The increased sophistication of customer needs pushes manufacturers toward integrated offerings where physical products and intangible services collaboratively generate value, also known as Product-Service Systems (PSS). This shifts the focal point from product performance to overall system functionality. However, this naturally increases the importance of requirements linked to the operation and the system's behavior, e.g., reliability, safety, and flexibility. These kinds of requirements that dictate how a system should behave and operate in its context are called non-functional requirements. However, most manufacturing firms have a legacy of focusing mainly on functional requirements. 

Alongside this trend, there has been an increasing affordability and availability of data. However, how this data can be utilized for value creation remains a question for the industry. Operational data can serve as a vital source of information about the PSS behavior and value delivery process. Since non-functional requirements depend on the operational context for measuring their performance, operational data can thus provide new insights. 

This thesis aims to study the motivation for and challenges of working with non-functional requirements and value within Digital PSS design by manufacturing firms. Firstly, the management of non-functional requirements in the design process is studied. The empirical research determined that there are six challenges that a design team and organization face when working with non-functional requirements. The challenges highlight that non-functional requirements’ fuzzy and intangible aspects make them easy to neglect and hard to include in design and decision-making. A state-of-the-art review is conducted to identify possible remedies.

Onward, the intersection between data and value is explored. An overarching classification of operational data and how these can contribute to different forms of value creation is presented based on previous literature. Further, the analysis shows what kind of operational data can be collected using three levels of granularity. Experiences and reflections from multiple companies at different stages in their servitization journey are gathered to complement and expand the perspective on operational data and value.