper |
---|---|---|---|---|
Internet of things | Process control, increase yield, maximize productivity, enhance information flow | Security issues, securitypolicy | Pharmaceutical industry, food industry, construction industry | [13] [14] [15,16,17,18] |
Cyber-physical system | Easier access to information, preventive maintenance, decision making, optimization routines | Security issues | Logistics | [19,20] |
Machine-to-machine communication | Easy monitoring of resources and production lines, improve resources reusing, reduces operational costs, automate the decision process, favour a human free manufacturing environment | Smart agriculture, smart grid, smart environment control, | [21] | |
Cloud system | Reduces costs, eliminates infrastructure complexity, extends work area, protects data, provides holistic access to information, increase speed and quality of production | Data integrity and availability | Medical service industry | [22] |
Cloud computing | Allows real-time collaboration from different locations, enhance decision-making, and ensure project deliverability | Construction industry | [15,23] | |
Big data | Provides business value through better strategic and operational decisions | Privacy law, perception of risk and unreliability of open data movement | SME Finance Logistics | [24,25] [26] [27] |
Augmented reality | Aids the design phase of products and production systems, reduce time to market and cost | Social impact | Medicine Interior design Fashion retails Museums Shipyard manufacturing system | [28,29,30] [31,32,33] [34,35,36] [37,38,39] [40,41,42] |
Enterprise resource planning | Improves process control, early indication of fails, communication transparency, optimize material and human resource utilization | Interoperability | Food industry Stone industry | [43,44] [45] |
Virtual manufacturing | Shorter lead time, reduced cost, more efficient and improved quality with clean and green process | Metal forming Manufacturing industry | [46] [47,48,49,50] | |
3D printing | Design and print parts | Archaeology Medical service industry Mechanical industry | [51] [52,53] [3,54] | |
Intelligent robotics | Reduces human force, inspection of dangerous process | Industrial accidents, human unemployment | Manufacturing industry Medical service industry | [55] [56,57] |
Blockchain | Security, traceability, immutability, accessibility of data provenance | No interoperability, data privacy policy, immutability | Pharmaceutical supply chain | [58] |
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Chiacchio, F.; D’Urso, D.; Oliveri, L.M.; Spitaleri, A.; Spampinato, C.; Giordano, D. A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain. Appl. Sci. 2022, 12, 4019. https://doi.org/10.3390/app12084019
Chiacchio F, D’Urso D, Oliveri LM, Spitaleri A, Spampinato C, Giordano D. A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain. Applied Sciences. 2022; 12(8):4019. https://doi.org/10.3390/app12084019
Chicago/Turabian StyleChiacchio, Ferdinando, Diego D’Urso, Ludovica Maria Oliveri, Alessia Spitaleri, Concetto Spampinato, and Daniela Giordano. 2022. "A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain" Applied Sciences 12, no. 8: 4019. https://doi.org/10.3390/app12084019