User-friendly Tracking of Manufacturing Processes Provenance

The tracking of manufacturing provenance (information about employed materials, machines, manufacturing processes, involved workers and orders over time) is an essential component of the reliable, digitized multi-tier supply chains in the Internet of Production. In order to track this data, the FactDAG model was recently introduced. In this model, resources are globally and persistently identified and the corresponding data is versioned and linked to related resources using provenance information. As a result, an immutable, trustable log of the (possibly entire) manufacturing processes is created.

An illustration of a multi-tier manufacturing process and the resulting FactDAG, recording the process steps and influences.

In the context of this thesis, the student should develop a web application (primarily to be used on tablet computers) in order to easily document manufacturing process steps and related information, as they are taking place in real time. For each step, it should be possible to add documentation in the form of photos, text data and links to employed resources and machines. As the identifiers for those resources are commonly physically expressed as barcodes or QR-codes, it should be able to scan and link those through the device camera. Finally, when adding an additional manufacturing process step, the tool should suggest common "next processing steps", based on previously recorded manufacturing processes.

The implemented solution should be evaluated in a user study with real-word manufacturing processes at the Institute of Textile Technology (ITA) at RWTH Aachen University.

Development should be conducted either with the

1. React web framework through the Expo tools, possibly in conjunction with Ant Design components, to speed up the development
2. Angular or Vue.js web frameworks, possibly in conjunction with NativeScript and/or Ant Design components (Angular / Vue), to speed up the development
3. Another solution of your choice if you have strong arguments why you prefer this toolchain