experience based dynamic choice a revealed preference approach abstract we use the revealed preference method to derive a model of dynamic choice where the agent s past experience may influence her current decisions our model generalizes the classical individual choice model which is rationalized by utility maximization and reduces to that model in the absence of experience as the agent gains experience her utility changes but only in a very restricted fashion every period after an alternative is chosen the utility of that and only that alternative may change while the utility of all other alternatives remains fixed the model provides a platform on which many behavioral dynamic phenomena may be examined we utilize it and look into the behavioral implications of bounded memory status quo bias and variety seeking

ordinally bayesian incentive compatible voting rules we study strategic voting after weakening the notion of strategy proofness to ordinal bayesian incentive compatibility obic under obic truth telling is required to maximize the expected utility of every voter expected utility being computed with respect to the voter s prior beliefs and under the assumption that everybody else is also telling the truth we show that for a special type of priors i e the uniform priors there exists a large class of social choice functions that are obic however for priors that are generic in the set of independent beliefs a social choice function is obic only if it is dictatorial this result underlines the robustness of the gibbard satterthwaite theorem

integrating replenishment decisions with advance demand information there is a growing consensus that a portfolio of customers with different demand lead times can lead to higher more regular revenues and better capacity utilization customers with positive demand lead times place orders in advance of their needs resulting inadvance demand information this gives rise to the problem of finding effective inventory control policies under advance demand information we show that state dependent s s and base stock policies are optimal for stochastic inventory systems with and without fixed costs the state of the system reflects our knowledge of advance demand information we also determine conditions under which advance demand information has no operational value a numerical study allows us to obtain additional insights and to evaluate strategies to induce advance demand information

impact of information sharing and lead time on bullwhip effect and on hand inventory abstract this work analyzes a two echelon warehouse retailer serial supply chain to study the impact of information sharing is and lead time on bullwhip effect and on hand inventory the customer demand at the retailer is assumed to be an autoregressive ar 1 process both the echelons use a minimum mean squared error mmse model for forecasting lead time demand ltd and follow an adaptive base stock inventory policy to determine their respective order quantities for the cases of without is and inter as well as intra echelon is expressions for the bullwhip effect and on hand inventory for the warehouse are obtained considering deterministic lead time the results are compared with the previous research work and an easy analysis of the various bullwhip effect expressions under different scenarios is done to understand the impact of is on the bullwhip effect phenomenon it is shown that some part of bullwhip effect will always remain even after sharing both inter as well as intra echelon information further with the help of a numerical example it is shown that the lead time reduction is more beneficial in comparison to the sharing of information in terms of reduction in the bullwhip effect phenomenon

on the interaction between forecasting and stock control the case of non stationary demand the effect of using estimated forecast demand parameters on the performance of an inventory control system is an intriguing and important subject recent research has been undertaken on this phenomenon assuming stationary demand data in this paper we extend the research to non stationary demands by means of simulation the case of a periodic order up to level inventory system is considered and the experimental structure allows us to evaluate in a progressive manner the accumulated effect of using the optimal forecasting method optimal forecast parameters and correct variance expression procedures the results allow insights to be gained into operational issues and demonstrate the scope for improving stock control systems

dynamic analysis of consumer response to marketing strategies this paper develops a methodology for modeling consumer response that integrates previous research in stochastic brand selection diffusion of innovation test market analysis and new product design the methodology makes it practical to extend brand selection models to include diffusion phenomena such as awareness trial and information flow purchase timing and brand selection are interdependent and both phenomena depend jointly on managerial controls such as advertising coupons price off promotion product positioning and consumer characteristics within this general structure we provide practical estimation procedures a least squares approximation to the maximum likelihood estimates to determine the parameters which link managerial controls to consumer response closed form solutions are derived for cumulative awareness cumulative trial penetration expected sales and purchases due to promotion all as a function of time we also provide simplified expressions for equilibrium t market share tradeoffs among complexity of the diffusion process number of managerial variables nonstationarity complexity of purchase timing consumer segmentation and sample size are made explicit so that the marketing scientist can customize his analyses to the managerial problems that he faces the effects of sample size data interval frequency and collinearity in the explanatory variables are investigated with simulations based on a five state consumer response process which depends on 8 10 marketing variables the paper closes with a brief description of the application and predictive test of a consumer response model based on the methodology

optimal policies and approximations for a serial multiechelon inventory system with time correlated demand since clark and scarf s pioneering work most advances in multiechelon inventory systems have been based on demand processes that are time independent this paper revisits the serial multiechelon inventory system of clark and scarf and develops three key results first we provide a simple lower bound approximation to the optimal echelon inventory levels and an upper bound to the total system cost for the basic model of clark and scarf second we show that the structure of the optimal stocking policy of clark and scarf holds under time correlated demand processes using a martingale model of forecast evolution third we extend the approximation to the time correlated demand process and study in particular for an autoregressive demand model the impact of lead times and autocorrelation on the performance of the serial inventory system

modeling multiattribute utility risk and belief dynamics for new consumer durable brand choice this paper proposes a brand choice model to aid in the prelaunch management of a new consumer durable entry in an existing category the model contributes to theory by integrating the critical phenomena of multiattribute preference risk and dynamics in an individual level expected utility framework the integration is based on established theoretical constructs in utility bayesian decision analysis and discrete choice theory measurement and estimation procedures are presented an application is described and the managerial relevance of this work as a planning and forecasting tool is examined

a time series framework for supply chain inventory management we consider a supply chain in which the underlying demand process can be described in a linear state space form inventory is managed at various points of the chain members based on local information that each member observes and continuously updates the key feature of our model is that it takes into account the ability of the members to observe subsets of the underlying state vector and adopt their forecasting and replenishment policies accordingly this enables us to model situations in which the members are privy to certain explanatory variables of the demand with the latter possibly evolving according to a vector autoregressive time series for each member we identify an associated demand evolution model for which we propose an adaptive inventory replenishment policy that utilizes the kalman filter technique we then provide a simple methodology for assessing the benefits of various types of information sharing agreements between members of the supply chain we also discuss inventory positioning and cost performance assessment in the supply chain our performance metrics and inventory target levels are usually presented in matrix forms allowing them to accommodate a relatively large spectrum of linear demand models and making them simple to implement several illustrations for possible applications of our models are provided

decision making under uncertainty capturing dynamic brand choice processes in turbulent consumer goods markets we construct two models of the behavior of consumers in an environment where there is uncertainty about brand attributes in our models both usage experience and advertising exposure give consumers noisy signals about brand attributes consumers use these signals to update their expectations of brand attributes in a bayesian manner the two models are 1 a dynamic model with immediate utility maximization and 2 a dynamic forward looking model in which consumers maximize the expected present value of utility over a planning horizon given this theoretical framework we derive from the bayesian learning framework how brand choice probabilities depend on past usage experience and advertising exposures we then form likelihood functions for the models and estimate them on nielsen scanner data for detergent we find that the functional forms for experience and advertising effects that we derive from the bayesian learning framework fit the data very well relative to flexible ad hoc functional forms such as exponential smoothing and also perform better at out of sample prediction another finding is that in the context of consumer learning of product attributes although the forward looking model fits the data statistically better at conventional significance levels both models produce similar parameter estimates and policy implications our estimates indicate that consumers are risk averse with respect to variation in brand attributes which discourages them from buying unfamiliar brands using the estimated behavioral models we perform various scenario evaluations to find how changes in marketing strategy affect brand choice both in the short and long run a key finding obtained from the policy experiments is that advertising intensity has only weak short run effects but a strong cumulative effect in the long run the substantive content of the paper is potentially of interest to academics in marketing economics and decision sciences as well as product managers marketing research managers and analysts interested in studying the effectiveness of marketing mix strategies our paper will be of particular interest to those interested in the long run effects of advertising note that our estimation strategy requires us to specify explicit behavioral models of consumer choice behavior derive the implied relationships among choice probabilities past purchases and marketing mix variables and then estimate the behavioral parameters of each model such an estimation strategy is referred to as structural estimation and econometric models that are based explicitly on the consumer s maximization problem and whose parameters are parameters of the consumers utility functions or of their constraints are referred to as structural models a key benefit of the structural approach is its potential usefulness for policy evaluation the parameters of structural models are invariant to policy that is they do not change due to a change in the policy in contrast the parameters of reduced form brand choice models are in general functions of marketing strategy variables e g consumer response to price may depend on pricing policy as a result the predictions of reduced form models for the outcomes of policy experiments may be unreliable because in making the prediction one must assume that the model parameters are unaffected by the policy change since the agents in our models choose among many alternative brands their choice probabilities take the form of higher order integrals we employ monte carlo methods to approximate these integrals and estimate our models using simulated maximum likelihood estimation of the dynamic forward looking model also requires that a dynamic programming problem be solved in order to form the likelihood function for this we use a new approximation method based on simulation and interpolation techniques these estimation techniques may be of interest to researchers and policy makers in many fields where dynamic choice among discrete alternatives is important such as marketing decision sciences labor and health economics and industrial organization

analysis of the bullwhip effect in a multiproduct setting with interdependent demands the bullwhip effect has been extensively studied primarily based on the analysis of various single product models with a few exceptions we extend the single product analysis to the multiproduct setting of interdependent demand streams with auto correlation and cross product correlation as well as contemporaneous correlation across forecasting errors we find that interdependency between demand streams plays a critical role in determining the existence and magnitude of the bullwhip effect specifically we consider two operating environments a the firm orders product specific materials so that the ordering decision is based on the product level and b the firm orders generic materials so that the order decision is based on the category level we show that even with demand pooling a firm operating at the category level can experience a larger bullwhip effect and a larger order variance under certain conditions that depend on the number of products in the category and the demand dependencies

market boundaries and product choice illustrating attraction and substitution effects what happens to the share of choices each item receives when the choice set boundaries are extended by adding a new item that is extremely good on one dimension but poor on the others first there is a substitution effect whereby the new item takes choice share mainly from similar items in the set second there is an attraction effect resulting in a general shift of preference toward the added item experimental studies show that choice patterns conflict with current theoretical and common sense ideas about the effect of added alternatives on choice

bayes solution to dynamic inventory models under unknown demand distribution this paper considers the periodic review inventory problem for which one or more parameters of the demand distribution are unknown with a known prior distribution chosen from the natural conjugate

value of information in capacitated supply chains we incorporate information flow between a supplier and a retailer in a two echelon model that captures the capacitated setting of a typical supply chain we consider three situations 1 a traditional model where there is no information to the supplier prior to a demand to him except for past data 2 the supplier knows the s s policy used by the retailer as well as the end item demand distribution and 3 the supplier has full information about the state of the retailer order up to policies continue to be optimal for models with information flow for the finite horizon the infinite horizon discounted and the infinite horizon average cost cases study of these three models enables us to understand the relationships between capacity inventory and information at the supplier level as well as how they are affected by the retailer s s s values and end item demand distribution we estimate the savings at the supplier due to information flow and study when information is most beneficial

inventory control in a fluctuating demand environment we present an inventory model where the demand rate varies with an underlying state of the world variable this variable can represent economic fluctuations or stages in the product life cycle for example we derive some basic characteristics of optimal policies and develop algorithms for computing them in addition we show that certain monotonicity patterns in the problem data are reflected in the optimal policies

a multiattribute model of consumer choice during product learning the processes of consumer preference formation during product learning are analyzed a hypothesis about how individuals form attribute expectations is used to derive a dynamic multinomial logit model of individual choice which endogenously recognizes product learning the model and its underlying assumptions are then tested in the context of an interactive grocery store learning game the results support most elements of the proposed model structure an assumption of temporal stationarity in attribute salience however could not be supported approaches to implementation of the model are discussed and implications for marketing management and research in individual choice modeling are addressed

information distortion in a supply chain the bullwhip effect this article originally appeared in management science april 1997 volume 43 number 4 pp 546 558 published by the institute of management sciences consider a series of companies in a supply chain each of whom orders from its immediate upstream member in this setting inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions this paper claims that the information transferred in the form of orders tends to be distorted and can misguide upstream members in their inventory and production decisions in particular the variance of orders may be larger than that of sales and distortion tends to increase as one moves upstream a phenomenon termed bullwhip effect this paper analyzes four sources of the bullwhip effect demand signal processing rationing game order batching and price variations actions that can be taken to mitigate the detrimental impact of this distortion are also discussed

on bullwhip in a family of order up to policies with arma 2 2 demand and arbitrary lead times a number of papers have recently appeared that investigate the bullwhip effect the variance amplification of ordering decisions in the supply chain produced by the order up to replenishment policy an adapted policy with a proportional inventory position feedback controller has shown improved bullwhip behaviour the dynamic behaviour of this so called proportional order up to policy has been investigated for arbitrary lead times and several demand models such as i i d demand and autoregressive moving average ar 1 and arma 1 1 models it has been shown that for a correct choice of the feedback parameter the bullwhip effect can always be avoided however less attractive properties of this policy have also become clear herein we investigate the behaviour of the proportional order up to policy for arma 2 2 demand with arbitrary lead times in order to compensate for possible weaknesses of the proportional out policy we propose another replenishment rule that accounts for the characteristics of the demand in a superior manner the characteristics of both policies are compared for several parameter settings of the arma 2 2 model finally the consequences of our full state feedback order up to policy are discussed

managing inventory with the prospect of obsolescence how should inventory management respond when there is a possibility of imminent obsolescence or more generally deteriorating demand we use an inventory control model to address this question the model incorporates a markovian submodel to describe the uncertain events leading to obsolescence these events and their uncertainties come in a variety of patterns we devote considerable attention to specifying the submodel and we compare a few alternatives numerically also we compare optimal policies to simpler alternatives and we investigate the response of the model to parameter changes generally we find that obsolescence does or should have a substantial impact in the way inventories are managed the nature of these effects moreover is fairly intricate it appears that obsolescence cannot be captured in a simpler model through parameter adjustments these conclusions presume that we cannot dispose of excess stock either directly or through price promotions we show also that the disposal option can make the problems of obsolescence more manageable

on the prediction power of market share attraction models logical consistency of market share models has received considerable attention in the recent marketing literature the reports suggest that at least theoretically attraction type specifications a

inventories and the volatility of production a stylized fact associated with inventory behavior is that the variance of production exceeds the variance of sales this paper presents a model of production decisions with demand uncertainty that incorporates no nnegativity constraints on inventories even with no productivity shocks optimal behavior by the firm is consistent with this stylized fact either if demand exhibits positive serial correlation or if the firm can backlog excess demand the reason is that a demand shock affects both the actual inventory level given unchanged production and the desired level production must replenish the stock and adjust to the new desired level copyright 1987 by american economic association

dynamic models incorporating individual heterogeneity utility evolution in conjoint analysis it has been shown in the behavioral decision making marketing research and psychometric literature that the structure underlying preferences can change during the administration of repeated measurements e g conjoint analysis and data collection because of effects from learning fatigue boredom and so on in this research note we propose a new class of hierarchical dynamic bayesian models for capturing such dynamic effects in conjoint applications which extend the standard hierarchical bayesian random effects and existing dynamic bayesian models by allowing for individual level heterogeneity around an aggregate dynamic trend using simulated conjoint data we explore the performance of these new dynamic models incorporating individual level heterogeneity across a number of possible types of dynamic effects and demonstrate the derived benefits versus static models in addition we introduce the idea of an unbiased dynamic estimate and demonstrate that using a counterbalanced design is important from an estimation perspective when parameter dynamics are present

formal choice models in marketing the paper gives an overview and evaluates the theoretical traditions underlying choice models that are used in marketing in particular the emphasis of this essay is on the underlying assumptions limitations and empirical demands of these choice models four fundamental choice models are used as a basis to analyze this complex field the neoclassical economic theory as extended by lancaster the risk preference theory of choices under uncertainty the strict utility theory and the random utility theory these four choice models and their extensions are compared and contrasted along the three critical steps in the model building process theory generation parameterization and estimation

predictive testing of consumer choice models not subject to independence of irrelevant alternatives the probabilistic modeling of the relationship between objective or perceived characteristics of multiattribute alternatives and consumer choice is receiving increased attention in marketing and ot

on the predictive power of market share attraction models the authors test naert and weverbergh s empirical finding that attraction type specifications are characterized by a predictive power greater than that of their nonrobust counterparts the empirica

myopic policies for some inventory models with uncertain demand distributions the majority of papers on stochastic inventory theory make the assumption that the distribution of consumer demand in each time period is known with certainty while this assumption is unsupported in many applied contexts it is conventionally held that more realistic models are more difficult to solve and will not yield simple operational policies this paper shows that a simple inventory policy based upon a critical fractile can be optimal or near optimal in some inventory models with parameter adaptive demand processes in these some parameter of the demand distribution is not known with certainty and estimates of the parameter are updated in a statistical fashion as demand is observed through time examples include exponentially smoothed forecasts and bayesian updating of parameter estimates bounds on the value loss relative to optimal cost when using the critical fractile policy can be calculated directly from the problem data some numerical examples illustrate the technique

bullwhip effect measurement and its implications the bullwhip effect or demand information distortion has been a subject of both theoretical and empirical studies in the operations management literature in this paper we develop a simple set of formulas that describe the traditional bullwhip measure as a combined outcome of several important drivers such as finite capacity batch ordering and seasonality our modeling framework is descriptive in nature as it features certain plausible approximations that are commonly employed in practical inventory systems the results are nonetheless compelling and can be used to explain various conflicting observations in previous empirical studies building on the theoretical framework we discuss the managerial implications of the bullwhip measurement we show that the measurement can be completely noninformative about the underlying supply chain cost performance if it is not linked to the operational details such as decision intervals and leadtimes specifically we show that an aggregated measurement over relatively long time periods can mask the operational level bullwhip in addition we show that masking also exists under product or location aggregation in some illustrative cases