The shift to next-gen manufacturing: Exploring the opportunities of integrating IoT, AI and robotics into the production process

Abstract

The digital transformation has had a significant impact on the production landscape worldwide in recent years. In particular, the introduction of Industry 4.0 technologies has led to profound changes in production processes. The concept of Industry 4.0 integrates an array of cutting-edge technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Robotic Process Automation (RPA), with the objective of advancing the connectivity and intelligence of manufacturing systems. The adoption of these innovations within production workflows offers the prospect of improved efficiency, heightened flexibility, and enhanced quality in manufacturing outputs. The following research questions are addressed in this thesis: RQ1: What are the potentials of integrating Industry 4.0 technologies into the production process? RQ2: What challenges arise when integrating Industry 4.0 technologies into the production process? Motivation and Problem Statement The digital transformation of manufacturing through Industry 4.0 technologies, particularly IoT, AI, and RPA, holds immense potential in enhancing operational efficiency, flexibility, and product quality. However, integrating these technologies presents significant challenges. The thesis aims to comprehensively understand the benefits and obstacles associated with this shift towards smart manufacturing. Key issues include system interoperability, workforce readiness, cybersecurity, data quality, and ethical concerns. These challenges are particularly critical in today’s increasingly complex and interconnected production environments. Structure and Methodology The thesis is structured into five main chapters. The first two chapters delve into theoretical foundations, while the third chapter presents a dual-perspective analysis of opportunities and challenges. The final chapter concludes with empirical validation through expert interviews. Methodologically, the study employs a qualitative-conceptual framework, combining a systematic literature review with semi-structured interviews conducted with industry professionals. The literature was sourced from academic databases and evaluated for relevance and credibility. The interview data were then thematically analyzed to identify crosscase insights and sector-specific findings. Key Findings The integration of IoT, AI, and RPA in production can bring about substantial benefits, including predictive maintenance, real-time responsiveness, quality optimization, and enhanced human-machine collaboration. However, successful implementation faces several challenges, including resistance to change, limitations imposed by legacy systems, inconsistencies in data, cyber-security threats, and ethical dilemmas. To overcome these obstacles, the thesis proposes strategic enablers such as robust data governance, cybersecurityby-design, structured change management, and ethical AI governance. The findings indicate that while the technical tools are readily available, organizational and cultural readiness are equally crucial for a successful transformation.

Date of Award	2025
Original language	English
Supervisor	Harald Dobernig (Supervisor)