technical note capacity allocation under retail competition uniform and competitive allocations when retailers orders exceed the supplier s available capacity the supplier allocates his capacity according to some allocation rule when retailers are local monopolists uniform allocation eliminates the gaming effect so that each retailer orders her ideal allocation however when two retailers engage in cournot competition under complete information a recent study has shown that uniform allocation fails to eliminate the gaming effect so that some retailer may inflate her order strategically by examining a more general situation in which two or more retailers engage in cournot competition under complete information we establish exact conditions under which uniform allocation fails to eliminate the gaming effect these exact conditions enable us to construct a new rule called competitive allocation that can eliminate the gaming effect without inflated orders from the retailers the supplier s profit could be lower under competitive allocation than under uniform allocation when certain restrictive conditions hold in contrast competitive allocation generates higher average profits for the retailers and for the supply chain hence it reduces the inefficiency of the decentralized supply chain

reducing the bullwhip effect looking through the appropriate lens demand amplification now frequently referred to as bullwhip is potentially a very costly phenomenon it can lead to stock outs large and expensive capacity utilisation swings lower quality products and considerable production transport on costs as deliveries are ramped up and down at the whim of the supply chain however the detection of bullwhip depends on which lens is used this in turn depends on the background and requirements of various players within the value stream to gain insight into this scenario we exploit a relatively simple replenishment model because new and novel analytic solutions have been derived for all important performance metrics comparison of the competing bullwhip measures is thereby greatly streamlined in the complex real world the likelihood is that supply chains will generate even greater inconsistency between alternative variance shock and filter lens viewpoints

behavioral causes of the bullwhip effect and the observed value of inventory information the tendency of orders to increase in variability as one moves up a supply chain is commonly known as the bullwhip effect we study this phenomenon from a behavioral perspective in the context of a simple serial supply chain subject to information lags and stochastic demand we conduct two experiments on two different sets of participants in the first we find the bullwhip effect still exists when normal operational causes e g batching price fluctuations demand estimation etc are removed the persistence of the bullwhip effect is explained to some extent by evidence that decision makers consistently underweight the supply line when making order decisions in the second experiment we find that the bullwhip and the underlying tendency of underweighting remains when information on inventory levels is shared however we observe that inventory information helps somewhat to alleviate the bullwhip effect by helping upstream chain members better anticipate and prepare for fluctuations in inventory needs downstream these experimental results support the theoretically suggested notion that upstream chain members stand to gain the most from information sharing initiatives

optimality of myopic ordering policies for inventory model with stochastic supply this paper addresses a discrete time inventory model where the maximum amount of supply from which instantaneous replenishment orders can be made is a random variable optimal ordering policies are characterized as critical number policies with monotone increasing optimal critical values a sufficient condition is then presented for myopic ordering policies to be optimal

bullwhip and batching an exploration the rounding of orders to achieve a batch size is recognised as a source of the bullwhip problem within supply chains while it is often advocated that batch sizes should be reduced as much as possible there has been limited investigation into the impact of batching on bullwhip here we consider scenarios where orders are placed only in multiples of a fixed batch size for both deterministic and stochastic demand rates we derive a closed form expression for bullwhip when demand is deterministic this is validated through a simple model of a production control system an expression for bullwhip in a pass on orders scenario with stochastic demand is also derived and validated using simulation we show the impact of changing batch size on bullwhip in a production control system our results show that a manager may achieve economies through batching while minimising the impact on bullwhip through the careful selection of the batch size

decision making under uncertainty a field study of cumulative prospect theory abstract the presented research tests cumulative prospect theory cpt kahneman d tversky a 1979 prospect theory an analysis of decision under risk econometrica 47 263 291 tversky a kahneman d 1981 the framing of decisions and the psychology of choice science 211 453 480 in the financial market using us stock option data option prices possess information about actual investors preferences in such a way that an exploitation of conventional option analysis along with theoretical relationships makes it possible to elicit investor preferences the option data in this study serve for estimating the two essential elements of the cpt namely the value function and the probability weighting function the main part of the work focuses on the functions simultaneous estimation under cpt original parametric specification the shape of the estimated functions is found to be in line with theory comparing to results of laboratory experiments the estimated functions are closer to linearity and loss aversion is less pronounced

new evidence on the monday seasonal in stock returns equity derivatives and the institutionalization of equity markets affect the monday seasonal the seasonal in the standard and poor s 500 s p declines significantly over 1962 93 this decline is positively related to the ratio of institutional to individual trading volume in contrast the seasonal for small stocks does not decline and is unaffected by institutional versus individual trading higher trading costs sustain the seasonal in small stocks and unlike the s p these costs are not lower for institutions than for individuals futures minus spot s p returns exhibit a reverse seasonal informed traders use the less costly market to exploit the seasonal copyright 1997 by university of chicago press

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

the bullwhip effect in supply chains an overestimated problem abstract a phenomenon that is now well known as the bullwhip effect suggests that the variability of orders increases as they move up the supply chain from retailers to wholesalers to manufacturers to suppliers in this paper we will focus mainly on measuring the bullwhip effect existing approaches that aim at quantifying the bullwhip effect neglect the network structure of supply chains by only assuming a simple two stage supply chain consisting of a single retailer and a single manufacturer some of the relevant risk pooling effects associated with the network structure of supply chains are disregarded risk pooling effects arise when the orders which a retailer receives from its customers are statistically correlated with a coefficient of correlation less than one when analyzing the bullwhip effect in supply chains however the influence of risk pooling has to be considered the fact that these influences have not yet been analyzed motivates the research presented in this paper we will show that the bullwhip effect is overestimated if just a simple supply chain is assumed and risk pooling effects are present

a new look at the monday effect it is well documented that expected stock returns vary with the day of the week the monday or weekend effect in this article we show that the well known monday effect occurs primarily in the last two weeks fourth and fifth weeks of the month in addition the mean monday return of the first three weeks of the month is not significantly different from zero this result holds for most of the subperiods during the 1962 1993 sampling period and for various stock return indexes the monthly effect reported by ariel 1987 and lakonishok and smidt 1988 cannot fully explain this phenomenon

analysis of compound bullwhip effect causes abstract this research investigates compound causes of the bullwhip effect bwe by considering an inventory system with multiple price sensitive demand streams joint price and demand dynamics are captured by a vector time series process that incorporates the stochastic co movements in price and demand we study two bwe measures one for each demand stream individually and one for the aggregated demand we show that demand parameters including demand autocorrelation cross correlation and price sensitivity serve as root causes of the bwe we prove that the impact of these parameters on the bwe can be additively decomposed conditions are established under which a pair of simultaneous compound causes may attenuate or dampen the bwe when demand streams are aggregated we derive a pooling factor that quantifies the impact of demand aggregation on order stability when positive the pooling factor corresponds to a synergy effect that captures the gain in the stability of the pooled orders conditions for the existence of the synergy effect are obtained for several special cases involving a zero leadtime we also discuss how our analytical findings can be managerially applied to bullwhip mitigation strategies

on the replenishment policy when the market demand information is lagged we consider a situation where the most up to date information on the market demand and the inventory levels is not available to a replenishment decision maker in a single echelon of a supply chain the objective of the decision maker is to minimise the sum of the inventory and the production costs an intuitively attractive strategy under this setting might be to reduce the information time lag as much as possible by utilising information technologies such as rfid we call this strategy the time lag elimination strategy tes however this course of action requires investment in information systems and will incur a running cost we propose an alternative strategy that has similar economic consequences as the tes strategy but it does not require new information systems we call this strategy the controlling dynamics strategy cds the benefit coming from cds is quantified and is compared to that from tes we also quantify the benefits gained from the combined use of these two strategies a new ordering policy is introduced that is easy to implement without any forecasting systems and can reduce the production cost significantly

the impact of forecasting methods on the bullwhip effect abstract this paper considers the impact of forecasting methods on the bullwhip effect for a simple replenishment system in which a first order autoregressive process describes the customer demand and an order up to inventory policy characterizes the replenishment decision a bullwhip effect measure is derived for the optimal forecasting procedure that minimizes the mean squared forecasting error for the specified demand process similar measures are obtained for the moving average and exponential smoothing methods the findings indicate that different forecasting methods lead to bullwhip effect measures with distinct properties in relation to lead time and underlying parameters of the demand process moreover a simple rule is established to help managers select a forecasting method that yields the lowest inventory cost

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

avoiding the bullwhip effect using damped trend forecasting and the order up to replenishment policy we study the damped trend forecasting method and its bullwhip generating behaviour when used within the order up to out replenishment policy using z transform transfer functions we determine complete stability criteria for the damped trend forecasting method we show that this forecasting mechanism is stable for a much larger proportion of the parametrical space than is generally acknowledged in the literature we provide a new proof to the known fact that the naive exponential smoothing and holts method forecasting when used inside the out policy will always generate bullwhip for every possible demand process for any lead time further we demonstrate the damped trend out system behaves differently sometimes it will generate bullwhip and sometimes it will not bullwhip avoidance occurs when demand is dominated by low frequency harmonics in some instances in other instances bullwhip avoidance happens when demand is dominated by high frequency harmonics we derive sufficient conditions for when bullwhip will definitely be generated and necessary conditions for when bullwhip may be avoided we verify our analytical findings with a numerical investigation

taking stock a critical assessment of recent research on inventories empirical and theoretical aspects of inventory behavior became hot topics in the 1950s and early 1960s no one seemed to notice the tension that was developing between the emerging macroeconomic and microeconomic views of inventories macroeconomists routinely thought of inventories as a destabilizing factor yet the prevailing micro theory viewed inventories as a stabilizing factor it was a fascinating question that was barely explored instead somewhat inexplicably interest in inventories dried up as if inventories were of minor economic significance and little intrinsic interest by the early 1980s then economists once again knew something they had known in the 1950s that inventory investment is of first order importance in business cycles but they were also beginning to realize that the standard production smoothing buffer stock model of inventories was in deep trouble this paper focuses on developments since that realization

seasonality aggregation and the testing of the production smoothing hypothesis one of the leading hypotheses concerning the dynamics of production over time is the production smoothing hypothesis given a planning horizon which spans a number of production periods the firm need not produce in each period an amount equal to expected sales rather resorting to inventory accumulation and liquidation the firm may follow a production plan temporally smoother than the path of demand if firms faced with convex cost functions chose to smooth the rate of output in order to minimize costs one would expect to observe that the rate of output would vary less than the rate of sales with variations in inventory stocks absorbing some of the fluctuations in sales recently work on the testing of the production smoothing hypothesis has cast doubt on its empirical validity the evidence presented by alan blinder seems to indicate that the variance of production exceeds that of sales in seven out of eight two digit retail industries 1981 and in eighteen out of twenty two digit manufacturing industries 1983 and 1986 the purpose of this paper then is to examine the validity of such tests when seasonally adjusted aggregated data are used the evidence presented show that the relative size of the variances of the seasonally adjusted production and sales does not provide valid tests of the production smoothing hypothesis in addition aggregating over firms where the seasonal patterns differ may also distort tests of production smoothing blinder realized that the use of seasonally adjusted data may not provide an adequate test of the hypothesis stating had they been available i would have preferred to use data that were not seasonally adjusted since the production smoothing model presumably applies to seasonal fluctuations in sales however such data are not available 1983 fn 19 in this paper i focus on the cement industry because the unadjusted disaggregated data are available for the direct testing of the conjecture that aggregate seasonally adjusted data mask production smoothing phenomenon aggregate monthly data on five other industries will also be examined

a unified theory of the dynamics of closed loop supply chains we investigate the dynamics of a closed loop supply chain with first order auto regressive ar 1 demand and return processes we assume these two processes are cross correlated the remanufacturing process is subject to a random triage yield remanufactured products are considered as goodas new and used to partially satisfy market demand newly manufactured products make up the remainder we derive the optimal linear policy in our closed loop supply chain setting to minimise the manufacturer s inventory costs we show that the lead time paradox can emerge in many cases in particular the auto and cross correlation parameters and variances of the error terms in the demand and the returns as well as the remanufacturing lead time all influence the existence of the lead time paradox finally we propose managerial recommendations for manufacturers

why is production more volatile than sales theory and evidence on the stockout avoidance motive for inventory holding this paper argues that the macroeconomically interesting features of inventory behavior are well captured by a model in which firms face only demand uncertainty with a nonnegativity constraint on inventories empirical implications of the stockout avoidance model of inventory behavior are derived and then tested on disaggregated automobile industry data the results largely support the model though they suggest a small role for production smoothing as well subsidiary evidence on the relative variance of demand and cost shocks suggests that demand shocks are indeed more important

measuring and avoiding the bullwhip effect a control theoretic approach an important contributory factor to the bullwhip effect i e the variance amplification of order quantities observed in supply chains is the replenishment rule used by supply chain members first the bullwhip effect induced by the use of different forecasting methods in order up to replenishment policies is analysed variance amplification is quantified and we prove that the bullwhip effect is guaranteed in the order up to model irrespective of the forecasting method used thus when production is inflexible and significant costs are incurred by frequently switching production quantities up and down order up to policies may no longer be desirable or even achievable in the second part of the paper a general decision rule is introduced that avoids variance amplification and succeeds in generating smooth ordering patterns even when demand has to be forecasted the methodology is based on control systems engineering and allows important insights to be gained about the dynamic behaviour of replenishment rules

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

the effect of information sharing on supply chain stability and the bullwhip effect this paper analyzes the bullwhip effect in multi stage supply chains operated with linear and time invariant inventory management policies and shared supply chain information such information includes past order sequences and inventory records at all supplier stages the paper characterizes the stream of orders placed at any stage of the chain when the customer demand process is known and ergodic and gives an exact formula for the variance of the orders placed the paper also derives robust analytical conditions based only on inventory management policies to predict the presence of the bullwhip effect and bound its magnitude these results hold independently of the customer demand the general framework proposed in this paper allows for any inventory replenishment policies any ways of sharing and utilizing information and any customer demand processes it is also shown as a special case that sharing customer demand information across the chain significantly reduces but does not completely eliminate the bullwhip effect

performance analysis of demand planning approaches for aggregating forecasting and disaggregating interrelated demands a synchronized and responsive flow of materials information funds processes and services is the goal of supply chain planning demand planning which is the very first step of supply chain planning determines the effectiveness of manufacturing and logistic operations in the chain propagation and magnification of the uncertainty of demand signals through the supply chain referred to as the bullwhip effect is the major cause of ineffective operation plans therefore a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile demand appropriate demand aggregation and statistical forecasting approaches are known to be effective in managing the demand variability this paper uses the bivariate var 1 time series model as a study vehicle to investigate the effects of aggregating forecasting and disaggregating two interrelated demands through theoretical development and systematic analysis guidelines are provided to select proper demand planning approaches a very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation moreover the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used

measure of bullwhip effect in supply chains the case of high order autoregressive demand process abstract bullwhip effect the phenomenon in which variance of demand is amplified when moving upstream has attracted the attention of many researchers for the last few decades although the main sources that cause bullwhip effect have been identified quantifying the bullwhip effect still remains a challenge in the past measuring the bullwhip effect for supply chains with autoregressive demand process has been conducted by some researchers however most past researches focused mainly on the simple ar 1 model in many practical situations autoregressive models with higher order should be employed because they might better represent the demand process up to now very little effort has been spent on this matter therefore this research is conducted to fill this gap by first dealing with ar 2 demand process and investigating the behavior of the developed measure with respect to autoregressive coefficients and order lead time extension to the general ar p demand process is then considered

the impact of pricing policy on sales variability in a supermarket retail context abstract the bullwhip effect is recognized to be a significant cost driver in supply chains one of the measures proposed frequently to counter bullwhip effect is price stability through everyday low pricing edlp however this study suggests that with an auto regressive ar1 demand process the use of constant instead of dynamic pricing may result in lower profitability and higher demand volatility an optimal price and stocking level policy is developed with normally distributed demand in this study and the model is tested using parameters from a supermarket scanner data set to determine the impact of two pricing policies the hypothesis that low prior period demand leads to discounted pricing is also tested and partially supported

from t bills to common stocks investigating the generality of intra week return seasonality the authors investigate the extent to which intra week seasonality still exists and whether its pattern is uniform across three stock indices and treasury bonds with seven different maturities they find that intra week seasonality continues to be significant and that its pattern is not uniform either between the stock indices and the treasury bonds or even among the bonds alone a pattern shared by stocks and bonds is that monday returns become increasingly negative with maturity these findings suggest that neither institutional nor general equilibrium explanations by themselves can explain the pattern of intra week seasonality in securities markets

information sharing and order variability control under a generalized demand model the value of information sharing and how it could address the bullwhip effect have been the subject of studies in the literature most of these studies used different forms of demand models assuming that no order smoothing was used by the retailer and that the supplier has full knowledge of the retailer s demand model and order policy in this paper we contribute to the literature by starting with a most general demand model coupled with a smoothing policy for order variability control in addition we do not require that the supplier has full knowledge of the retailer s demand model and order policy but instead let the retailer share its projected future orders and freely revise them as the retailer sees fit under such a setting we first obtain a unifying formula for the magnitude of the bullwhip effect the formula indicates that it is the forecast correlation over the exposure period as a whole that determines the magnitude of the bullwhip effect we then quantify the value of information sharing and generalize the existing results in the literature finally we explore the optimal smoothing parameters that could benefit the total supply chain the resulting optimal policy resembles the postponement strategy we find that information sharing together with order postponement improves the supply chain performance even though the order variability may amplify in some cases

a measure of bullwhip effect in supply chains with a mixed autoregressive moving average demand process abstract in this paper we quantify the impact of the bullwhip effect the phenomenon in which information on demand is distorted as moving up a supply chain for a simple two stage supply chain with one supplier and one retailer assuming that the retailer employs a base stock inventory policy and that the demand forecast is performed via a mixed autoregressive moving average model arma 1 1 we investigate the effects of the autoregressive coefficient the moving average parameter and the lead time on the bullwhip effect

day of the week effect on the paris bourse abstract we present evidence on the day of the week effect on a stock market with a particular settlement procedure the paris bourse a strong and persistent negative return is found on tuesdays a similar result was previously found for japan and australia the settlement procedure is shown to have an effect on the distribution of daily returns consistent with forward pricing theory it cannot explain the negative tuesday return after adjusting for this settlement effect the mean return on mondays is the largest in the week a result at odds with the evidence in the u s a

the censored newsvendor and the optimal acquisition of information this paper investigates the effect of demand censoring on the optimal policy in newsvendor inventory models with general parametric demand distributions and unknown parameter values we show that the newsvendor problem withobservable lost sales reduces to a sequence of single period problems while the newsvendor problem withunobservable lost sales requires a dynamic analysis using a bayesian markov decision process approach we show that the optimalin ventory level in the presence of censored demand ishigher than would be determined using a bayesian myopic policy we explore the economic rationality for this observation and illustrate it with numerical examples

day of the week and intraday effects in stock returns abstract this study examines day of the week effects using hourly values of the dow jones industrial average we find that over the 1963 1983 period the weekend effect has sifted from characterizing active trading on monday to characterizing the non trading weekend over the early part of our sample period negative returns characterize each hour of trading on monday while the return from friday close to monday open is positive in the most recent subperiod monday average hourly returns after noon are all positive and the weekend effect is due to negative average returns from friday close to monday open

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

a human experiment on inventory decisions under supply uncertainty abstract controlled human experiments are adopted in this paper to investigate the impact of supply uncertainty on buyers inventory management the experiments aim at assessing the impact of one specific source of supply chain uncertainty namely stochastic lead times on inventory holdings and the extent of the bullwhip effect three experimental treatments are run within the framework of the beer game manipulating variability in demand and in lead times results confirm that the bullwhip effect arises in all experimental treatments and that the variance of orders is higher under stochastic lead times analysis of players behaviour in the course of the game suggests that players react to higher uncertainty by holding fewer inventories a behaviour consistent with the predictions of some psychological models of choice under ambiguity

production smoothing in the japanese vehicle industry abstract physical data from the japanese vehicle industry covering monthly observations from 1985 1 to 1994 12 are investigated across nine goods ranging from bicycles to large buses smaller variance of production is found relative to sales across all goods for unadjusted data and in 17 out of 18 cases for seasonally adjusted data such evidence in favor of the production smoothing model psm is confirmed by full information maximum likelihood employed jointly on inventories and sales equations the estimates suggest the industry operates in the range of increasing marginal costs in six out of nine goods positive costs of being away from targeted inventory are also present in six out of these nine goods which make the psm a good description of this industry

production planning horizon production smoothing and convexity of the cost functions abstract one of the productions planning models economists have used for the past four decades is the production smoothing model the model s conclusion is simple if cost functions are convex under certain conditions firm s production plans may show smaller variability than the firm s sales this is a result of the first order condition for cost minimization over the production planning horizon in a 1986 paper blinder raised doubts regarding the ability of the production smoothing model to explain the observed data some researchers e g ramey journal of political economy 99 2 1991 306 explained the inconsistency between the theory and the data by providing evidence that the cost functions are non convex in this paper i point out that the shapes of the cost functions depend on the length of the time horizon under consideration it is possible that while short run cost functions are upward sloping long run cost functions may be downward sloping because the planning horizon for firms facing seasonal demand has been shown to be one seasonal cycle the short run cost function whose shape determines the firm s production plan over the horizon need to be estimated using single planning horizon data i e a single seasonal cycle researchers who use long time series data are in effect estimating long run cost functions the slopes of which may be non positive i show further that convexity of the production cost function is neither necessary nor sufficient condition for production smoothing to be an optimal plan

the variability of aggregate demand with s s inventory policies this paper develops a general theory of the aggregate implications of s s inventory policies it is shown that s s policies add to the variability of demand with the variance of orders exceeding the variance of sales overall the s s theory contradicts the widely held notion that retail inventories act as a buffer protecting manufacturers from fluctuating sales in 1951 arrow harris and marschak 3 introduced the s s form of inventory policy the policies are designed for retailers of finished goods who face economies of scale when placing orders with their suppliers to pursue an s s inventory policy the retailer establishes a lower stock point s and an upper stock point s no order is placed until inventories fall to s or below whereupon they are restored to the maximum of s a general proof of the optimality of these s s inventory policies was provided by scarf 13 at the microeconomic level the model has been extensively investigated formulae are available to compute optimal policies e g ehrhardt 6 and these policies are xidely used in industry e g schwartz ed 14 in addition the model has been extended to increasingly complex demand environments e g karlin and fabens 11 in contrast little is known about the macroeconomic implications of s s policies several recent papers have begun to correct this deficiency akerlof has suggested that pursuit of constant threshold money holding policies of the s s variety might be responsible for the observed low short run income elasticity of the demand for money akerlof 1 and akerlof and milbourne 2 in the operations research literature ehrhardt schultz and wagner 7 analyzed the demand environment of a wholesaler supplying several retailers they required that the distinct retailers have independent sales ruling out the analysis of common factors in sales finally simulation results of blinder 4 suggested a role for the s s model in understanding retail sector inventories however the theoretical difficulties with the model remained unresolved blinder commented if firms have a technology that makes the s s rule optimal aggregation across firms is inherently difficult indeed it is precisely this difficulty which has prevented the s s model from being used in empirical work to date blinder 4 p 459 in this paper we present a general theory of the aggregate implications of s s policies our central finding is that s s policies add to the variability of demand with the variance of orders exceeding the variance of sales this result holds even in the presence of common factors in retail sales in addition a close connection

reduction of the value of information sharing as demand becomes strongly auto correlated information sharing has been identified in the academic literature as one of the most important levers to mitigate the bullwhip effect in supply chains a highly cited article on the bullwhip effect has claimed that the percentage inventory reduction resulting from information sharing in a two level supply chain when the downstream demand is autoregressive of order one is an increasing function of the autoregressive parameter of the demand in this paper we show that this is true only for a certain range of the autoregressive parameter and there is a maximum value beyond which the bullwhip ratio at the upstream stage is reduced and the percentage inventory reduction resulting from information sharing decreases towards zero we also show that this maximum value of the autoregressive parameter can be as high as 0 7 which represents a common value that may be encountered in many practical contexts this means that large benefits of information sharing cannot be assumed for those stock keeping units skus with highly positively auto correlated demand instead equally careful analysis is needed for these items as for those skus with less strongly auto correlated demand

the bullwhip effect progress trends and directions the bullwhip effect refers to the phenomenon where order variability increases as the orders move upstream in the supply chain this paper provides a review of the bullwhip literature which adopts empirical experimental and analytical methodologies early econometric evidence of bullwhip is highlighted findings from empirical and experimental research are compared with analytical and simulation results assumptions and approximations for modelling the bullwhip effect in terms of demand forecast delay replenishment policy and coordination strategy are considered we identify recent research trends and future research directions concerned with supply chain structure product type price competition and sustainability

modeling and analysis of cash flow bullwhip in supply chain abstract the bullwhip effect that occurs in supply chain inventory can distort demand forecasts and lead to inefficiencies such as excessive inventory stock outs and backorders in this paper we theorize that inventory bullwhip also leads to cash flow bullwhip cfb specifically this paper focuses on studying cfb by developing mathematical and simulation models to analyze the relationship between inventory and cash flow bullwhip by using cash conversion cycle ccc as a metric the mathematical models for inventory bullwhip are developed for two stage and generic multi stage supply chains and then by extending these inventory models the cfb models are developed for two stage cfb predicted by the proposed mathematical models approximately differ 14 from detailed simulation models we find that increasing variability increases inventory and cash flow bullwhip along with lead time whereas increasing the demand observation period has the opposite effect the average marginal impact of the bullwhip effect on the cfb is approximately 20 additionally the cfb is also an increasing function of an expected value of inventory and a decreasing function of an expected value of demand

day of the week effects in financial futures an analysis of gnma t bond t note and t bill contracts this paper provides a comprehensive study of weekly seasonal effects in gnma t bond t note and t bill futures returns two distinct patterns are found in returns on gnma t bond and t note contracts while no seasonals are noted for t bill futures a negative monday seasonal similar to the well known monday effect in stock returns is found for gnma and t bond contracts a positive tuesday seasonal is found on gnma t bond and t note contracts our evidence indicates that the significance of weekly sea sonals depends in an important way on the time period studied the negative monday phenomenon occurs only in the data before 1982 while the positive tuesday effect is present only after 1984 in addition we find that both seasonal phenomena occur only during months prior to a delivery month this effect appears to be related to the calendar month more specifically the monday effect is apparently concentrated during february while the tuesday effect is concentrated during may

an arima supply chain model this paper presents a multistage supply chain model that is based on autoregressive integrated moving average arima time series models given an arima model of consumer demand and the lead times at each stage it is shown that the orders and inventories at each stage are also arima and closed form expressions for these models are given the paper also discusses the causes of the bullwhip effect a phenomenon in which variation in demand produces larger variations in upstream orders and inventory this discussion reveals how different modeling can lead to different insights because they make different assumptions about the cause of the bullwhip effect these observations are used to develop managerial insights about reducing the bullwhip effect

nonconvex costs and the behavior of inventories this paper explores one possible explanation for the apparent excess volatility of production relative to sales nonconvexities in the technology facing firms it is shown that if firms operate in a region of declining marginal costs then small shifts in demand can cause production to jump substantially estimates for six production to stock industries as well as the automobile industry suggest that all these industries behave as if they were operating in the region of nonconvex costs the results have important implications not only for inventory investment but also for the cyclical behavior of productivity and prices

the bullwhip effect in supply chain networks this paper analyzes the propagation and amplification of order fluctuations i e the bullwhip effect in supply chain networks operated with linear and time invariant inventory management policies the supply chain network is allowed to include multiple customers e g markets any network structure with or without sharing information the paper characterizes the stream of orders placed by any supplier for any stationary customer demand processes and gives exact formulas for the variance of the orders placed and the amplification of order fluctuations the paper also derives robust analytical conditions based only on inventory management policies to predict the presence of the bullwhip effect for any network structure any inventory replenishment policies and arbitrary customer demand processes numerical examples show that the analytical results accurately quantify the bullwhip effect managerial insights are drawn from the analysis the methodology presented in this paper generalizes those in previous studies for serial supply chains

information transmission and the bullwhip effect an empirical investigation the bullwhip effect is the amplification of demand variability along a supply chain a company bullwhips if it purchases from suppliers more variably than it sells to customers such bullwhips amplifications of demand variability can lead to mismatches between demand and production and hence to lower supply chain efficiency we investigate the bullwhip effect in a sample of 4 689 public u s companies over 1974 2008 overall about two thirds of firms bullwhip the sample s mean and median bullwhips both significantly positive respectively measure 15 8 and 6 7 of total demand variability put another way the mean quarterly standard deviation of upstream orders exceeds that of demand by 20 million we decompose the bullwhip by information transmission lead time estimating the bullwhip s information lead time components with a two stage estimator we find that demand signals firms observe with more than three quarters notice drive 30 of the bullwhip and those firms observe with less than one quarter s notice drive 51 from 1974 1994 to 1995 2008 our sample s mean bullwhip dropped by a third this paper was accepted by christian terwiesch operations management

the wandering weekday effect in major stock markets this paper reports a wandering weekday effect the pattern of day seasonality in stock market returns is not fixed as assumed in the monday or weekend effects but changes over time analysing daily closing prices in eleven major stock markets during 1993 2007 our results show that the wandering weekday is not conditional on average returns in the previous week the twist in the monday effect nor does it diminish through the period of analysis the results have important implications for market efficiency and help to reconcile mixed findings in previous studies including the reported disappearance of the weekday effect in recent years

optimal inventory decisions in a multiperiod newsvendor problem with partially observed markovian supply capacities abstract this paper considers a multi period news vendor problem with partially observed supply capacity information which evolves as a markovian process the supply capacity is fully observed by the buyer when the capacity is smaller than the buyer s ordering quantity otherwise the buyer knows that the current period supply capacity is greater than its ordering quantity based on these two observations the buyer updates the future supply capacity forecasting accordingly with a dynamic programming formulation we prove the existence of an optimal ordering policy we also prove that the optimal order quantity is greater than the myopic order quantity

the impact of information enrichment on the bullwhip effect in supply chains a control engineering perspective this paper examines the beneficial impact of information sharing in multi echelon supply chains we compare a traditional supply chain in which only the first stage in the chain observes end consumer demand and upstream stages have to base their forecasts on incoming orders with an information enriched supply chain where customer demand data e g epos data is shared throughout the chain two types of replenishment rules are analysed order up to out policies and smoothing policies policies used to reduce or dampen variability in the demand for the class of out policies we will show that information sharing helps to reduce the bullwhipeffect variance amplification of ordering quantities in supply chains significantly especially at higher levels in the chain however the bullwhip problem is not completely eliminated and it still increases as one moves up the chain for the smoothing policies we show that information sharing is necessary to reduce order variance at higher levels of the chain the methodology is based on control systems engineering and allows us to gain valuable insights into the dynamic behaviour of supply chain replenishment rules we also introduce acontrolengineering based measure to quantify the variance amplification bullwhip or variance reduction

reverse bullwhip effect in pricing price variability is one of the major causes of the bullwhip effect this paper analyzes the impact of procurement price variability in the upstream of a supply chain on the downstream retail prices procurement prices may fluctuate over time for example when the supply chain players deploy auction type procurement mechanisms or if the prices are dictated in market exchanges a game theory framework is used here to model a serial supply chain sequential price game scenarios are investigated to show that there is an increase in retail price variability and an amplified reverse bullwhip effect on prices rbp under certain demand conditions

the multivariate bullwhip effect abstract a multivariate bullwhip expression for m products with an order up to inventory policy is developed the demand models under consideration are differenced stationary vector time series with a wold representation for which general forecasting formulas are available resulting in a large class of possible models including nonstationary ones examples are provided for common demand models and implemented on sales data it is found that the multivariate approach gives rise to mechanisms for understanding and reducing the bullwhip effect through horizontal information sharing particularly for the nonstationary demand case in the stationary setting a more nuanced approach to bullwhip reduction can be achieved by managing the relationship between cross correlations and lead times a method of determining whether a multivariate or univariate approach generates a lower bullwhip effect is proposed

the individual investor and the weekend effect it is well known that stock returns on average are negative on mondays yet it is less well known that this finding is substantially the consequence of returns in prior trading sessions when friday s return is negative monday s return is negative nearly 80 percent of the time with a mean return of 0 61 percent when friday s return is positive the subsequent monday s mean return is positive 0 11 percent this relationship is stronger than for any other pair of trading days and is most acute in small and medium size companies the trading behavior of individual investors appears to be at least one factor contributing to this pattern individual investors are more active sellers of stock on mondays particularly following bad news in the market

capacity allocation using past sales when to turn and earn consider a supplier selling to multiple retailers demand varies across periods but the supplier s capacity and wholesale price are fixed if demand is high the retailers needs exceed capacity and the supplier must implement an allocation mechanism to dole out production we examine how the choice of mechanism impacts retailer actions and supply chain performance in particular we analyze turn and earn allocation a method commonly used in the automobile industry this scheme bases current allocations on past sales and thus enables retailers to influence their future allocations they compete for scarce capacity even if they do not compete for customers we show that turn and earn induces the retailers to increase their sales when demand is low and the supplier s capacity is otherwise underutilized supplier profits thus increase the impact on the supply chain depends on how restrictive capacity is with mildly tight capacity the retailers higher sales rate does not significantly lower their profits but does reduce the cost of idle capacity supply chain performance improves with extremely tight capacity the retailers intense competition dissipates more profits than the supplier gains and supply chain performance suffers consequently turn and earn does not generally coordinate the system it is best characterized as a means for the supplier to increase her profits at the expense of the retailers and potentially even the supply chain furthermore these results hold even if the retailers can hold inventory in anticipation of scarce capacity

managing supply chain demand variability with scheduled ordering policies this paper studies supply chain demand variability in a model with one supplier and n retailers that face stochastic demand retailers implement scheduled ordering policies orders occur at fixed intervals and are equal to some multiple of a fixed batch size a method is presented that exactly evaluates costs previous research demonstrates that the supplier s demand variance declines as the retailers order intervals are balanced i e the same number of retailers order each period this research shows that the supplier s demand variance will generally decline as the retailers order interval is lengthened or as their batch size is increased lower supplier demand variance can certainly lead to lower inventory at the supplier this paper finds that reducing supplier demand variance with scheduled ordering policies can also lower total supply chain costs

state space investigation of the bullwhip problem with arma 1 1 demand processes using state space techniques we study a myopic order up to policy the policy is myopic because it is optimal at minimising local inventory holding and shortage costs in particular we study the bullwhip effect produced by the replenishment policy reacting to a stochastic arma 1 1 demand processes we reveal that bullwhip is fundamentally caused by the co variance between the inventory level and the demand forecast we go on to highlight the impact of a simple control engineering inspired bullwhip reduction technique a proportional controller in the inventory feedback loop although it can be shown this approach is always able to remove bullwhip we expose that it is not possible to arbitrarily tune the proportional controller without knowing in advance the likely structure of the demand process we conclude with an investigation of trade offs and optimisations in the order and inventory variance and their sum produced by our policy

divergence of sentiment and stock market trading this paper introduces the concept of divergence of sentiment to the behavioral finance literature we measure the distance between people with positive and negative sentiment on a daily basis for 20 countries by using data from status updates on facebook the prediction is that a higher divergence of sentiment leads to more diverging views on prospects and risks and thus to more diverging views on the value of a stock in line with this prediction divergence of sentiment is positively related to trading volume we further predict and find a positive relation between divergence of sentiment and stock price volatility the observed relations are stronger when individual investors are more likely to trade we compare the effect of our country specific measures to a global measure of divergence of sentiment we find that the separate effects of country specific and global divergence measures depend on a country s level of market integration

impact of market demand mis specification on a two level supply chain this paper investigates the impact of mis specifying the market demand process on a serially linked two level supply chain box jenkins models are used to represent both the true and a mis specified market demand processes it is shown that the impact of mis specification on cost is minor if the supply chain tries to minimise the market demand forecast errors furthermore our analysis suggests that mis specification does not always result in additional costs a managerial insight is revealed poor forecast accuracy is not always bad for the total supply chain costs in other words employing more accurate forecasting methods may actually result in higher total supply chain costs

supply chain decision making will shorter cycle times and shared point of sale information necessarily help using a simulated supply chain experiment based on the well known beer game we examine how changes in order and delivery cycles availability of shared point of sale pos information and the pattern of customer demand affect supply chain efficiency we find that speeding up cycle time is beneficial but the sharing of pos information is not necessarily so whether or not the sharing of pos information is beneficial depends on the nature of the demand pattern represented by the pos information if the demand pattern conveys continual change in ultimate downstream customer demand as does an s shaped demand pattern the pos information can distract the upstream decision maker from what is perhaps more immediately relevant information orders placed by the proximate downstream agent and the supply line

return autocorrelations around nontrading days we document a pattern in the serial dependence of security returns around nontrading days the correlation of returns the second day after a weekend or holiday with returns the first day after is unusually low and in many return series is negative implying a reversal of price movements we also document unusually large positive return autocorrelations the last day before and the first day after weekends and holidays the pattern has existed in equity returns for over 100 years and also exists in several futures markets implying that the pattern is robust to alternative market microstructures article published by oxford university press on behalf of the society for financial studies in its journal the review of financial studies

the production smoothing model of inventories revisited an error correction specification which nests the linear quadratic production smoothing and stock adjustment models of inventories is estimated on u s seasonally adjusted two digit sic data the restrictions of the general model to the production smoothing specification is accepted for four out of six of the production to stock industries and for total nondurables the estimation results do not suggest any use of inventories to smooth the level of production but there is evidence of smoothing of the rate of change of production for three production to stock industries and for both total nondurables and total durables coefficient estimates may however be biased copyright 1994 by royal economic society

nominal data and the production smoothing hypothesis abstract this paper shows that when price variations are not properly purged from the data the estimated ratios of production variances to sales variances are likely to be biased upward leading to more frequent rejections of the production smoothing hypothesis the results point to the importance of removing price variations from the data before testing the hypothesis

stochastic stackelberg equilibria with applications to time dependent newsvendor models this is the author s version of the article stochastic stackelberg equilibria with applications to time dependent newsvendor models journal of economic dynamics and control volume 37 issue 7 july 2013 pages 1284 1299

on the behavioral differences between professional and amateur investors after the weekend abstract this paper compares the trading patterns of amateur and professional investors during the days following the weekend the comparison is based on all the daily transactions of a large sample of both amateurs and professionally managed investors in a major brokerage house in israel from 1994 to 1998 we find that weekends influence both amateurs and professional investors however they affect them in opposite directions individuals increase both their buy and sell activities and their propensity to sell rises more than their propensity to buy professionals on the other hand tend to perform fewer buy as well as sell trades after the weekend but unlike individuals the drop in their activity is almost the same for buy trades and for sell trades the results agree with previous hypotheses raised in the literature which were not directly tested about the effects of the weekend on the predisposition to trade of individuals and institutions in other markets we also find that returns on the israeli stock market index are correlated in general with the behavioral patterns exhibited by the investors in our sample in particular the returns on the days following the weekend are lower than those in other weekdays in a manner consistent with the behavioral patterns we found

on bullwhip in supply chains historical review present practice and expected future impact abstract demand amplification or bullwhip as it is now called is not a new phenomenon since evidence of its existence has been recorded at least as far back as the start of the 20th century and is well known to economists yet industry worldwide still has to cope with bullwhip measured not just in terms of the 2 1 amplification which is frequently quoted but sometimes it is as high as 20 1 from end to end in the supply chain this can be very costly in terms of capacity on costs and stock out costs on the upswing and stockholding and obsolescence costs on the downswing in this paper we have identified 10 published causes of bullwhip all of which are capable of elimination by re engineering the supply chain we offer evidence on the present health of a family of supply chains and pinpoint much good practice this is in anticipation that such excellence will become normative in the near future as the learning experience gathers momentum and provided that human factors are properly addressed

the dynamics of short term mutual fund flows and returns a time series and cross sectional investigation abstract this study analyzes the dynamics of daily mutual fund flows a vector auto regression var of flows and returns shows that the behavior of fund investors is more consistent with contrarian rather than momentum characteristics past fund flows have a positive impact on future fund returns with the long term information effect dominating the transient price pressure effect seasonality in daily flows such as day of week and day of month patterns are present and daily flows are generally mean reverting probit regressions indicate that fund investment objective marketing policy and level of active management explain cross sectional variation in the behavioral patterns displayed in daily flows our results are robust to the different methods of calculating daily flows based on whether or not the day end tna figures include the current day s flow throughout the analysis we contrast the dynamics of daily flows with established results for monthly fund flows and find important differences between the two

mitigating the bullwhip effect by ordering policies and forecasting methods abstract the bullwhip effect in which order variability increases as one moves up the supply chain has been observed in a range of industries modeled by several authors and various remedies suggested this paper provides a simulation of the effect of improved forecasting methods and finds that holt s and brown s methods substantially mitigate the bullwhip effect across a range of performance metrics the end result is to identify ordering policies that perform particularly well in combination with these forecasting methods and indicate how they can be implemented in practice

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

measure of bullwhip effect in supply chains with autoregressive demand process abstract in supply chain management one of the most critical problems which require a lot of effort to deal with is how to quantify and alleviate the impact of bullwhip effect the phenomenon in which information on demand is distorted while moving upstream although it is well established that demand forecast lead time order batching shortage gaming and price fluctuation are the main sources that lead to the bullwhip effect the problem of quantifying bullwhip effect still remain unsolved in many situations due to the complex nature of the problem in this research a measure of bullwhip effect will be developed for a simple two stage supply chain that includes only one retailer and one supplier in the environment where the retailer employs base stock policy for their inventory and demand forecast is performed through the first order autoregressive model ar 1 the effect of autoregressive coefficient and lead time on this measure will then be investigated

the effect of replenishment policies on the bullwhip effect a transfer function approach an important phenomenon in supply chain management known as the bullwhip effect suggests that demand variability increases as one moves up a supply chain this paper examines the influence of different replenishment policies on the occurrence of the bullwhip effect the paper demonstrates that certain replenishment policies can in themselves be inducers of the bullwhip effect while others inherently lower demand variability the main causes of increase in variability are projections of future demand expectations which result in over exaggerated responses to changes in demand we suggest that through appropriate selection and use of certain replenishment rules the bullwhip effect can be avoided subsequently allowing supply chain management costs to be lowered

periodic review production models with variable capacity random yield and uncertain demand we investigate a production planning problem in a periodic review environment with variable production capacity random yields and uncertain demand the implications of random yields and variable capacity for lot sizing previously have been explored separately but not jointly many production environments are likely to be subject to both types of uncertainties to minimize the total discounted expected costs production holding and shortage costs we formulate the problem as a stochastic dynamic program for the finite horizon problem we prove that the objective function is quasi convex and that the structure of the optimal policy is characterized by a single critical point for the initial stock level at each period that is if the initial stock is greater than this critical point the optimal planned production is zero otherwise it is greater than zero expressions for solving the critical point and the optimal planned production are obtained we further show that the solution for the finite horizon problem converges to that of the infinite horizon problem

a partially observed markov decision process for dynamic pricing in this paper we develop a stylized partially observed markov decision process pomdp framework to study a dynamic pricing problem faced by sellers of fashion like goods we consider a retailer that plans to sell a given stock of items during a finite sales season the objective of the retailer is to dynamically price the product in a way that maximizes expected revenues our model brings together various types of uncertainties about the demand some of which are resolvable through sales observations we develop a rigorous upper bound for the seller s optimal dynamic decision problem and use it to propose an active learning heuristic pricing policy we conduct a numerical study to test the performance of four different heuristic dynamic pricing policies in order to gain insight into several important managerial questions that arise in the context of revenue management

weekend effects on stock returns a note some researchers have apparently been surprised to discover that the distribution of stock returns depends on the day of the week kenneth french 3 for example in testing whether daily stock returns are generated by a trading time or calendar time hypothesis provided convincing evidence of a negative market return on mondays as french carefully notes this finding runs counter to both hypotheses since a trading time view would have expected stock returns equal on different days and a calendar time view would have higher expected returns on monday to compensate for the longer holding period in this paper we offer a partial explanation for the apparently puzzling discovery of different daily returns we argue that the expected stock returns as measured for example from closing to closing prices should depend on the day of the week in general we argue that the expected returns on mondays should be lower than would be implied simply by a trading time or calendar time model and the returns on fridays should be higher in addition we anticipate that holidays will have complex effects on stock returns on other days of the week our argument is based on the delay between trading and settlements in stocks and in clearing checks the explanation that we offer for different measured daily returns does not contradict the efficient market hypothesis as correctly adjusted expected returns should not differ according to the day of the week

all or nothing ordering under a capacity constraint we consider a special case of the single item periodic review inventory control problem with fixed plus linear ordering costs and an upper bound on production capacity we assume that the fixed cost is large relative to the variable cost and restrict our analysis to full capacity orders we show that the optimal policy is a threshold policy with respect to the inventory position for a class of cost to go functions that include the class of convex functions

capacitated inventory problems with fixed order costs some optimal policy structure abstract almost 40 years ago h scarf the optimality of s s policies in dynamic inventory problems in k arrow s karlin p suppes eds mathematical models in the social sciences stanford university press stanford 1960 established the optimal s s policy structure for the periodic review inventory problem with fixed ordering costs and no capacity constraint since then the capacitated problem has resisted characterization in the present paper we partially bridge this gap using a generalization of scarf s k convexity we show that the optimal capacitated policy has an s s like structure to do so we divide the parameter space into four regions in two of these regions the optimal policy is completely specified while in the other two it is partially specified we complement these findings with a computational study this study suggests that a still simpler optimal policy structure exists

a smart market for industrial procurement with capacity constraints we address the problem of designing multi item procurement auctions for a monopsonistic buyer in capacity constrained environments using insights from classical auction theory we construct an optimization based auction mechanism smart market relying on the dynamic resolution of a linear program minimizing the buyer s cost under the suppliers capacity constraints suppliers can modify their offers in response to the optimal allocation corresponding to each set of bids giving rise to a dynamic competitive bidding process to assist suppliers we also develop a bidding suggestion device based on a myopic best response mbr calculation that solves an associated optimization problem assuming linear costs for the suppliers we study within a game theoretic framework the sequence of bids arising in this smart market under a weak behavioral assumption and some symmetry requirements an explicit upper bound for the winning bids is established we then formulate a complete behavioral model and solution methodology based on the mbr rationale and show that the bounds derived earlier continue to hold we analytically derive some structural and convergence properties of the mbr dynamics in the simplest nontrivial market environment which suggests further possible design improvements and investigate bidding dynamics and incentive compatibility issues via numerical simulations