Purpose – The purpose of this study is to provide a structured process to evaluate data sharing opportunities. In doing so, we provide a three phase process that assists data owners to increase utilisation of their resource, as well as introducing the possibility to scale such a process to other industries through future research. Method – To gain insights, thematic analysis was used on data collected through a single case study in three separate waves of interviews, as well as through observations. A total of 13 respondents were involved, all industry experts from a global pharmaceutical company working actively with the researched question. Findings – The findings resulted in 25 challenges with the current evaluation process, segmented into 11 sub themes and four main themes: Unstructured process for evaluating data sharing, Unclear information gathering requirements, Lack of objective evaluation criteria, and Uncertain decision making. Theoretical contribution – This study contributes to the existing literature by conceptualising challenges with evaluating data sharing opportunities. Furthermore, by applying principles and logic of Stage-Gate methodology, the thesis introduces a more structured way of evaluating data sharing opportunities. Practical contribution – This study introduces a process for data owners and companies within the pharmaceutical industry that facilitate a smoother and more efficient workflow when faced with data sharing opportunities. Our three phase process promotes utilisation to increase development through data sharing. Limitations of the study – The case study was limited to a single company that imposed the risk of bias and misguided focus. We propose future research to trial the recommended process in other companies within the pharmaceutical industry as well as introduce it to other data focused industries. / Syfte - Syftet med den här studien är att tillhandahålla en strukturerad process för att utvärdera möjligheter till datadelning. Därmed tillhandahåller vi en process i tre faser som hjälper dataägare att öka utnyttjandet av sina resurser och som ger möjlighet att skala upp en sådan process till andra branscher genom framtida forskning. Metod - För att få insikter användes tematisk analys av data som samlats in genom en enda fallstudie i tre separata intervjuer samt genom observationer. Totalt deltog 13 respondenter, alla branschexperter från ett globalt läkemedelsföretag som arbetar aktivt med den aktuella forskningsfrågan. Resultat - Resultaten resulterade i 25 utmaningar med den nuvarande utvärderingsprocessen, uppdelade i 11 underteman och fyra huvudteman: Ostrukturerad process för utvärdering av datadelning, otydliga krav på informationsinsamling, brist på objektiva utvärderingskriterier och osäkert beslutsfattande. Teoretiskt bidrag - Den här studien bidrar till den befintliga litteraturen genom att konceptualisera utmaningar med att utvärdera möjligheter till datadelning. Genom att tillämpa principerna och logiken i Stage-Gate-metodiken introducerar avhandlingen dessutom ett mer strukturerat sätt att utvärdera möjligheter till datadelning. Praktiskt bidrag - I denna studie introduceras en process för dataägare och företag inom läkemedelsindustrin som underlättar ett smidigare och effektivare arbetsflöde när de ställs inför möjligheter till datadelning. Vår process i tre faser främjar utnyttjandet för att öka utvecklingen genom datadelning. Begränsningar i studien - Fallstudien var begränsad till ett enda företag, vilket medförde en risk för bias och missriktad fokus. Vi föreslår framtida forskning för att testa den rekommenderade processen i andra företag inom läkemedelsindustrin samt införa den i andra datafokuserade branscher.

Metal-based additive manufacturing (AM) has emerged as a cutting-edge technology for fabricating complex geometries with high precision. However, the major challenges to the wider adoption of metal AM technologies are process uncertainty-induced quality issues. Consequently, there is an urgent need for fast and reliable certification techniques for AM components, which can be achieved by leveraging Artificial Intelligence (AI)-enabled modeling. Developing a robust AI-enabled model presents a significant challenge because of the costly and time-intensive nature of acquiring diverse and high volume of datasets. In this context, the data-sharing attributes of Manufacturing-as-a-Service (MaaS) platforms can facilitate the development of AI-enabled certification techniques in a collaborative manner. However, sharing process data poses critical concerns about protecting users’ intellectual property and privacy since it contains confidential product design information. To address these challenges, the overarching goal of this research is to investigate how process data and process physics can be leveraged to develop in-situ component certification techniques focusing on data privacy for metal AM systems. This dissertation aims to address the need for novel quality monitoring methodologies by utilizing diverse data sources derived from a range of printed samples. Specifically, the research effort focuses on 1) the use of in-situ thermal history data and ex-situ X-ray computed tomography data for real-time layer-wise anomaly detection method development by analyzing the morphological dynamics of melt pool images; 2) the development of a framework to evaluate the design information disclosure of various thermal history-based feature extraction methods for anomaly detection; and 3) the privacy-preserving and utility-aware adaptive AM data deidentification method development that takes thermal history data as input.