The rapid evolution of market dynamics and consumer expectations has significantly intensified the competitive pressures facing product development processes. In response, organizations increasingly adopt advanced technologies such as Artificial Intelligence (AI), especially Machine Learning (ML). This master’s thesis investigates the systematic integration of Machine Learning across the product development lifecycle, aiming to address the limitations observed in traditional implementations. The core research problem addressed is the fragmented and bounded adoption of ML across product development phases, due primarily to technical, economic, and organizational barriers such as data quality issues, significant initial investments, and resistance to change. To fill the identified research gap, the thesis seeks to establish a comprehensive framework guiding the systematic application of ML techniques across all stages of product development, from initial planning to product launch and post-launch review. To fulfill this objective, the thesis follows a structured approach. Initially, it examines foundational concepts of Artificial Intelligence and Machine Learning, including historical developments, key paradigms, and essential ML techniques. Following this, the thesis delves into the product development process, highlighting each phase's specific challenges and opportunities for ML integration. Evaluating the proposed framework through extensive review of literature and real-world case studies, the research identifies clear benefits of ML integration on product quality, product cost, development time, development cost and development capability. Moreover, practical recommendations are provided to overcome typical adoption barriers. By presenting actionable insights and methodology, this thesis offers valuable contributions to both academic knowledge and industrial practice, helping organizations to realize ML's potential, thereby enabling faster innovation, improved product quality and reduced costs, and greater responsiveness to market changes.
- Innovation and Product Management
Implementation of Machine Learning in Product Development: An Integrated Approach
Lupinacci, F. (Autor). 2025
Studienabschlussarbeit: Masterarbeit