Digital Transformation Dissertations

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https://hdl.handle.net/20.500.14136/124

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    The Knowledge, Attitude And Practice Of Pharmacists Towards the Use And Development Of Robotics Pharmacies In Hospital Pharmacies Settings In India
    (2024-05) Biju Kumar, Devika
    This study explores the knowledge, attitudes, and behaviours of Indian pharmacists on the adoption and usage of robotic pharmacies in hospital settings. It aims to determine the advantages, disadvantages, and accuracy of robotic pharmacy systems in terms of medication administration and patient safety. Using a pragmatic research mindset and a deductive technique, the study gathered qualitative and quantitative data from 85 pharmacists via an online survey. The findings show that, while there is considerable interest and positive feedback regarding robotic pharmacies, significant limitations such as financial restrictions, regulatory compliance, and labor adaptability prevent widespread implementation. The research's results show that there is a positive correlation between the perceived benefits of robotic pharmacy and their current and future use in hospitals. Pharmacists understand whether these technologies may reduce drug errors, increase operational efficiency, and improve patient safety. However, concerns regarding job displacement, machine dependability, and managing complex medication remain. The study emphasizes the importance of extensive training programs, cost-benefit analyses, and staff transition management measures in order to optimize the benefits of robotic pharmacies. The study's hypotheses testing indicated beneficial connections between knowledge, attitudes, and the use of robotic systems, emphasizing the relevance of education and training in fostering acceptance and successful implementation. Despite the limitations, the promise for robotic pharmacies to transform healthcare delivery by enhancing medication management is clear. Future advances in artificial intelligence and machine learning are projected to improve these systems capabilities.To summarize, while robotic pharmacies provide considerable benefits, overcoming the previously mentioned challenges through strategic planning, regulatory compliance, and education is critical for their successful integration into Indian hospital pharmacies. The study offers useful information for stakeholders and policymakers seeking to develop best practices and promote the effective implementation of robotic pharmacies, eventually increasing patient care and operational efficiency.
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    Utilizing Artificial Intelligence (Machine learning algorithms) for Process Optimization in Pharmaceutical Manufacturing Processes
    (2024-05) Adelodun Johnson, Arafat
    Digital transformation has introduced smart manufacturing, artificial intelligence, IoT, and advanced computerization to the pharmaceutical industry to drive Process Optimization. This plays a crucial role in the pharmaceutical industry as the complexity of manufacturing processes presents multidimensionality of product design, process development and product manufacturing data. While statistical techniques such as multivariate data analysis has made significant contribution to the pharmaceutical sector, its application can only be subjected to one process at a time in terms of providing support for quality-by-design based development and manufacturing of pharmaceuticals, limiting the enormous potential for automation. By leveraging machine learning, manufacturing processes can be streamlined to mitigate challenges associated with variability and complexity through predictive analysis of the large volume of data generated by PAT. This paper aims to provide a critical overview of how ML can be applied during various stages of the manufacturing process through a comprehensive analysis of existing literature from peer-reviewed journals, books, academic papers with illustrative examples applied in the context of pharmaceutical formulation development and related technologies as well as future trends. The study also aims to gain objective insights regarding the use of ML in pharmaceutical dosage manufacturing by exploring the opinions and perspectives of professionals actively involved in pharmaceutical manufacturing processes. With an estimated sample size of 90 participants, the study utilised an online survey-questionnaire that was administered to process managers, operators, industry experts, quality assurance and control officers to gather quantitative data in Ireland. An overall response rate of 69% was obtained and their opinion was evaluated in line with reviewed literature. The outcome of the study demonstrated the potential benefits that ML had to offer the pharmaceutical industry, the current applications, the limitations, and regulatory issues surrounding the adoption of ML in pharmaceutical manufacturing from both primary and secondary data sources.
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    The impact of Smart Manufacturing on the Digital Maturity of Medical Device Industry
    (2024-05) Dattaram Solkar, Ankita
    The study explores the impact of smart manufacturing on the digital maturity of the medical device industry. The research aims to assess smart manufacturing technologies contributions to digital maturity, focusing on technological advancements, connectivity features, and overall industry improvements. The literature review highlights the importance of operational efficiency, quality improvement, product customization, and regulatory compliance as primary drivers for adopting digital technologies in the medical device industry. Additionally, the study emphasizes the significance of a robust data strategy, seamless digital integration, and smart manufacturing practices. The study adopts a mixed-methods approach, combining qualitative and quantitative data to understand the impact of smart manufacturing on digital maturity, involving a comprehensive literature review and a survey of industry professionals from India and Ireland. Findings suggest that digital maturity positively impacts operational efficiency and product quality, with potential benefits including enhanced quality, efficiency, and innovation through advanced technologies like AI, IoT, and big data analytics. However, challenges such as data security and system interoperability need to be addressed. The research recommends a supportive organizational culture, leadership commitment, and continuous employee training to drive digital maturity, ultimately fostering innovation and competitiveness.
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    The Impact of AI (Machine Learning and Automation) on Biopharmaceutical Manufacturing Industry
    (2024-05) Sayal, Ruchi
    This research investigates the transformative effects of artificial intelligence (AI), machine learning (ML), and automation on the biopharmaceutical manufacturing industry. The study provides a comprehensive analysis of how these advanced technologies are revolutionizing manufacturing processes. Through an extensive literature review and detailed survey analysis, the research examines how these technologies address challenges such as data quality and complex biological systems, emphasizing their role in overcoming regulatory hurdles and improving manufacturing efficiency. The study explores key trends and drivers behind the escalating adoption of AI, including the need for process optimization, advancements in drug discovery, and enhanced quality control. Furthermore, the research assesses the impact of AI on traditional biopharmaceutical manufacturing models. It illustrates how AI disrupts conventional processes by enabling real-time issue identification, enhancing quality control, and boosting productivity. The introduction of new methodologies such as personalized medication production, AI-powered robotics, and AI-assisted drug discovery showcases the transformative potential of these technologies. In conclusion, the study reveals that AI adoption in the biopharmaceutical industry is rapidly advancing, driven by its transformative potential in enhancing efficiency, innovation, and competitiveness. Addressing challenges and ensuring responsible adoption will be pivotal in realizing the full benefits of AI-enabled technologies in biopharmaceutical manufacturing. The research provides valuable insights for industry stakeholders, guiding strategic decision-making and fostering a more informed approach to integrating AI technologies, ultimately contributing to the growth and advancement of the biopharmaceutical sector.
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    Ensuring Product Quality in Irish Biologic and Biopharmaceutical Logistics through IoT-enabled Cold Chain Monitoring
    (2024-05) Manikantan Nair, Nisha
    The fourth industrial revolution along with the advancement in digital technologies have revolutionized the realm of biologics and biopharmaceutical logistics. This dissertation embarks on a journey to explore the integration of Internet of things (IoT) enabled technologies in cold chain monitoring within the Irish biologic and biopharmaceutical logistics sector. The study focuses on ensuring the quality and safety of temperature-sensitive biologics and biopharmaceutical products like gene therapies, monoclonal antibodies, vaccines, diagnostic kits and reagents during storage and transportation till it reaches the end customer. The study is relevant especially for a country like Ireland renowned for being one of the leading global hubs in biopharmaceuticals, medical device technology and digital health technology. The biologic and biopharmaceutical logistics industry of Ireland rely heavily on efficient and reliable cold chain monitoring to ensure the quality, safety, and efficacy of temperature sensitive medications, vaccines, and biologics. The ever-increasing demand for vaccines, gene therapies, monoclonal antibodies, and biomedical products like diagnostic reagents and medical laboratory supplies post COVID-19 pandemic has made it important to maintain the optimal storage conditions throughout their transit within the supply chain. Industry 4.0 technologies like the Internet of things (IoT) can be a game changer in the cold chain logistics through its capabilities in offering real-time monitoring of temperature and other environmental factors like humidity, pressure, etc. Through time-monitoring, anomalies in temperature and other environmental factors can be detected immediately and immediate measures can be taken to prevent any adversities, thus ensuring the safety and efficacy of the product. This research further aims to emphasise the understanding of available IoT technologies having the potential to transform the biologic and biopharmaceutical cold chain, thus enhancing product quality and ensuring patient safety.