49,528 research outputs found

    The Price Consideration Model of Brand Choice

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    The workhorse brand choice models in marketing are the multinomial logit (MNL) and nested multinomial logit (NMNL). These models place strong restrictions on how brand share and purchase incidence price elasticities are related. In this paper, we propose a new model of brand choice, the “price consideration” (PC) model, that allows more flexibility in this relationship. In the PC model, consumers do not observe prices in each period. Every week, a consumer decides whether to consider a category. Only then does he/she look at prices and decide whether and what to buy. Using scanner data, we show the PC model fits much better than MNL or NMNL. Simulations reveal the reason: the PC model provides a vastly superior fit to inter-purchase spells.Brand Choice; Purchase Incidence; Price Elasticity; Inter-purchase Spell

    LOGISTICS AND SUPPLY CHAIN STRATEGIES IN GRAIN EXPORTING

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    During the past decade, the grain shipping industry has become highly competitive and technologically advanced. These changes, along with the introduction of innovative shipping mechanisms, have made logistics management an important source of opportunity and risk for grain shippers. In this study, a stochastic simulation model was developed to evaluate the tradeoffs and effects of key variables on logistical performance in managing the grain supply chain. Average demurrage cost for the supply chain was $2.03 million with the greatest cost being for railcars and the least cost being for barges. Of the stochastic variables modeled, changes in export demand had the greatest impact on demurrage costs.Supply Chain, Grain Shipping, Logistics, Demurrage, Guaranteed Freight, Industrial Organization,

    Small Retailer Credit Sources

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    AdaNET Dynamic Software Inventory (DSI) prototype component acquisition plan

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    A component acquisition plan contains the information needed to evaluate, select, and acquire software and hardware components necessary for successful completion of the AdaNET Dynamic Software Inventory (DSI) Management System Prototype. This plan will evolve and be applicable to all phases of the DSI prototype development. Resources, budgets, schedules, and organizations related to component acquisition activities are provided. A purpose and description of a software or hardware component which is to be acquired are presented. Since this is a plan for acquisition of all components, this section is not applicable. The procurement activities and events conducted by the acquirer are described and who is responsible is identified, where the activity will be performed, and when the activities will occur for each planned procurement. Acquisition requirements describe the specific requirements and standards to be followed during component acquisition. The activities which will take place during component acquisition are described. A list of abbreviations and acronyms, and a glossary are contained

    A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters

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    Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In this paper, we criticize this model from another angle. Estimating the discrete finite-period model poses problems of indeterminacy and non-robustness: Arbitrarily fixing T leads to arbitrary control values and on the other hand estimating T from data adds an additional layer of indeterminacy. To counter this, we first propose an alternate finite-population model that avoids this problem of fixing T and allows a wider range of demand distributions, while retaining the useful marginal-value properties of the finite-period model. The finite-population model still requires jointly estimating market size and the parameters of the customer purchase model without observing no-purchases. Estimation of market-size when no-purchases are unobservable has rarely been attempted in the marketing or revenue management literature. Indeed, we point out that it is akin to the classical statistical problem of estimating the parameters of a binomial distribution with unknown population size and success probability, and hence likely to be challenging. However, when the purchase probabilities are given by a functional form such as a multinomial-logit model, we propose an estimation heuristic that exploits the specification of the functional form, the variety of the offer sets in a typical RM setting, and qualitative knowledge of arrival rates. Finally we perform simulations to show that the estimator is very promising in obtaining unbiased estimates of population size and the model parameters.Revenue management, estimation, multi-nomial logit, risk-ratio

    State Dependence and Alternative Explanations for Consumer Inertia

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    For many consumer packaged goods products, researchers have documented a form of state dependence whereby consumers become "loyal" to products they have consumed in the past. That is, consumers behave as though there is a utility premium from continuing to purchase the same product as they have purchased in the past or, equivalently, there is a psychological cost to switching products. However, it has not been established that this form of state dependence can be identified in the presence of consumer heterogeneity of an unknown form. Most importantly, before this inertia can be given a structural interpretation and used in policy experiments such as counterfactual pricing exercises,alternative explanations which might give rise to similar consumer behavior must be ruled out. We develop a flexible model of heterogeneity which can be given a semi-parametric interpretation and rule out alternative explanations for positive state dependence such as autocorrelated choice errors, consumer search, or consumer learning.

    The role of learning on industrial simulation design and analysis

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    The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose
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