Competition and Cooperative Bargaining Models in Supply Chains
Bibliographic record
Abstract
In the last two decades or so, a significant emphasis of the research literature in operations management has been on the strategic interaction of firms in a supply chain. Individual firms in supply chains make decisions on multiple levers such as capacity, inventory and price, to name a few, that have consequences for the entire supply chain. In modeling strategic interactions, the operations literature has followed the large literature in industrial organization and economics. Competition between firms in a supply chain has largely been modeled using noncooperative game theory and the associated concepts of equilibrium that predict the outcomes. There are a few key differences between the industrial organization literature and the research in operations management. First of all, the operations literature looks more at operational variables, such as capacity and inventory, as a response to various sources of process uncertainty that any firm faces. The preferences of individual customers, their valuations and the construction of the specific form of the uncertainty is less of a concern (although more recent literature emphasize this). Second, the findings in the operations literature usually have the objective of improving individual firms’ (and supply chains’) profits and operational efficiencies rather than one of dictating economic policy. Third, although non-cooperative models are the norm, there is also an underlying emphasis in the operations literature on cooperation between firms in a supply chain to improve the overall profit of the supply chain. This is probably because, unlike the levers traditionally studied in economics, many operational variables in a supply chain are often jointly decided between firms. The goal of this review taps on this last sentiment. We provide an overview of some of the basic multi-firm models studied in supply chain management. We look at how the literature uses non-cooperative game theory to analyze these models. We then look at how some of these models can be analyzed using a cooperative bargaining framework. We compare the modeling tools and the insights one obtains by taking this twofold approach. This process also allows us to discuss a few topics of interest such as the relative channel power of a firm, the relative merits of using a non-cooperative game versus cooperative bargaining to model a supply chain setting, etc. Finally, we conclude this review by exploring some issues that remain unresolved and are topics for future research.
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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 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".