Decoding the digital thread digitalization approach for product design and development: benefits, challenges, and extensions
Bibliographic record
Abstract
Abstract Digital Tools are reshaping how Engineering Design data and information are produced, processed, used, reused, shared, and stored. The Digital Thread prioritizes the flow of design data and information, promoting effective collaboration and process efficiency. While literature showcases the immense application and capability of taking a Digital Thread approach to Product Design, best practices, key features, and benefits of successful implementations remain scarce. Reviewing and understanding successful implementations can assist researchers and practitioners in making informed decisions to effectively implement Digital Threads in their product design processes. This article addresses this gap by reporting a post hoc review of a collaborative Research & Development project that developed and implemented a Digital Thread approach to the design of hydrogen composite pressure vessels. A thematic analysis of the project’s reports and interviews with members of the project team was performed to identify the key features that expedite and improve the design process through an effective Digital Thread implementation. The post hoc review offers valuable insights – in the form of six feature benefits, four potential implementation challenges, three possible extensions, and four best practice recommendations – for companies looking to adopt and implement a Digital Thread approach to their design process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".