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Demand Distribution (demand + distribution)
Selected AbstractsChannel Coordination for a Supply Chain with a Risk-Neutral Manufacturer and a Loss-Averse Retailer,DECISION SCIENCES, Issue 3 2007Charles X. Wang ABSTRACT This articles considers a decentralized supply chain in which a single manufacturer is selling a perishable product to a single retailer facing uncertain demand. It differs from traditional supply chain contract models in two ways. First, while traditional supply chain models are based on risk neutrality, this article takes the viewpoint of behavioral principal,agency theory and assumes the manufacturer is risk neutral and the retailer is loss averse. Second, while gain/loss (GL) sharing is common in practice, there is a lack of analysis of GL-sharing contracts in the supply chain contract literature. This article investigates the role of a GL-sharing provision for mitigating the loss-aversion effect, which drives down the retailer order quantity and total supply chain profit. We analyze contracts that include GL-sharing-and-buyback (GLB) credit provisions as well as the special cases of GL contracts and buyback contracts. Our analytical and numerical results lend insight into how a manufacturer can design a contract to improve total supply chain, manufacturer, and retailer performance. In particular, we show that there exists a special class of distribution-free GLB contracts that can coordinate the supply chain and arbitrarily allocate the expected supply chain profit between the manufacturer and retailer; in contrast with other contracts, the parameter values for contracts in this class do not depend on the probability distribution of market demand. This feature is meaningful in practice because (i) the probability distribution of demand faced by a retailer is typically unknown by the manufacturer and (ii) a manufacturer can offer the same contract to multiple noncompeting retailers that differ by demand distribution and still coordinate the supply chains. [source] Decision Making in a Standby Service System,DECISION SCIENCES, Issue 3 2000H. V. Ravinder A standby service option allows a firm to lower its risk of not having sufficient capacity to satisfy demand without investing in additional capacity. Standby service options currently exist in the natural gas, electric, and water utility industries. Firms seeking standby service are typically large industrial or institutional organizations that, due to unexpectedly high demand or interruptions in their own supply system, look to a public utility to supplement their requirements. Typically, the firm pays the utility a reservation fee based on a nominated volume and a consumption charge based on the volume actually taken. In this paper, a single-period model is developed and optimized with respect to the amount of standby capacity a firm should reserve. Expressions for the mean and variance of the supplier's aggregate standby demand distribution are developed. A procedure for computing the level of capacity needed to safely meet its standby obligations is presented. Numerical results suggest that the standby supplier can safely meet its standby demand with a capacity that is generally between 20 to 50% of the aggregate nominated volume. [source] Optimal operating policies in a commodity trading market with the manufacturer's presenceNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 2 2010Hui Zhao Abstract With the help of the Internet and express delivery at relatively low costs, trading markets have become increasingly popular as a venue to sell excess inventory and a source to obtain products at lower prices. In this article, we study the operational decisions in the presence of a trading market in a periodic-review, finite-horizon setting. Prices in the trading market change periodically and are determined endogenously by the demand and supply in the market. We characterize the retailers'optimal ordering and trading policies when the original manufacturer and the trading market co-exist and retailers face fees to participate in the trading market. Comparing with the case with no trading fees, we obtain insights into the impact of trading fees and the fee structure on the retailers and the manufacturer. Further, we find that by continually staying in the market, the manufacturer may use her pricing strategies to counter-balance the negative impact of the trading market on her profit. Finally, we extend the model to the case when retailers dynamically update their demand distribution based on demand observations in previous periods. A numerical study provides additional insights into the impact of demand updating in a trading market with the manufacturer's competition. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010 [source] Revenue sharing contracts in a supply chain with uncontractible actionsNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 5 2008Albert Y. Ha Abstract We consider a supplier,customer relationship where the customer faces a typical Newsvendor problem of determining perishable capacity to meet uncertain demand. The customer outsources a critical, demand-enhancing service to an outside supplier, who receives a fixed share of the revenue from the customer. Given such a linear sharing contract, the customer chooses capacity and the service supplier chooses service effort level before demand is realized. We consider the two cases when these decisions are made simultaneously (simultaneous game) or sequentially (sequential game). For each game, we analyze how the equilibrium solutions vary with the parameters of the problem. We show that in the equilibrium, it is possible that either the customer's capacity increases or the service supplier's effort level decreases when the supplier receives a larger share of the revenue. We also show that given the same sharing contract, the sequential game always induces a higher capacity and more effort. For the case of additive effort effect and uniform demand distribution, we consider the customer's problem of designing the optimal contract with or without a fixed payment in the contract, and obtain sensitivity results on how the optimal contract depends on the problem parameters. For the case of fixed payment, it is optimal to allocate more revenue to the supplier to induce more service effort when the profit margin is higher, the cost of effort is lower, effort is more effective in stimulating demand, the variability of demand is smaller or the supplier makes the first move in the sequential game. For the case of no fixed payment, however, it is optimal to allocate more revenue to the supplier when the variability of demand is larger or its mean is smaller. Numerical examples are analyzed to validate the sensitivity results for the case of normal demand distribution and to provide more managerial insights. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008 [source] Optimal booking limits in the presence of strategic consumer behaviorINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 2 2006John G. Wilson Abstract We consider a two-period airline yield management problem where customers may act strategically. Specifically, we allow for the possibility that a customer may decide to defer purchase in the hope that a ticket cheaper than those currently on offer will become available. We also allow for the possibility that some customers will buy a more expensive ticket if the cheaper tickets are not available. We show how to find optimal booking limits in the presence of such strategic customer behavior. We also explicitly incorporate the fact that, once a booking limit has been reached, demand distributions are now censored distributions. [source] Optimal feeder bus routes on irregular street networksJOURNAL OF ADVANCED TRANSPORTATION, Issue 2 2000Steven Chien The methodology presented here seeks to optimize bus routes feeding a major intermodal transit transfer station while considering intersection delays and realistic street networks. A model is developed for finding the optimal bus route location and its operating headway in a heterogeneous service area. The criterion for optimality is the minimum total cost, including supplier and user costs. Irregular and discrete demand distributions, which realistically represent geographic variations in demand, are considered in the proposed model. The optimal headway is derived analytically for an irregularly shaped service area without demand elasticity, with non-uniformly distributed demand density, and with a many-to-one travel pattern. Computer programs are designed to analyze numerical examples, which show that the combinatory type routing problem can be globally optimized. The improved computational efficiency of the near-optimal algorithm is demonstrated through numerical comparisons to an optimal solution obtained by the exhaustive search (ES) algorithm. The CPU time spent by each algorithm is also compared to demonstrate that the near-optimal algorithm converges to an acceptable solution significantly faster than the ES algorithm. [source] Nash bargaining over allocations in inventory pooling contractsNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 6 2008Eran Hanany Abstract When facing uncertain demand, several firms may consider pooling their inventories leading to the emergence of two key contractual issues. How much should each produce or purchase for inventory purposes? How should inventory be allocated when shortages occur to some of the firms? Previously, if the allocations issue was considered, it was undertaken through evaluation of the consequences of an arbitrary priority scheme. We consider both these issues within a Nash bargaining solution (NBS) cooperative framework. The firms may not be risk neutral, hence a nontransferable utility bargaining game is defined. Thus the physical pooling mechanism itself must benefit the firms, even without any monetary transfers. The firms may be asymmetric in the sense of having different unit production costs and unit revenues. Our assumption with respect to shortage allocation is that a firm not suffering from a shortfall, will not be affected by any of the other firms' shortages. For two risk neutral firms, the NBS is shown to award priority on all inventory produced to the firm with higher ratio of unit revenue to unit production cost. Nevertheless, the arrangement is also beneficial for the other firm contributing to the total production. We provide examples of Uniform and Bernoulli demand distributions, for which the problem can be solved analytically. For firms with constant absolute risk aversion, the agreement may not award priority to any firm. Analytically solvable examples allow additional insights, e.g. that higher risk aversion can, for some problem parameters, cause an increase in the sum of quantities produced, which is not the case in a single newsvendor setting. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008 [source] THE VALUE OF SKU RATIONALIZATION IN PRACTICE (THE POOLING EFFECT UNDER SUBOPTIMAL INVENTORY POLICIES AND NONNORMAL DEMAND)PRODUCTION AND OPERATIONS MANAGEMENT, Issue 1 2003JOSÉ A. ALFARO Several approaches to the widely recognized challenge of managing product variety rely on the pooling effect. Pooling can be accomplished through the reduction of the number of products or stock-keeping units (SKUs), through postponement of differentiation, or in other ways. These approaches are well known and becoming widely applied in practice. However, theoretical analyses of the pooling effect assume an optimal inventory policy before pooling and after pooling, and, in most cases, that demand is normally distributed. In this article, we address the effect of nonoptimal inventory policies and the effect of nonnormally distributed demand on the value of pooling. First, we show that there is always a range of current inventory levels within which pooling is better and beyond which optimizing inventory policy is better. We also find that the value of pooling may be negative when the inventory policy in use is suboptimal. Second, we use extensive Monte Carlo simulation to examine the value of pooling for nonnormal demand distributions. We find that the value of pooling varies relatively little across the distributions we used, but that it varies considerably with the concentration of uncertainty. We also find that the ranges within which pooling is preferred over optimizing inventory policy generally are quite wide but vary considerably across distributions. Together, this indicates that the value of pooling under an optimal inventory policy is robust across distributions, but that its sensitivity to suboptimal policies is not. Third, we use a set of real (and highly erratic) demand data to analyze the benefits of pooling under optimal and suboptimal policies and nonnormal demand with a high number of SKUs. With our specific but highly nonnormal demand data, we find that pooling is beneficial and robust to suboptimal policies. Altogether, this study provides deeper theoretical, numerical, and empirical understanding of the value of pooling. [source] | |||||||||||||