Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3347
Title: Smart Pharmaceutical Manufacturing: Ensuring End-to-End Traceability and Data Integrity in Medicine Production
Authors: Leal, Fátima
Chis, Adriana E.
Caton, Simon
González–Vélez, Horacio
García–Gómez, Juan M.
Keywords: ALCOA
Blockchain
Data anaytics
Data quality
Intelligent agents
Smart contracts
Issue Date: 2021
Publisher: Elsevier
Citation: Leal, F., Chis, A. E., Caton, S., González–Vélez, H., García–Gómez, J. M., et al. (2021). Smart Pharmaceutical Manufacturing: Ensuring End-to-End Traceability and Data Integrity in Medicine Production. Big Data Research, 24, 1-12. DOI: https://doi.org/10.1016/j.bdr.2020.100172. Disponível no Repositório UPT, http://hdl.handle.net/11328/3347
Abstract: Production lines in pharmaceutical manufacturing generate numerous heterogeneous data sets from various embedded systems which control the multiple processes of medicine production. Such data sets should arguably ensure end-to-end traceability and data integrity in order to release a medicine batch, which is uniquely identified and tracked by its batch number/code. Consequently, auditable computerised systems are crucial on pharmaceutical production lines, since the industry is becoming increasingly regulated for product quality and patient health purposes. This paper describes the EU- funded SPuMoNI project, which aims to ensure the quality of large amounts of data produced by computerised production systems in representative pharmaceutical environments. Our initial results include significant progress in: (i) end-to-end verification taking advantage of blockchain properties and smart contracts to ensure data authenticity, transparency, and immutability; (ii) data quality assessment models to identify data behavioural patterns that can violate industry practices and/or international regulations; and (iii) intelligent agents to collect and manipulate data as well as perform smart decisions. By analysing multiple sensors in medicine production lines, manufacturing work centres, and quality control laboratories, our approach has been initially evaluated using representative industry- grade pharmaceutical manufacturing data sets generated at an IT environment with regulated processes inspected by regulatory and government agencies.
URI: http://hdl.handle.net/11328/3347
ISSN: 2214-5796
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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