Harmonizing Transformations: Key Considerations for Implementing AI-Driven Sustainability in Pharmaceutical Manufacturing

Abstract

This master’s thesis investigates the role of artificial intelligence (AI) in enhancing sustainability within the pharmaceutical manufacturing sector. As environmental concerns intensify globally, the pharmaceutical industry is under increasing societal and regulatory pressure to adopt sustainable practices that minimize its ecological footprint. Meanwhile, the adoption of Industry 4.0 technologies, including AI, becomes increasingly relevant due to their potential to optimize manufacturing processes and enhance sustainability. However, there currently is a significant lack of practical guidance for the successful integration of these technologies, specifically within the pharmaceutical industry. To address this, the study aims to identify key considerations for integrating AI methods to optimize sustainability parameters, such as energy efficiency, carbon emissions, water usage, resource efficiency, and raw material optimization.

Employing a mixed-methods approach, the research combines a semi-systematic literature review with qualitative interviews and a quantitative survey involving industry stakeholders and subject matter experts. The findings reveal that machine learning (ML), a subset of AI, is the most prevalent technique applied for sustainability optimization of manufacturing processes. The most feasible parameter to optimize with AI was found to be energy consumption, highlighting the importance of prioritizing energy efficiency within the manufacturing domain. The importance of adopting AI for sustainability purposes was highlighted by the survey results, which indicate a strong consensus among participants regarding the benefits of AI integration, including improved operational efficiency, enhanced decision-making, and significant cost savings.

The findings underscore the necessity of fostering sustainability literacy among professionals involved in AI implementation, such as data scientists and manufacturing experts. Furthermore, the research highlights the need to reevaluate pharmaceutical intellectual property rights to facilitate knowledge sharing and collaboration, which are essential for advancing sustainable practices across the industry.

This thesis contributes to the existing body of knowledge by addressing the limited exploration of AI's transformative potential in pharmaceutical manufacturing sustainability. It provides actionable insights and practical guidance for industry stakeholders, emphasizing the importance of aligning AI integration with sustainability goals. By offering targeted recommendations, particularly in prioritizing energy efficiency and leveraging machine learning techniques, this research empowers pharmaceutical firms to navigate the complexities of sustainable manufacturing effectively. Ultimately, this study supports the industry's transition towards greener production methods, aligning with societal expectations for responsible innovation and environmental stewardship.