The effects of decision timing for pricing and marketing efforts in a supply chain with competing manufacturers
Bibliographic record
Abstract
Abstract This paper investigates the impact of decision timing for pricing and marketing efforts in a supply chain led by competing manufacturers. We develop and solve six games to consider the scenarios (games) where prices and marketing efforts (ME) are decided simultaneously, and when they are not (i.e., ME is set either before or after prices). We examine these three scenarios for the benchmark case of a bilateral monopolistic channel, then extend the analysis to a supply chain with competing manufacturers. We identify the optimal decision timing by comparing equilibrium profits and strategies across games in each supply chain setup. We find that a monopolistic manufacturer always prefers that prices and ME be decided simultaneously. However, this result does not hold when product competition is taken into account. The optimal decision timing for competing manufacturers depends on the retailer's and manufacturers' ME effectiveness levels as well as on competition intensity. Specifically, when ME are not very effective, a simultaneous decision scenario is preferred because it provides the advantage of higher profit margins or sales. However, for highly effective ME, manufacturers prefer to decouple ME and pricing decisions. The retailer's optimal scenario is either to make all decisions simultaneously or to choose prices prior to ME. This means that supply chain firms can face conflict due to the decision timing for prices and ME.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".