Order Quantity and Timing Flexibility in Supply Chains: The Role of Demand Characteristics
Bibliographic record
Abstract
We study how differences in product demand characteristics affect the strategic value of different types of supply chain flexibility for accurate response. We propose a single-period inventory modelling framework with two ordering opportunities. The second order reflects updated demand information and potentially capitalizes on supply chain flexibility. We consider two complementary forms of flexibility: quantity flexibility in production and timing flexibility in scheduling. In this framework, we analyze the total inventory cost of a firm for alternate demand types. We model functional products through the standard assumption of independent demand over the period, fashion-driven innovative products through a Bayesian model, and innovative products with evolving demand through a Martingale process. The three demand processes exhibit very different behavior with respect to the value of the alternate forms of flexibility. We observe that quantity flexibility is of moderate value for functional goods and of high value for fashion-driven products for all lead times. Quantity flexibility is of low value for goods with evolving demand with long lead times but of high value for short lead times. Alternately, we observe timing flexibility is of highest value for functional goods, especially for cases of high holding cost, and is of lesser value for fashion-driven goods. It is of least value for goods with evolving demand. Both quantity and timing flexibility capabilities are required to significantly reduce the relevant supply chain costs for evolving-demand innovative goods when the lead times are long.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".