R K Amit

In this paper, we develop an optimal shelf-space stocking policy when demand, in addition to the exogenous uncertainty, is influenced by the amount of inventory displayed (supply) on the shelves. Our model exploits stochastic dominance condition; and, we assume that the distribution of realized demand with higher stocking level stochastically dominates the distribution of realized demand with lower stocking level. We show that the critical fractile with endogenous demand may not exceed the critical fractile of the classical newsvendor model. Our computational results validate the optimality of amount of units stocked on the retail shelves.

In this paper, we consider a supply chain coordination problem when demand faced by a retailer is influenced by the amount of inventory displayed on the retail shelf. We assume that shelf space inventory is used as one of the levers to stimulate demand. Our objective in this research is to design individually rational contracts that coordinate the supply chain when the retailer faces inventory-level-dependent demand. We consider a buyback contract where any leftover inventory at the retailer can be returned to the supplier at a pre-specified terms of the buyback contract. The existing buyback contracts in the supply chain coordination literature do not guarantee the satisfaction of individual rationality constraint. A continuum of buyback contracts coordinate the supply chain. The contracts may differ on the basis of division of profits resulting in contracts that may not be individually rational. This motivates us to use the Shapley value from the cooperative game theory which ensures fairness and individual rationality in the buyback contract. We also provide managerial insights into the design of the contracts and analyze the impact of shelf space inventory on the contract parameters. © 2012 Elsevier B.V.

In this study, we model the classical newsvendor ordering preferences under ambiguity. The extant literature on normative models in the newsvendor setting assumes decision-making under risk, where decision-maker has exact knowledge of the probabilities associated with the outcomes. In several business situations, the demand distribution is often incomplete or unknown. This results in decision-making under ambiguous situations. Decision theory recognizes the difference between exact probabilities and more realistic ambiguous probabilities. In his seminal paper, Scarf (1958) develops a max-min approach for the newsvendor with incomplete demand information. In the Scarf model, the newsvendor is assumed to be risk-neutral and ambiguity averse. In the recent experimental literature, it has been observed that the newsvendor behavior is not consistent with the Scarf model, and exhibits pull-to- center bias and other biases. This motivates our research to develop quantitative models under ambiguity to describe the observed biases in the literature.