Currently, the supply chain QA organization within Roche produces a lot of data in its daily business. It is many types of data, such as information about temperature deviation, planned as well as unplanned events, contract status, and many more. This data is either stored in different systems or in local files. However, the data obtained is currently not being used to develop knowledge and make decisions in the most effective way possible. This is due to the fact that there are currently no frameworks available for employees that provide a guideline of how to employ DDDM.

To create the DDDM framework, first the state of the art in DDDM was explained. Based on the knowledge acquired through literature research and by extracting important aspects from QA organizations, the DDDM framework was created iteratively. The framework comprises of the application of DDDM in supply chain QA, a DDDM maturity level categorization, and a step-by-step guideline that helps the organization to apply DDDM. A current use case was used to evaluate the DDDM framework during a workshop. Finally, an outlook for future use and development of the DDDM framework was recommended to Roche.

The thesis created several deliverables and outcomes that benefited the supply chain QA organization in further areas of work. Based on the results of this thesis, it can be concluded that the DDDM framework generates added value for the supply chain QA organization. The DDDM framework assisted in expanding the organization's knowledge in regards to DDDM and successfully integrating a use case.