Decision, Operations & Information Technologies Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1588

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Start date: 2000End date: 2009

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Now showing 1 - 8 of 8
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    Note: An Application of the EOQ Model with Nonlinear Holding Cost to Inventory Management of Perishables
    (2005-07-19T20:53:40Z) Souza, Gilvan; Ferguson, Mark; Jayaraman, Vaidy
    We consider a variation of the economic order quantity (EOQ) model where cumulative holding cost is a nonlinear function of time. This problem has been studied by Weiss (1982), and we here show how it is an approximation of the optimal order quantity for perishable goods, such as milk, and produce, sold in small to medium size grocery stores where there are delivery surcharges due to infrequent ordering, and managers frequently utilize markdowns to stabilize demand as the product’s expiration date nears. We show how the holding cost curve parameters can be estimated via a regression approach from the product’s usual holding cost (storage plus capital costs), lifetime, and markdown policy. We show in a numerical study that the model provides significant improvement in cost vis-à-vis the classic EOQ model, with a median improvement of 40%. This improvement is more significant for higher daily demand rate, lower holding cost, shorter lifetime, and a markdown policy with steeper discounts.
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    A Large Deviations Analysis of Quantile Estimation with Application to Value at Risk
    (2005-07-01T12:31:49Z) Jin, Xing; Fu, Michael C.
    Quantile estimation has become increasingly important, particularly in the financial industry, where Value-at-Risk has emerged as a standard measurement tool for controlling portfolio risk. In this paper we apply the theory of large deviations to analyze various simulation-based quantile estimators. First, we show that the coverage probability of the standard quantile estimator converges to one exponentially fast with sample size. Then we introduce a new quantile estimator that has a provably faster convergence rate. Furthermore, we show that the coverage probability for this new estimator can be guaranteed to be 100% with sufficiently large, but finite, sample size. Numerical experiments on a VaR example illustrate the potential for dramatic variance reduction.
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    Multi-Echelon Models for Repairable Items: A Review
    (2005-07-01T12:31:37Z) Diaz, Angel; Fu, Michael C.
    We review multi-echelon inventory models for repairable items. Such models have been widely applied to the management of critical spare parts for military equipment for around three decades, but the application to manufacturing and service industries seems to be much less documented. We feel that the appropriate use of models in the management of spare parts for heavily utilized equipment in industry can result in significant cost savings, in particular in those settings where repair facilities are resource constrained. In our review, we provide a strategic framework for making these decisions, place the modeling problem in the broader context of inventory control, and review the prominent models in the literature under a unified setting, highlighting some key relationships. We concentrate on describing those models which we feel are most applicable for practical application, revisiting in detail the Multi-Echelon Technique for Recoverable Item Control (METRIC) model and its variations, and then discussing a variety of more general queueing models. We then discuss the components which we feel must be addressed in the models in order to apply them practically to industrial settings.
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    Stochastic Gradient Estimation
    (2005-07-01T12:31:02Z) Fu, Michael C.
    We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives.
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    Supply Chain Coordination for False Failure Returns
    (2005-04-11T13:17:49Z) Souza, Gilvan; Ferguson, Mark; Guide, V. Daniel, Jr.
    False failure returns are products that are returned by consumers to retailers with no functional or cosmetic defect. The cost of a false failure return includes the processing actions of testing, refurbishing if necessary, repackaging, the loss in value during the time the product spends in the reverse supply chain (a time that can exceed several months for many firms), and the loss in revenue because the product is sold at a discounted price. This cost is significant, and is incurred primarily by the manufacturer. Reducing false failure returns, however, requires effort primarily by the retailer, for example informing consumers about the exact product that best fits their needs. We address the problem of reducing false failure returns via supply chain coordination methods. Specifically, we propose a target rebate contract that pays the retailer a specific dollar amount per each unit of false failure returns below a target. This target rebate provides an incentive to the retailer to increase her effort, thus decreasing the number of false failures and (potentially) increasing net sales. We show that this contract is Pareto–improving in the majority of cases. Our results also indicate that the profit improvement to both parties, and the supply chain, is substantial.
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    Time Value of Commercial Product Returns
    (2005-01-13T15:06:27Z) Souza, Gilvan; Guide, V. Daniel; Van Wassenhove, Luk; Blackburn, Joseph
    Manufacturers and their distributors must cope with an increased flow of returned products from their customers. The value of commercial product returns, which we define as products returned for any reason within 90 days of sale, now exceeds US $100 billion annually in the US. Although the reverse supply chain of returned products represents a sizeable flow of potentially recoverable assets, only a relatively small fraction of the value is currently extracted by manufacturers; a large proportion of the product value erodes away due to long processing delays. Thus, there are significant opportunities to build competitive advantage from making the appropriate reverse supply chain design choices. In this paper, we present a simple queuing network model that includes the marginal value of time to identify the drivers of reverse supply chain design. We illustrate our approach with specific examples from two companies in different industries and then examine how industry clockspeed generally affects the choice between an efficient and a responsive returns network.
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    The Optimal Pace of Product Updates
    (2005-01-13T15:05:47Z) Souza, Gilvan; Druehl, Cheryl; Schmidt, Glen
    Some firms (such as Intel and Medtronics) use a time–pacing strategy for new product development, introducing new generations at regular intervals. If the firm adopts a fast pace (introducing frequently) then it prematurely cannibalizes its old generation and incurs high development costs, while if it waits too long, it fails to capitalize on customer willingness–to–pay for more advanced technology. We develop a model to gain insight into which factors drive the pace. We consider the degree to which a new generation stimulates market growth, the rate at which it diffuses (its coefficients of innovation and imitation), the rate of decline in its margin over time, and the cost of new product development. The optimization problem is non–concave; however we are able to solve it numerically for a wide range of parameters because there is a finite number of possible solutions for each case. Somewhat intuitively, we find that a faster pace is associated with a higher market growth rate and faster margin decay. Not so intuitively, we find that relatively minor differences in the new product development cost function can significantly impact the optimal pace. Regarding the Bass coefficients of innovation and imitation, we find that a higher sum of these coefficients leads to a faster pace but with diminishing effects, and that for relatively higher sums the coefficients are effectively substitutes.
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    Finding the Value of Information About a State Variable in a Markov Decision Process
    (2005-01-13T15:04:40Z) Souza, Gilvan
    In this paper we present a mixed–integer programming formulation that computes the optimal solution for a certain class of Markov decision processes with finite state and action spaces, where a state is comprised of multiple state variables, and one of the state variables is unobservable to the decision maker. Our approach is a much simpler modeling alternative to the theory of partially observable Markov decision processes (POMDP), where an information and updating structure about the decision variable needs to be defined. We illustrate the approach with an example of a duopoly where one firm’s actions are not immediately observable by the other firm, and present computational results. We believe that this approach can be used in a variety of applications, where the decision maker wants to assess the value of information about an additional decision variable.