Towards responsive vehicle supply: a simulation‐based investigation into automotive scheduling systems
Bibliographic record
Abstract
Abstract Vehicle supply has traditionally been based on forecast‐driven production, and a large fraction of cars has been sold from stock—a practice which incurs considerable cost in terms of stock holding and sales incentives. Derived from successes in other industries, the benefits of responsive supply systems capable of providing customized vehicles in short lead‐times have been pointed out. While the theoretical discussion of such ‘build‐to‐order’ (BTO) strategies is well advanced, the dynamic feasibility of implementing these concepts is far from understood. Using a simulation of a multi‐tier supply chain‐system, this paper investigates the impact of altering key aspects of the scheduling activities with the objective of determining the scope for potential improvements in responsiveness of the supply chain. The simulation results show that current vehicle supply systems are not capable of supporting BTO due to insufficient feedback between supply and demand, as well as due to the strong reliance on forecasting in the scheduling process. The paper concludes with a set of recommendations on how to improve current scheduling systems towards increasing the content of vehicles built to customer order.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".