On tacit knowledge management in product design: status, challenges, and trends
Bibliographic record
Abstract
Mass personalisation production is one of the strategic priorities for the next transformation of the production paradigm and market economy. With the purpose of offering personalised products to satisfy customers on an individual basis, tacit knowledge rooted in individuals has become increasingly accepted as an integral part of product design. This paper aims to explore the state-of-the-art in tacit knowledge management with a product design focus. Particularly, methods for tacit knowledge acquisition, transfer, and reuse are reviewed and analysed. Research on tacit knowledge acquisition is mainly dedicated to making tacit knowledge explicit. In knowledge transfer, both formal and informal approaches have been adopted to enable knowledge circulation. Research on tacit knowledge reuse is much less and scattered, and the main work focuses on user modelling and the reuse of empirical knowledge. Five challenges of tacit knowledge management are identified in this paper: lack of unified tacit knowledge definition, massive heterogeneous data, authenticity and completeness verification, uncertainty and gaps in bridging tacit knowledge management and personalised design, lack of practical knowledge sharing and inheritance tools. To fill these research gaps, five thematic future directions are suggested with possible visions to facilitate knowledge circulation, customer co-creation and innovation in mass personalised design.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".