Coordination of a Supply Chain with Satisficing Objectives Using Contracts
Bibliographic record
Abstract
Setting performance targets and managing to achieve them is fundamental to business success. As a result, it is common for managers to adopt a satisficing objective—that is, to maximize the probability of achieving some preset target profit level. This is especially true when companies are increasingly engaged in short-term relationships enabled by electronic commerce. In this chapter, our main focus is a decentralized supply chain consisting of a supplier and a retailer, both with the satisficing objective. The supply chain is examined under three types of commonly used contracts: wholesale price, buy back, and quantity flexibility contracts. Because a coordinating contract has to be Pareto optimal regardless of the bargaining powers among the agents, we first identify the Pareto-optimal contract(s) for each contractual form. Second, we identify the contractual forms that are capable of coordination of the supply chain with the satisficing objectives. In contrast to the well-known results for the supply chain with the objectives of expected profit maximization, we show that wholesale price contracts can coordinate the supply chain with the satisficing objectives, whereas buy back contracts cannot. Furthermore, quantity flexibility contracts have to degenerate into wholesale price contracts to coordinate the supply chain. This provides an important justification for the popularity of wholesale price contracts besides their simplicities and lower administration costs. Finally, we discuss possible extensions to the model by considering different types of objectives for different agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".