Decision support tools for product customisation: an in-depth review
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
Product customisation has grown in popularity in recent decades with the advent of Industry 4.0 technologies and other advanced manufacturing tools. Decision support systems can play an important role in the early planning stages of the customisation process to assist companies in selecting strategies for customisation and guide them through the product design process. Such decision support tools must take into consideration many factors relating to the capabilities and goals of the company, the market, and the product being offered. The focus of this paper is to identify the different types of customisation strategies that a firm can pursue, highlight the key factors of success in offering customisation and review decision support tools for deciding to customise, selecting a customisation strategy and for the design customisation process. This paper culminates with a proposed framework for a decision support tool for automatic matching of customisation needs and services. [Submitted 7 October 2022; Accepted 15 August 2023]
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it