236 research outputs found

    Implementation of the Newsvendor Model with Clearance Pricing: How to (and How Not to) Estimate a Salvage Value

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    The newsvendor model is designed to decide how much of a product to order when the product is to be sold over a short selling season with stochastic demand and there are no additional opportunities to replenish inventory. There are many practical situations that reasonably conform to those assumptions, but the traditional newsvendor model also assumes a fixed salvage value: all inventory left over at the end of the season is sold off at a fixed per-unit price. The fixed salvage value assumption is questionable when a clearance price is rationally chosen in response to the events observed during the selling season: a deep discount should be taken if there is plenty of inventory remaining at the end of the season, whereas a shallow discount is appropriate for a product with higher than expected demand. This paper solves for the optimal order quantity in the newsvendor model, assuming rational clearance pricing. We then study the performance of the traditional newsvendor model. The key to effective implementation of the traditional newsvendor model is choosing an appropriate fixed salvage value. (We show that an optimal order quantity cannot be generally achieved by merely enhancing the traditional newsvendor model to include a nonlinear salvage value function.) We demonstrate that several intuitive methods for estimating the salvage value can lead to an excessively large order quantity and a substantial profit loss. Even though the traditional model can result in poor performance, the model seems as if it is working correctly: the order quantity chosen is optimal given the salvage value inputted to the model, and the observed salvage value given the chosen order quantity equals the inputted one. We discuss how to estimate a salvage value that leads the traditional newsvendor model to the optimal or near-optimal order quantity. Our results highlight the importance of understanding how a model can interact with its own inputs: when inputs to a model are influenced by the decisions of the model, care is needed to appreciate how that interaction influences the decisions recommended by the model and how the model’s inputs should be estimated

    Information and decentralization in inventory, supply chain, and transportation systems

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.Includes bibliographical references (p. 199-213).This thesis investigates the impact of lack of information and decentralization of decision-making on the performance of inventory, supply chain, and transportation systems. In the first part of the thesis, we study two extensions of a classic single-item, single-period inventory control problem: the "newsvendor problem." We first analyze the newsvendor problem when the demand distribution is only partially specified by some moments and shape parameters. We determine order quantities that are robust, in the sense that they minimize the newsvendor's maximum regret about not acting optimally, and we compute the maximum value of additional information. The minimax regret approach is scalable to solve large practical problems, such as those arising in network revenue management, since it combines an efficient solution procedure with very modest data requirements. We then analyze the newsvendor problem when the inventory decision-making is decentralized. In supply chains, inventory decisions often result from complex negotiations among supply partners and might therefore lead to a loss of efficiency (in terms of profit loss).(cont.) We quantify the loss of efficiency of decentralized supply chains that use price-only contracts under the following configurations: series, assembly, competitive procurement, and competitive distribution. In the second part of the thesis, we characterize the dynamic nature of traffic equilibria in a transportation network. Using the theory of kinematic waves, we derive an analytical model for traffic delays capturing the first-order traffic dynamics and the impact of shock waves. We then incorporate the travel-time model within a dynamic user equilibrium setting and illustrate how the model applies to solve a large network assignment problem.by Guillaume Roels.Ph.D

    Signaling to Partially Informed Investors in the Newsvendor Model

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    We analyze a signaling game between the manager of a firm and an investor in the firm. The manager has private information about the firm\u27s demand and cares about the short-term stock price assigned by the investor. Previous research has shown that under continuous decision choices and the Intuitive Criterion refinement, the least-cost separating equilibrium will result, in which a low-quality firm chooses its optimal capacity and a high-quality firm over-invests in order to signal its quality to investors. We build on this research by showing the existence of pooling outcomes in which low-quality firms over-invest and high-quality firms under-invest so as to provide identical signals to investors. The pooling equilibrium is practically appealing because it yields a Pareto improvement compared to the least-cost separating equilibrium. Distinguishing features of our analysis are that: (i) we allow the capacity decision to have either discrete or continuous support, and (ii) we allow beliefs to be refined based on either the Undefeated refinement or the Intuitive Criterion refinement. We find that the newsvendor model parameters impact the likelihood of a pooling outcome, and this impact changes in both sign and magnitude depending on which refinement is used

    Inventory dynamics and the bullwhip effect : studies in supply chain performance

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    COOPERATION OR COMPETITION: A STUDY OF SOCIAL CAPITAL AND PRODUCTION DECISION UNDER POTENTIAL VERTICAL COMPETITION

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    Since the 2000s when retailers recognised the huge market potential, the growth of private labels has been unstoppable worldwide. As a result of the recession of national brands, manufacturers are in a relatively weaker position when dealing with large retailers. The relationship between manufacturers and retailers has transformed from pure cooperation to a delicate balance of cooperation and competition. Yet, how such a balance influences supply chain dynamics is an intriguing and overdue issue. This thesis explores the influence of social capital over manufacturers’ perceptions regarding their retailers’ trustworthiness in the presence of potential vertical competition, as well as the consequential performance from the perspective of cognitive abilities. Data was collected through an online scenario-based role play (SBRP) experiment, where 371 participants were recruited and put in three groups. In each group, participants were provided with a scenario depicting the product substitution level between a newly launched private label and a national brand. The data was analysed statistically to test the hypotheses. The results identify relational capital as the most influential dimension of social capital in suppressing manufacturer’s perception of opportunistic information sharing behaviour from retailers, and suggest that such suppression is moderated by the level of product substitution between private labels and national brands. This thesis has reference value to academia by looking into the overlapping issues of supply chain management and marketing and providing empirical evidence of the influences induced by the introduction of private labels. It also benefits industry, especially manufacturers, by giving a brief standard regarding whether to cooperate or compete when faced with potential vertical competition with retailers

    Computing (R, S) policies with correlated demand

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    This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good
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