Course Insights: Innopharma’s Post-Graduate Diploma in Digital Transformation

Digital transformation is the integration of technology into all aspects of a business and it is a growing area with €500 billion expected investment annually in EU Digital transformation. While 75% of EU companies use Cloud, Artificial Intelligence and Big Data technologies, 40% of employees will require reskilling in digital skills in the next 3 years. Taking a course in digital transformation could be the right next step on your career journey whether you want to move up the ladder, across to another division or even move to a different organisation. Find out more about the growth of the digital transformation industry in our latest blog.

Overview of the Postgraduate Diploma in Digital Transformation 

This course will equip you with knowledge and skills related to the evolution of life science manufacturing from a traditional reactive process to the emergence of Industry 4.0, Pharma 4.0 and smart factories. You will research the current and future trends in life science manufacturing; the applications new technologies, data analysis; strategy, digital maturity and ethical/corporate social responsibilities associated with digital transformations. 

The blended programme is delivered over one full year, consisting of three semesters, with students completing three semesters of taught modules. We have two intakes per year: February and September. The course is delivered live online each Tuesday and Thursday evening (6:30 pm – 9:30 pm) along with an average of 2 on-site lectures per month on Saturdays (typically 9:30am – 2:30pm). Our lectures are recorded so you can watch back later if you miss it or want to revise key concepts when preparing for assignments/exams.   

A Focus on Life Sciences 

This course is ideal for those in or entering the life science industry. Life science encompasses companies in the fields of biotechnology, pharmaceuticals, biomedical technologies, life systems technologies, nutraceuticals, cosmeceuticals, food processing, environmental, biomedical devices, and organisations and institutions that devote most of their efforts in the various stages of research, development, technology transfer and commercialisation. Many organisations that provide products or services to this sector such as consulting, project management, regulatory oversight and more. 

What Will You Learn on the Course 

On successful completion of the programme learners will be able to: 

• Demonstrate detailed knowledge of the facts, concepts, principles and methods associated with the life cycle of scientific discovery, development, manufacture and advanced manufacture of pharmaceutical and medical device products and services.
• Demonstrate detailed knowledge of the concepts of data acquisition, data architecture, data storage and data analysis of big data as well as data systems associated with manufacturing within life science.
• Critically interpret current legislation, regulation and quality concepts as well future implications of new technologies, processes analytical tools and big data on the advancement and governance of product quality and patient safety.
• Assess business and operational strategies as well as implications to organisations and individuals, based on advancements in new technologies and the abundance of data in a virtual environment.
• Apply advanced methods for acquiring, interpreting and analysing current research relating to trends in life science and big data management.
• Effectively communicate information using data visualisation tools and techniques.
• Critically comment on current and potential trends in advanced manufacturing and data analytics in the life science industry.
• Make recommendations on current and future strategies informed by the technical, economic, environmental and social implications involved. 

Opportunities After Completing the Course 

After completing the course, you will be capable of addressing many different challenges in the current manufacturing landscape, including process control, data acquisition, data management, data analysis, quality and process improvement. You will be able to take up employment within life science organisations who are transforming their processes to integrate data, new technologies, data analytics, improved ways of working and improved support processes.  Here is a guide on how to secure a job interview

Typical roles include: 

• Manufacturing managers
• Process engineers
• Manufacturing execution system managers
• Disruption specialists
• Data analysts
• Digital Transformation consultants
• Business improvement specialists
• Business intelligence specialists
• Regulatory compliance specialists
• Departmental or project managers 

The Modules on The Course 

The program contains several modules focusing around the key pillars of advanced manufacturing, business & operations and data analytics. Below is an overview of each module and its assessment. 

Advanced Manufacturing in the Smart Factory 

This module explores the concepts of the Smart Factory and investigates the evolution of pharmaceutical and medical device manufacturing from a traditional reactive process to the emergence of Industry 4.0 and Pharma 4.0. The module investigates how a true smart factory can integrate data from system-wide physical, operational, and human assets to drive manufacturing, maintenance, inventory tracking, digitisation of operations through the digital twin, and other types of activities across the entire manufacturing network. 

The syllabus includes: 

• Traditional Life Science Manufacturing & Levels of Automation.
• Industry 4.0 Pillars.
• Technological & Commercial Drivers of the Smart Factor.
• Emerging Technologies of Industry 4.0.
• Process Analytical Technologies (PAT). 

Typical assessments have asked students to search aspects of industry 4.0 implementation and its impacts on the digitalisation of processes within the life science industry. 

Business Case Development 

This module investigates the concepts of Business Case Development and Business Model Canvas specifically, as it relates to the life science industries. It explores how to develop an effective business case based on a transformation of an organisation and the impact of connecting the supply chain end to end through real-time performance feedback and leveraging data from various sources. 

The syllabus includes: 

• Overview of business case development
• Business model canvas
• Financial / risk analysis
• Quality / compliance analysis
• Blueprint development / solution selection
• Project management
• Change management communication strategy 

Typical assessments have asked students to develop a business model canvas checklist and project template suitable for application in the life science sector.   

Big Data Acquisition and Management 

This module is specifically designed to develop the learners’ knowledge of the use of data collection, cleaning, aggregation, storage and data serving within the context of the life science industry. Learners identify and gain an understanding of the principles and functionalities of big data management models and tools, as well as their ability to acquire, process and manage large data collections. 

The syllabus includes: 

• Concept and types of Big Data.
• Data Pipeline Models.
• Data Acquisition.
• Data Warehousing.
• Hybrid Architecture.
• Life Science Data Management Systems Architectures.
• Data Serving. 

Typical assessments have asked students to identify the data set to be collected, identify the appropriate pipeline model, data storage and serving mechanisms. 

Quality Management in a Digital Age 

This module explores the impact of advances in technology on the perception of quality and such implications on systems and processes. By leveraging cloud-based data management and advanced analytics, key information can be generated going beyond compliance and supporting quality decisions and continuous improvement. As much of the critical data resides in a GxP-regulated cloud, quality professionals have the visibility and access they need to monitor quality metrics, and make fact-based quality decisions in a timely manner, supported by predictive analytics on quality parameters. 

 The syllabus includes: 

• Future Definition of Quality and Quality Management.
• Lifecycle approach to pharma and biopharma products.
• Lifecycle approach to medical device / combination devices.
• Quality Metrics. 

Typical assessments have assessed students’ ability to research specific topics relating to future trends in quality – future of quality, life cycle approach for pharma and medical devices. 

Research Methods 

This module provides learners with the knowledge, skills and competences to conduct postgraduate-level research activities. It presents a broad range of research methods, both quantitative and qualitative which serve to advance the quality and rigour of their assignment work in this and related modules. A significant element of the module is devoted to research design issues to ensure that learners understand the significance of the process of research and its implications for how trustworthy the findings from the research can be considered. This component of the course covers research paradigms, research strategies, and quantitative and qualitative data collection and analysis. The importance of illustrating the process in reporting and presenting the research concludes the module. 

The syllabus includes: 

• The research process.
• Conducting literature review.
• Research questions and hypotheses.
• Ethics.
• Quantitative and Qualitative methods
• Transforming research ideas to product and services.
• Project management. 

Typical assessments have asked students to design a research plan. 

Operational Excellence – Lean Sigma 4.0 

The aim of the module is to research the origins of operational excellence, lean thinking, six sigma and industry 4.0 and explore how this integration of models can deliver the next advancement in improvement. Learners explore emerging models of Lean Industry 4.0 and Lean Sigma 4.0 and research the optimal ways of implementing and adapting lean tools and digital technologies to suit the life science industry. Concepts such as corrective action, preventative action (CAPA), quality risk management (QRM) and failure mode effect analysis (FMEA) are also explored as well as how effectively they have been implemented in the industry and the resulting impact on patient safety. The module also examines the technological innovation techniques to generate new products and ideas, specifically concepts such as design for six sigma and theory of inventive problem solving (TRIZ). 

The syllabus includes: 

• Operational Excellence Implementation Models.
• The Lean Enterprise.
• Six Sigma.
• Application of Quality Function Deployment in life science manufacturing. 

Typical assessments have asked students to develop a quality function deployment matrix to develop a solution to some aspect of the life science industry.  

Business Strategy & Change Management 

This module examines the factors which are driving strategic change in the life science sector, and the implications for the individuals and teams tasked with planning and leading that change. It also examines the leadership styles required to lead such organisations through significant change plus deliver a culture of innovation, accountability and performance. Innovation becomes successful with a culture in the organisation that promotes initiative, ideation, transparent value analysis of ideas, and prompt and efficient implementation and exploitation. 

The syllabus includes: 

• Current challenges of life science industry.
• Application of strategic thinking in achieving Industry 4.0.
• Measuring organisational maturity & transformation.
• Resourcing for the future organisational structure.
• Corporate governance and business ethics. 

Typical assessments have asked students to use a digital maturity matrix to assess an organization as it prepares for a digital transformation. 

Analysis of Big Data 

This module aims to equip the learner with a range of the most relevant topics that pertain to contemporary analysis practices and are foundational to the field of big data analytics. Learners are guided through the theoretical and practical differences between traditional datasets and big data datasets. An overview of the initial collection of data is explored for multiple data sources. A grounding in analytical statistics is an important component of the module. Learners are expected to apply principles of statistical analytics to solve problems and inform decision-making. Learners achieve this through developing a knowledge and understanding of statistical analytics techniques and principles, while applying these techniques and principles in typical real-world scenarios. 

The syllabus includes: 

• Overview of big data analysis.
• Types of data analytics.
• Descriptive analytics.
• Diagnostic analytics.
• Predictive analytics.
• Prescriptive analytics.
• Data analytical software. 

Typical assessments have asked students to use the appropriate analytical tools to analyse a given dataset. 

Visualisation and Storytelling with Data 

The aim of this module is to enable learners to effectively use computer-based data visualisation techniques and strategies to communicate information. Learners are able to construct datasets appropriate to the life science industry and analyse and present complex data as information through the application of design principles for data visualisation. In addition, learners are able to critically assess and evaluate different data types, information requirements, and data visualisation tools to provide clear, effective, and engaging graphical information representations. 

The syllabus includes: 

• Story telling through Data Visualisation
• The Visualisation Process.
• Visualisation Techniques and Software Application.
• Comparing and Evaluating Visualisation Techniques. 

Typical assessments have asked students to demonstrate their visualisation skills through an assignment to visualize a specific dataset which they collect, analyse and present. 

Critical Competency Development 

This module provides you with the skills to recognise the changing environmental climate, and identify the skills and competencies to act in such an environment. From an employer’s perspective, the ability to define new job roles, competencies and development opportunities are equally critical. The module provides an overview of a range of professional skills required to obtain employment in the life science industries. The ability to develop an appropriate CV, successfully complete an interview and present oneself in the best possible manner are key elements of the module. An additional element of the module is to assist learners to develop their network which aids their entry into the industry. 

The syllabus includes: 

• The Future of Work, Skills and Competencies.
• Critical Thinking and Problem Solving.
• Competency Development and Demonstration.
• Networking.
• Interviewing skills.
• Personal Coaching and Mentorship.
• Professionalism and work ethic. 

Typical assessments have asked students to assess the future skills they need, prepare a CV, prepare a Linkedin profile and participate in a simulated interview. 

Design and Academic Considerations 

The programme was designed by Innopharma Education through engagements with industry advisors and in conjunction with Microsoft. The programme is delivered by Innopharma Education with Griffith College. Successful graduates are awarded a postgraduate diploma validated by Quality & Qualifications Ireland (QQI) and it is at level 9 on the National Qualification Framework (NQF). 

Make The Right Move 

Over 75% of our Innopharma Graduates have successfully advanced their career post completion of our courses. Choosing the appropriate digital transformation program is a crucial milestone on your career journey. This correct course will equip you with the necessary expertise and abilities, paving the way for your long-term success.  Contact us to learn more about the growth of digital transformation and our digital transformation course.