Using Big Data to Minimize Uncertainty Effects in Adaptable Product Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the major concerns for adaptable products is to ensure the products to meet customer preferences. As customers may update their preferences over the product lifetime, designers need methods to measure those preferences. Lack of knowledge (uncertainty) in customer preferences could endanger the product success. If designers can update their views for customer requirements, a product can be designed to follow the user requirements. Huge data are generated continuously in product user behavior, product usage, manufacturing cost etc., now called as Big Data. Collecting, managing and applying such huge set of data in an innovative method can reduce uncertainties. In this paper, a method is discussed to minimize uncertainty effects on products to improve the product adaptability. Uncertainty is considered as changes of the customer preference. The proposed method uses Big Data (BD) in the analysis of uncertainty. The effect of quantified uncertainties on product adaptability is investigated. The method is concluded with the most affected parts and functional requirements to be updated to meet changing requirements. The proposed method is compared to a developed agent-based modeling (ABM) method in a case study. Although there are some differences between both methods in the uncertainty effect evaluation, The BD method provides more confidence for the design solution. The paper also proposes some future research directions for design of adaptable products using Big Data.
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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 | 0.001 | 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) | 0.001 | 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 it