With or Without Forecast Sharing: Competition and Credibility under Information Asymmetry
Bibliographic record
Abstract
Forecast sharing among trading partners lies at the heart of many collaborative and contractual supply chain management efforts. Even though it has been praised in both academic and practitioner circles for its critical role in increasing demand visibility, some concerns remain: The first one is related to the credibility of forecast sharing, and the second is the fear that it may turn into a competitive disadvantage and induce suppliers to increase their price offerings. In this study, we explore the validity of these concerns under a supply chain with a competitive upstream structure, focusing specifically on (i) when and how a credible forecast sharing can be sustainable, and (ii) how it impacts on the intensity of price competition. To address these issues, we develop a supply chain model with a buyer facing a demand risk and two heterogeneous suppliers competing for order allocation from the buyer. The extent of demand is known only to the buyer. The buyer submits a buying request to the suppliers via a commonly used procurement mechanism called request for quotation (RFQ). We consider two variants of RFQ. In the first type, the buyer simply shares the estimated order quantity with no further specifications. In the second one, in addition to this, the buyer also specifies minimum and/or maximum order quantities. We fully characterize equilibrium decisions and profits associated with them under symmetric and asymmetric information scenarios. Our main findings are that the buyer can use a RFQ with quantity restrictions as a credible signal for forecast sharing as long as the degree of demand information asymmetry is not too high, and that, contrary to above concerns, the equilibrium prices that emerge between competing suppliers under asymmetric information may indeed increase if the buyer can not share forecast information credibly with its upstream partners.
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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 | 0.017 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".