Effect of customer demand information sharing on a four-stage serial supply chain performance: an experimental study
Bibliographic record
Abstract
Customer Demand Information (CDI) sharing plays a vital role in reducing the bullwhip effect as well as in improving the performance of a supply chain.The objective of the present research is to identify the best form of CDI sharing experimentally for a four-stage serial supply chain under lost sales business environment.A supply chain role play game software package is developed for conducting suitable experiments.Different forms of CDI sharing tested in this research are periodic CDI, history of CDI and CDI in the form of distribution.It is found that all forms of CDI sharing have significant impact on the reduction of bullwhip effect compared to non-sharing of information and the upstream stages in the supply chain are benefited the most under CDI sharing.The statistical analysis also confirms that sharing CDI in the form of distribution is the most effective among the various forms of information sharing studied.The percentage reductions in magnitude of order variance under the most benefitted information sharing at distributor and factory stages are 64.43 and 66.04, respectively.It is also found that the performance of a supply chain depends on the degree of customer demand information shared among the stages in the supply chain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.006 | 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".