Organizing the fragmented landscape of multidisciplinary product development: a mapping of approaches, processes, methods and tools from the scientific literature
Bibliographic record
Abstract
Abstract The fourth industrial revolution is shaping a new industrial landscape. A variety of technologies related to software, information and communication technologies embody a ubiquitous digital and connectivity era. These technologies enable the creation of new products with the integration of connectivity, data collection and processing capacities which require combining engineering disciplines. Increasing product multidisciplinarity compels companies to adapt their product development practices. The scientific literature offers a variety of concepts and techniques to support multidisciplinary product development. This paper seeks to organize the landscape of concepts and techniques available for multidisciplinary product development. An extensive literature review was conducted, and 236 concepts and techniques were identified. Multidisciplinary products of interest deal with both software and hardware development and can be encountered through the denominations of cyber-physical systems, mechatronics and smart products and systems. An in-depth analysis led to the classification and mapping, for each product denomination, of the concepts and techniques available to support their development. The classification relies on a four-level model paired with a decision tree to thoroughly sort the variety of concepts and techniques into the approach, process, method, and tool levels. The mapping between the sorted concepts and techniques enabled the generation of graphical representations called cartographies. These cartographies serve to support companies’ transformation towards the fourth industrial revolution from the product development perspective by giving a general overview of the related literature, and guiding them in the identification of the most suitable approaches, processes, methods and tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.060 | 0.055 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".