This module aims to provide students with the background of Industry 4.0 and how this conceptual framework can be applied in the biopharmaceutical sector. The course will provide students with the knowledge and understanding of the emerging needs of the biopharmaceutical sector as it progresses towards greater integration of technology and data, to become more agile, to increase productivity and competitiveness.

1. Demonstrate they have detailed knowledge and understanding of the background to Industry 4.0 and emerging needs in the biopharma sector, the source and quality of existing data and applicability of industry4.0

2. Demonstrate they have detailed knowledge and understanding of the application of predictive modelling, automation systems, PLCs, robotics, vision systems

3. Evaluate embedded systems, the internet of things (IoT) and the cyber-physical systems (sensors, control boards) necessary for data acquisition

4. Formulate judgements on the relevant value and application of existing data sources, design tools, digital twins, simulation models, and augmented reality/virtual reality.

5. Integrate knowledge and formulate judgements onthe application of robotics and industrial vision systems to biomanufacturing operations.

This module aims to provide students with a detailed knowledge and understanding of the future needs of the biopharmaceutical sector beyond Industry 4.0, and includes approaches such as continuous manufacturing, single-use optimization, modular facility design; ATMPs, Cell & Gene therapy; Novel technologies.

1. Demonstrate a detailed knowledge of current manufacturing processes for upstream, downstream and fill finish operations.

2. Evaluate novel technologies in upstream and downstream to implement continuous and intensified processes.

3. Demonstrate an understanding of Advanced Therapy Medicinal Products (ATMPs), including biomedical science, molecular basis of disease, stem cell biology and tissue repair, and clinical applications for ATMP

4. Integrate knowledge and formulate judgements onATMP manufacturing challenges, current and developing technologies, and quality control / quality affairs.

The purpose of this module is to complete a research proposal which may subsequently form part of the MSc thesis. Students will have the opportunity to formulate a research topic, develop a research schedule and select appropriate methodologies for a research project. The learner will study different research methods, ethical considerations, learn how to critically analyse scientific documents, disseminate research in terms of reports, and communicate effectively.

1. Produce a research proposal document.

2. Undertake a focused literature search and generate a literature review on a research topic.

3. Select and apply an appropriate research methodology while adhering to ethical considerations.

4. Communicate effectively their research outcomes.

This module aims to provide students with an understanding of the key concepts of Industry 4.0 and outlines the application of the latest digital trends and technologies in the Bio-Industry 4.0 field, and how they can be practically applied.

1. Critically assess the smart factory concept in the biopharma industry, considering digital supply network, real-time data, optimised and predictive production.

2. Evaluate the role of PAT in the implementation of the smart factory concept, focusing on efficient use of data from different sources old and new, to bring about measured efficiencies and controls.

3. Evaluate the current applications of automation and the future potential applications and benefits of automation and data integration in the implementation of the smart factory including theidentification of challenges.

4. Demonstrate an understanding of the ISA-95 model, the international standard for the integration of enterprise and control systems, along with other automation standards such as NAMUR and Profinet.

5. Covering the entire bioproduction supply-chain from design to delivery of the product to the patient, learn to evaluate various digital tools reproducing essential production elements (digital twins, serious games, immersive reality, virtual reality, and augmented reality) and cognitive approaches supported by artificial intelligence to promote understanding of processes and the appropriation of professional practices, using case studies where appropriate.

6. Engage in and demonstrate independent learning as well as communicate effectively as an Individual and ora member of a team

This module aims to provide students with a detailed knowledge and understanding of the future needs of the biopharmaceutical sector developing data literacy and skills vital for ensuring that biopharma organisations can transform large, complex, disconnect data sets into tangible business assets.

1. Demonstrate a knowledge of the theories, techniques and models for data identification, acquisition, cleaning, and aggregation of the range of biopharma data types

2. Critically assess a range of BioPharma data management systems architectures (ERP, MES, LIMS) in terms of their suitability to acquire, process and manage large data collections

3. Evaluate the challenges with data integrity, storage, security, management, cybersecurity, and data ethics

4. Research and assess a range of architecture models considering future positive impact on the biopharma business. Assessment of company requirements for implementation of architectural models.

5. Prepare data for use in industry 4.0 analytical use cases to support data driven business insights. Overview of key trends in the application of data and analytics to pharma analytics use cases, including business continuity.