A synthesis mapping of approaches, processes, methods and tools from the scientific literature for multidisciplinary product development - Searchable map
Bibliographic record
Abstract
This document introduces a synthesis map of approaches, processes, methods and tools for multidisciplinary product development. The presented map is associated with an article and differs from the one provided in the article in that it is searchable, which makes it easier to pinpoint the approaches, processes, methods and tools. This document comprises a legend and the map. To contextualize the map, multidisciplinary products arise from the integration of contributions from mechanical, electrical, electronics, software, and broadly information and communication technologies. This “multidisciplinarity” implies a higher technical and organizational complexity that can invite companies to adapt their development. To support companies in the adaptation of their development and navigate the dense and fragmented scientific literature corpus on multidisciplinary product development, the authors proposed to graphically organize it. In a first phase, multidisciplinary product development was investigated by analyzing three specific types of multidisciplinary products that can be referred to as “cyber-physical systems”, “mechatronics”, and “smart products and systems” in the literature. This first phase led to three maps which graphically organized a total of 236 “concepts and techniques” identified from 167 scientific papers through an extensive literature review and categorized based on a four-level model paired with a decision tree. A second phase, which result is represented below, introduced some simplifications and filters to the initial maps, which narrow down the number of concepts and techniques for multidisciplinary product development to 61. The following map represents a preliminary repository of concepts and techniques for multidisciplinary product development and serves to support companies in their transformation from the product development perspective by providing them with a synthesized overview of the related literature. This work is particularly suited for companies and researchers looking for getting acquainted with the scientific literature related to multidisciplinary product development and how the different concepts and techniques can be associated. A list of the filtered and merged concepts and techniques is appended to the end of the document.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".