Recent Advancements in Product Family Design and Platform-Based Product Development: A Literature Review
Bibliographic record
Abstract
Increase of demand on product variety has pushed companies to think about offering more and more product variants in order to take more market shares. However, product variation can lead to cost increase for design and production, as well as the lead time for new variants. As a result, a proper tradeoff is required between cost-effectiveness of manufacturing and satisfying diverse demands. Such tradeoff has been shown to be manageable effectively by exploiting product family design (PFD) and platform-based product development. These strategies have been widely studied during the past decades, and a large number of approaches have been proposed for covering different issues and steps related to design and development of product families and platforms. Verification and performance of such approaches have also been traced through practical case studies applied to several industries. This paper focuses on a review of the research in this field and efforts to classify the recent advancements relevant to product family design and platform development issues. A comprehensive review on the state-of-the-art research in this field was done by Jiao et al. in 2007; therefore the main focus of this paper is on the research activities from 2006 to present. Mainly, the effort of this paper is to identify new achievements in regard with different aspects of product family design such as customer involvement in design, market driven studies, new indices and metrics for assessing families and developing the desired platforms, issues relevant to product family optimization (i.e., new algorithms and optimization approaches applied to different PFD problems along with their benefits and limitations in comparison to previously developed approaches), issues relevant to development of platforms (i.e., platform configuration approaches, joint platform design and optimization, and factors effective on forming proper platform types), and issues relevant to knowledge management and modeling of families and platforms for facilitating and supporting future design efforts. Through a comparison with previous research, new achievements are discussed and the remaining challenges and potential new research areas in this field are addressed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".