calculating order up to levels for products with intermittent demand abstract the croston 1972 forecasting and stock control for intermittent demands operational research quarterly 23 289 303 method is the standard method for forecasting intermittent demand it has been shown to perform well in various studies and is available in most commercial forecasting packages however little attention has been paid to linking the method with inventory control i e using the generated forecasts to calculate the inventory control parameters in this study we consider the most commonly applied order up to policy and show how the forecasts can be used to calculate order up to levels in a numerical study we show that the calculated order up to levels lead to service levels that are close to their targets

reducing inventory system costs by using robust demand estimators applications of inventory theory typically use historical data to estimate demand distribution parameters imprecise knowledge of the demand distribution adds to the usual replenishment costs associated with stochastic demands only limited research has been directed at the problem of choosing cost effective statistical procedures for estimating these parameters available theoretical findings on estimating the demand parameters for s s inventory replenishment policies are limited by their restrictive assumptions the impact on total system cost of using the sample mean and standard deviation as compared to robust parameter estimators has not been tested this paper explores the circumstances under which the cost due to statistical estimation can be substantially reduced by a better choice of estimators specifically an exponentially smoothed average and a modified exponentially smoothed mean absolute deviation are shown to outperform the sample mean and standard deviation for a wide range of computer simulated and u s air force empirical demands when the s s policies are calculated using ehrhardt s power approximation those situations in which the method of demand parameter estimation has negligible impact on total system cost are also indicated

demand for home modifications a household production function approach coping with activity limitations that occur in old age is an important issue in the context of increasing life expectancy and the still inevitable onset of chronic conditions in old age elderly households can be viewed as coping with activity limitations by producing functionality with the use of direct inputs that include home modifications a model of the household production of functionality is developed within the general framework of household production function models of health hypotheses generated from this model are tested using a logit model for data from the survey of asset and health dynamics among the oldest old ahead the main findings of the empirical analysis are that the demand for home modifications is fairly income inelastic home modifications and personal care are substitutes to some degree the demand for home modifications increases with years of schooling and that particular health conditions and the use of other assistive devices are important determinants of the demand for

how much demand should be fulfilled we study the inventory replenishment of a product whose demand can be manipulated by restricting the supply this research is motivated by a novel marketing tactic employed by manufacturers of fashion and luxury items such a tactic combines innovative marketing with deliberate understocking in an attempt to create shortages i e waitlists that add to the allure and sense of exclusivity of a product and stimulate its demand we model the problem as a finite horizon periodic review system where demand in each period is a decreasing function of the net ending inventory in the previous period although the optimal structure can be complex in general under certain conditions we are able to characterize the optimal policy as a state dependent monotone base stock policy we compare this policy with the optimal policy for the case in which demand is independent of the net inventory we also show that understocking is optimal in various scenarios we then propose a novel strategy called the inventory withholding strategy to further explore the wait list effect by making customers wait even when there is inventory on hand to satisfy them our numerical experiments study the impact of various model parameters in combination with the wait list effect on the optimal policy and the corresponding expected profits

a test for moral hazard in the labor market contractual arrangements effort and health moral hazard plays a central role in many models depicting contractual relationships involving worker effort the authors show how time series information on worker health consumption and work time can be used to measure the effort effects of payment schemes estimates from longitudinal data describing farming rural households indicate that time wage payment schemes and share tenancy contracts reduce effort compared to piece rate payment schemes and on farm employment the evidence also indicates consistent with moral hazard that the same workers consume more calories under a piece rate payment scheme or in on farm employment than when employed for time wages copyright 1994 by mit press

competition wages and teacher sorting lessons learned from a voucher reform this paper studies how local school competition affects teacher wages at markets where wages are set via individual wage bargaining using regional variation in private school entry generated by a swedish reform which allowed private schools to enter freely and a comprehensive matched employer employee data covering all high school teachers in sweden over 16 years i analyze the effects of competition on wages as well as labor flows the results suggest that competition translates into higher wages also for teachers in public schools while the average increases are modest new teachers gain 2 percent and high ability teachers in math and science receive 4 percent higher wages in the most competitive areas compared to areas without any competition from private schools several robustness checks support a causal interpretation of the results which together highlight the potential gains from school competition through a more differentiated wage setting of teachers

order quantity and timing flexibility in supply chains the role of demand characteristics we study how differences in product demand characteristics affect the strategic value of different types of supply chain flexibility for accurate response we propose a single period inventory modelling framework with two ordering opportunities the second order reflects updated demand information and potentially capitalizes on supply chain flexibility we consider two complementary forms of flexibility quantity flexibility in production and timing flexibility in scheduling in this framework we analyze the total inventory cost of a firm for alternate demand types we model functional products through the standard assumption of independent demand over the period fashion driven innovative products through a bayesian model and innovative products with evolving demand through a martingale process the three demand processes exhibit very different behavior with respect to the value of the alternate forms of flexibility we observe that quantity flexibility is of moderate value for functional goods and of high value for fashion driven products for all lead times quantity flexibility is of low value for goods with evolving demand with long lead times but of high value for short lead times alternately we observe timing flexibility is of highest value for functional goods especially for cases of high holding cost and is of lesser value for fashion driven goods it is of least value for goods with evolving demand both quantity and timing flexibility capabilities are required to significantly reduce the relevant supply chain costs for evolving demand innovative goods when the lead times are long

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

adaptive ordering and pricing for perishable products we consider the combined problem of pricing and ordering for a perishable product with unknown demand distribution and censored demand observations resulting from lost sales faced by a monopolistic retailer we develop an adaptive pricing and ordering policy with the asymptotic property that the average realized profit per period converges with probability one to the optimal value under complete information on the distribution the pricing mechanism is modeled as a multiarmed bandit problem while the order quantity decision made after the price level is established is based on a stochastic approximation procedure with multiplicative updates

schooling and seat belt use in the health production literature schooling has distinguished itself as the most important correlate of good health according to fuchs 11 economists have been so persuasive in their demonstration of the importance of schooling that medical researchers are increasingly drawing attention to lack of schooling as a risk factor in a variety of diseases 24 while there is little doubt among health economists about the empirical strength of the relation between schooling and health there is considerable controversy surrounding explanations for the empirical association grossman 15 16 maintains that schooling directly enhances health for example by increasing knowledge pertaining to healthy habits fuchs 11 12 on the other hand maintains that schooling serves as an empirical proxy for some unobserved third variable which also happens to be associated with health the third variable most frequently mentioned is time preference fuchs rejects the grossman idea that education plays a direct role in promoting health the debate has relevance for policy if schooling promotes health it could be argued that expenditures on schooling could be compared to for example expenditures on medical care as an alternative for increasing average life spans but if fuchs is right any expenditure comparisons would be futile

evaluation of forecasting error measurements and techniques for intermittent demand abstract this paper is a part and contribution to develop flexible and robust supply chain forecasting systems under changing market demands it suggests new tools and models to evaluate forecasting error measurements the paper especially studies slow moving or intermittent demand when for items the forecasting time periods often have zero demand for the difficult to forecast intermittent demand the croston forecasting technique is mostly regarded as a better choice than single exponential smoothing these two methods croston and single exponential smoothing together with two modifications of the croston method are discussed and evaluated with real intermittent data the apprehended performance of a forecasting technique is dependent of the chosen measurement of forecast errors the main purpose is to examine and evaluate different forecasting error measurements traditional measurements of forecast errors are studied mean absolute deviation mad mean square error mse etc together with new suggested error and bias measurements periods in stock pis and number of shortages nos pis considers the time aspect of the forecast error nos considers the development of cumulated forecast error cfe a complementary measure for descriptive statistics of time series is also suggested mean average change the conclusion through principal components analysis pca is that a single measure of forecast errors does not present the total different dimensions of the errors complementary error measures must be used

confidence based optimisation for the newsvendor problem under binomial poisson and exponential demand abstract we introduce a novel strategy to address the issue of demand estimation in single item single period stochastic inventory optimisation problems our strategy analytically combines confidence interval analysis and inventory optimisation we assume that the decision maker is given a set of past demand samples and we employ confidence interval analysis in order to identify a range of candidate order quantities that with prescribed confidence probability includes the real optimal order quantity for the underlying stochastic demand process with unknown stationary parameter s in addition for each candidate order quantity that is identified our approach produces an upper and a lower bound for the associated cost we apply this approach to three demand distributions in the exponential family binomial poisson and exponential for two of these distributions we also discuss the extension to the case of unobserved lost sales numerical examples are presented in which we show how our approach complements existing frequentist e g based on maximum likelihood estimators or bayesian strategies

on the stock control performance of intermittent demand estimators the purpose of this paper is to assess the empirical stock control performance of intermittent demand estimation procedures the forecasting methods considered are the simple moving average single exponential smoothing croston s method and a new method recently developed by the authors of this paper we first discuss the nature of the empirical demand data set 3000 stock keeping units and we specify the stock control model to be used for experimentation purposes performance measures are then selected to report customer service level and stock volume differences the out of sample empirical comparison results demonstrate the superior stock control performance of the new intermittent demand forecasting method and enable insights to be gained into the empirical utility of the other estimators

health behavior health knowledge and schooling the positive correlation between schooling and good health is well documented one explanation is that schooling helps people choose healthier life styles by improving their knowledge of the relationship between health behaviors and health outcomes that is schooling improves the household s allocative efficiency in producing health this empirical study uses direct measures of health knowledge to test this explanation part of the relationship between schooling and the consumption of cigarettes alcohol and exercise is explained by differences in health knowledge however most of schooling s effects on health behavior remain after differences in knowledge are controlled for

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

future capacity procurements under unknown demand and increasing costs in this paper we study a situation in which a broker must manage the procurement of a short life cycle product as the broker observes demand for the item she learns about the demand process however as is often the case in practice it becomes either more difficult or more expensive to procure the item as the selling season advances thus the broker must trade off higher procurement costs against the benefit of making ordering decisions with better information about demand problems of this type arise for example in the travel industry where a travel agent s cost of procuring airline and hotel reservations increases as the date of a vacation package approaches we develop a newsvendor like characterization of the optimal procurement policy in a numerical analysis we demonstrate how broker procurements tend to cluster just before price increases and how brokers can benefit from explicitly considering the effects of information about demand in their ordering policies

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

on the calculation of safety stocks when demand is forecasted the inventory control literature generally assumes that the demand distribution and all its parameters are known in practical applications it is often suggested to estimate the demand variance either directly or based on the one period ahead forecast errors the variance of the lead time demand essential for safety stock calculations is then obtained by multiplying the estimated per period demand variance by the length of the lead time however this is flawed since forecast errors for different periods of the lead time are positively correlated even if the demand process itself does not show process auto correlation as a result these traditional procedures lead to safety stocks that are too low this paper presents corrected lead time demand variance expressions and reorder levels for inventory systems with a constant lead time where demand fluctuates around a constant level firstly we derive the exact lead time forecast error of mean demand conditional on the true demand variance secondly we derive for normally distributed demand the correct reorder level under uncertainty of both the demand mean and variance we show how the results can be implemented in inventory models and particularly discuss batch ordering policies combined with moving average and exponential smoothing forecasts we find that traditional approaches can lead to safety stocks that are up to 30 percent too low and service levels that are up to 10 percent below the target

forecasting and inventory performance in a two stage supply chain with arima 0 1 1 demand theory and empirical analysis the arima 0 1 1 demand model has been analysed extensively by researchers and used widely by forecasting practitioners due to its attractive theoretical properties and empirical evidence in its support however no empirical investigations have been conducted in the academic literature to analyse demand forecasting and inventory performance under such a demand model in this paper we consider a supply chain formed by a manufacturer and a retailer facing an arima 0 1 1 demand process the relationship between the forecasting accuracy and inventory performance is analysed along with an investigation on the potential benefits of forecast information sharing between the retailer and the manufacturer results are obtained analytically but also empirically by means of experimentation with the sales data related to 329 stock keeping units skus from a major european superstore our analysis contributes towards the development of the current state of knowledge in the areas of inventory forecasting and forecast information sharing and offers insights that should be valuable from the practitioner perspective

a theory of health investment under competing mortality risks in this paper we present a theory of health investment when there are multiple causes of death we analyze the optimal investment rules and the comparative statics in particular we examine the conditions under which a cause specific intervention has a spillover effect by spillover effect we mean a price reduction in one cause specific health investment would increase all other investments we also study the strength of the spillover effect which is measured in terms of cross price elasticities we find that while a cause specific intervention might not be wasteful the spillover effect would not be large either

optimal minimum bids and inventory scrapping in sequential single unit vickrey auctions with demand learning abstract retailers often conduct sequential single unit auctions and need to decide the minimum bid in each auction to reduce inventory costs it may be optimal to scrap some of the inventory rather than holding it until it is auctioned off in some auctions the seller may be uncertain about the market response and hence may want to dynamically learn the demand by observing the number of posted bids we formulate a markov decision process mdp to study this dynamic auction design problem under the vickrey mechanism we first develop a clairvoyant model where the seller knows the demand distribution we prove that it is optimal to scrap all inventory above a certain threshold and then auction the remaining units we derive a first order necessary condition whereby the bidders virtual value at an optimal minimum bid equals the seller s marginal profit this is a generalization of riley and samuelson s result from the one single unit auction case when the virtual value is strictly increasing this necessary condition is also sufficient and leads to a structured value iteration algorithm we then assume that the number of bidders is poisson distributed but the seller does not know its mean the seller uses a mixture of gamma prior on this mean and updates this belief over several auctions this results in a high dimensional bayesian mdp whose exact solution is intractable we therefore propose and compare two approximation methods called certainty equivalent control cec and q function approximation numerical experiments suggest that q function approximation can attain higher revenues than cec

what is the price of prevention new evidence from a field experiment how does increasing access to treatment affect the demand for preventive testing in this paper we present results from a field experiment in nigeria in which we offered cervical cancer screening to women at randomly chosen prices to test our hypothesis we also offered women a lottery where the payoff was a subsidy towards the cost of cervical cancer treatment conditional upon a diagnosis of cervical cancer we find that women randomly selected to receive the conditional cancer treatment subsidy were about 4 percentage points more likely to take up screening than those in the control group we also show that reducing the price of screening by 10 cents increased take up by about 1 percentage point these results offer compelling evidence that the optimal set of subsidies to increase take up of preventive testing in developing countries must include subsidies towards treatment costs in addition to price subsidies

management of a periodic review inventory system using bayesian model averaging when new marketing efforts are made abstract many companies invest in various marketing efforts such as price promotion and advertising in order to attract new customers and build customer loyalty this paper examines the problem of setting efficient inventory levels when new marketing efforts are made and product demand is autocorrelated we assume that the inventory manager operates with a base stock policy based on a critical fractile if marketing has a temporary effect the underlying demand tends to revert to a long term equilibrium trend and the inventory manager needs to use a stationary demand model e g autoregressive model to determine the required inventory level in contrast if the effect is permanent demand shocks contain an element that represents a permanent departure from previous levels and a non stationary demand model e g random walk needs to be used instead we show that the required inventory behaves much differently for the case of using a stationary demand model as opposed to a non stationary model but it is difficult in practice to identify a correct demand model in the absence of a long sampling span in this paper we propose an inventory model that explicitly acknowledges uncertainty over stationary and non stationary demand models in response to new marketing efforts the proposed model averages the inventory policies of the two demand models weighted by each model s posterior probability this is an extension of bayesian model averaging simulation results demonstrate that the bayesian model averaging inventory model improves the inventory system

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

intermittent demand forecasts with neural networks intermittent demand appears when demand events occur only sporadically typically such time series have few observations making intermittent demand forecasting challenging forecast errors can be costly in terms of unmet demand or obsolescent stock intermittent demand forecasting has been addressed using established forecasting methods including simple moving averages exponential smoothing and croston s method with its variants this study proposes a neural network nn methodology to forecast intermittent time series these nns are used to provide dynamic demand rate forecasts which do not assume constant demand rate in the future and can capture interactions between the non zero demand and the inter arrival rate of demand events overcoming the limitations of croston s method in order to mitigate the issue of limited fitting sample which is common in intermittent demand the proposed models use regularised training and median ensembles over multiple training initialisations to produce robust forecasts the nns are evaluated against established benchmarks using both forecasting accuracy and inventory metrics the findings of forecasting and inventory metrics are conflicting while nns achieved poor forecasting accuracy and bias all nn variants achieved higher service levels than the best performing croston s method variant without requiring analogous increases in stock holding volume therefore nns are found to be effective for intermittent demand applications this study provides further arguments and evidence against the use of conventional forecasting accuracy metrics to evaluate forecasting methods for intermittent demand concluding that attention to inventory metrics is desirable

analysis of order up to level inventory systems with compound poisson demand abstract we analyse a single echelon single item inventory system where the demand and the lead time are stochastic demand is modelled as a compound poisson process and the stock is controlled according to a continuous time order up to out level policy we propose a method for determining the optimal out level for cost oriented inventory systems where unfilled demands are backordered we first establish an analytical characterization of the optimal out level the actual calculation is based on a numerical procedure the accuracy of which can be set as highly as desired by means of a numerical investigation we show that the method is very efficient in calculating the optimal out level we compare our results with those obtained using an approximation proposed in the literature and we show that there is a significant difference in accuracy for slow moving items our work allows insights to be gained on stock control related issues for both fast and slow moving stock keeping units skus

coordination mechanisms of supply chain systems supply chain management scm has become an important management paradigm as supply chain members are often separate and independent economic entities a key issue in scm is to develop mechanisms that can align their objectives and coordinate their activities so as to optimize system performance in this paper we provide a review of coordination mechanisms of supply chain systems in a framework that is based on supply chain decision structure and nature of demand this framework highlights the behavioral aspects and information need in the coordination of a supply chain the identification of these issues points out several directions of future research in this area

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

pre season stocking and pricing decisions for fashion retailers with multiple information updating abstract motivated by the industrial practice we investigate in this paper the pre season inventory and pricing decisions for fashion retailers before the selling season a retailer can place orders for a seasonal fashion product from her supplier at two distinct stages via two different delivery modes market information from the sales of a pre seasonal product is collected and used to update the demand forecast of the seasonal product at the succeeding stages by using bayesian approach we formulate a dynamic optimization problem and obtain the optimal stocking policy after the ordered seasonal product has arrived and just before the start of the selling season the retailer can determine the optimal selling price of the product with respect to the latest demand information and the amount of product on hand we study the pricing policy under different objectives sensitivity analysis is carried out and the features of the policies are revealed managerial insights are generated

preventive care underused even when free is there something else at work explaining the rationale of preventive care underuse is a difficult task considering its great benefits for health underuse is even more difficult to explain in countries like italy where preventive care can be obtained for free in this article we investigate the determinants of prevention underuse with an empirical model based on human capital theory which also includes three factors to which little attention has been paid so far role played by the general practitioner gp nonmonetary barriers to access and health beliefs we apply a recursive probit model explaining both recourse to prevention and to the gp which allows us to adequately measure the effect of the latter on the former and to quantitatively compare the determinants of curative and preventive care we find that the gp plays a minor role in prevention use but that nonmonetary barriers to access and health beliefs are strong determinants of preventive care demand finally we also find support for both grossman s capital depreciation the

assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function inventory models need some specification of the distribution of demand in order to find the optimal order up to level or reorder point this distribution is unknown in real life and there are several solutions to overcome this problem one approach is to assume a distribution estimate its parameters and replace the unknown demand parameters by these estimates in the theoretically correct model earlier research suggests that this approach will lead to underperformance even if the true demand distribution is indeed the assumed one this paper directs the cause of the underperformance and quantifies it in case of normally distributed demand furthermore the formulae for the order up to levels are corrected analytically where possible and otherwise by use of simulation and linear regression simulation shows that these corrections improve the attained performance

health insurance and the demand for medical care evidence from a randomized experiment we estimate how cost sharing the portion of the bill the patient pays affects the demand for medical services the data come from a randomized experiment a catastrophic insurance plan reduces expenditures 31 percent relative to zero out of pocket price the price elasticity is approximately 0 2 we reject the hypothesis that less favorable coverage of outpatient services increases total expenditure for example by deterring preventive care or inducing hospitalization

teacher quality in public and private schools under a voucher system the case of chile chile is unusual in having long term experience with nationwide school vouchers a key criticism of school voucher systems is that they make it easier for private schools to attract better teachers to the detriment of public schools this paper uses longitudinal data from chile to estimate a discrete choice dynamic programming dcdp model of teacher and nonteacher labor supply decisions and to explore how wage policies affect the composition of the teacher labor force in public and private schools in the model individuals first decide whether to get a teaching degree and then choose annually from among five work home sector alternatives empirical results show that private voucher schools attract better teachers than public schools however the existence of the private voucher sector also draws higher productivity individuals into the teaching profession

scarf s state reduction method flexibility and a dependent demand inventory model we consider a finite horizon inventory model with linear holding shortage and ordering costs the demand random variables are dependent and average demand is described by an exponential smoothing formula this model can be formulated as a two state variable inventory level weighted past demands dynamic program using a procedure first developed by scarf for a bayesian inventory model we are able to reformulate the model as a one state variable dynamic program resulting in a considerable computational saving over the two state variable formulation we also show that this dependent demand model orders less than or equal the amount ordered by a comparable independent demand model we establish this result under the assumption that demand represents failures that are repaired by the beginning of the next period

the demand for preventive medical care the idea that people invest in health capital is an essential part of models of the demand for health but the investment motives behind health decision are often obscured by other factors this empirical paper investigates the demand for adult preventive medical care where the investment motives are relatively clear cut several important results demonstrate the usefulness of the approach first the analysis finds that annual use of two preventive services decreases with age although not the only plausible explanation the results are consistent with individuals shortens over the lifecycle second schooling is found to be an important determinant of demand with the more educated much more likely to use the services neither lifecycle nor schooling effects are consistently found in studies of the demand for culture care finally the empirical analysis also provides additional evidence on the responsiveness of the demand for preventive care to change in insurance coverage an important issue for heal

teacher sorting teacher quality and student composition abstract using panel data for norwegian schools we establish a two equation supply and demand model for teachers with approved education taking into account nationally determined teacher pay and a strict teacher appointment rule the data enable us to separately estimate supply and demand functions for certified teachers the results clearly indicate that the student body composition and in particular students belonging to ethnic minorities influences both teacher supply and teacher demand the implied negative relationship between excess demand for certified teachers and the share of minority students is likely to be important for teacher quality

do alternative opportunities matter the role of female labor markets in the decline of teacher quality abstract this paper documents the widely perceived but little investigated notion that teachers today are less qualified than they once were evidence of a marked decline in the quality of young women going into teaching between 1960 and 1990 is presented using standardized test scores undergraduate institution selectivity and positive assortative mating characteristics as indicators of quality in contrast the quality of young women becoming professionals increased the roy model of self selection highlights how occupational differences in the returns to skill determine teacher quality estimates suggest the significance of increasing professional opportunities for women in affecting the decline in teacher quality

design for postponement a comprehensive characterization of its benefits under unknown demand distributions recent papers have developed analytical models to explain and quantify the benefits of delayed differentiation and quick response programs these models assume that while demands in each period are random they are independent across time and their distribution is perfectly known i e sales forecasts do not need to be updated as time progresses in this paper we characterize these benefits in more general settings where parameters of the demand distributions fail to be known with accuracy or where consecutive demands are correlated here it is necessary to revise estimates of the parameters of the demand distributions on the basis of observed demand data we analyze these systems in a bayesian framework assuming that our initial information about the parameters of the demand distributions is characterized via prior distributions we also characterize the structure of close to optimal ordering rules in these systems for a variety of types of order cost functions

a postponement model for demand management in this paper we analyze demand postponement as a strategy to handle potential demand surges under demand postponement a fraction of the demands from the regular period are postponed and satisfied during a postponement period this permits capacity to be procured to satisfy the postponed demands a reimbursement per unit is paid to customers whose demands are postponed the basic idea is that by preempting stockouts through demand postponement we can reduce overall stockout costs we formulate and solve a two stage capacity planning problem under demand postponement we propose a power range class of distributions to capture the nature of demand surges we establish the scalability and conjugate properties of the power range distributions under demand postponement which leads to a tractable analysis of the problem we analytically solve the problem of determining the optimal regular and postponement period capacities and the demand splitting rule to minimize the supplier s expected cost we show that a the value of postponement may be significant depending on cost and demand parameters b a postponement strategy may lead to reduced investment in initial capacity and c it may be optimal to do no demand postponement over a range of demands even after observing a higher demand signal we then relax several model assumptions and provide results for these extensions we conclude with managerial insights

adapting wright s modification of holt s method to forecasting intermittent demand abstract the challenge of accurately forecasting demand for spare parts is due to the intermittent nature of their demand this study compares a recent modification of croston s method and a method based on holt s double exponential smoothing taking the firm s competitive priorities into consideration wright s modification of holt s method is presented as a viable alternative when forecasting demand for spares especially when trend is present results indicate that firms focusing on minimizing inventory levels as a priority should consider forecasting using the modified croston s method if their priority is high customer service then the modified holt s method is superior

a tri level optimization model for inventory control with uncertain demand and lead time abstract we propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty the objective is to make ordering decisions to minimize the total system cost we introduce a two stage tri level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions in addition an exact algorithm is designed to solve the model we compare this model in a case study with three decision making strategies optimistic moderate and pessimistic our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters such as holding cost and shortage cost whereas the new tri level optimization model almost always results in the lowest total cost in all parameter settings

supply contracts with financial hedging we study the performance of a stylized supply chain where two firms a retailer and a producer compete in a stackelberg game the retailer purchases a single product from the producer and afterward sells it in the retail market at a stochastic clearance price the retailer however is budget constrained and is therefore limited in the number of units that he may purchase from the producer we also assume that the retailer s profit depends in part on the realized path or terminal value of some observable stochastic process we interpret this process as a financial process such as a foreign exchange rate or interest rate more generally the process can be interpreted as any relevant economic index we consider a variation the flexible contract of the traditional wholesale price contract that is offered by the producer to the retailer under this flexible contract at t 0 the producer offers a menu of wholesale prices to the retailer one for each realization of the financial process up to a future time the retailer then commits to purchasing at time a variable number of units with the specific quantity depending on the realization of the process up to time because of the retailer s budget constraint the supply chain might be more profitable if the retailer was able to shift some of the budget from states where the constraint is not binding to states where it is binding we therefore consider a variation of the flexible contract where we assume that the retailer is able to trade dynamically between zero and in the financial market we refer to this variation as the flexible contract with hedging we compare the decentralized competitive solution for the two contracts with the solutions obtained by a central planner we also compare the supply chain s performance across the two contracts we find for example that the producer always prefers the flexible contract with hedging to the flexible contract without hedging depending on model parameters however the retailer might or might not prefer the flexible contract with hedging

on the bias of intermittent demand estimates forecasting and inventory control for intermittent demand items has been a major problem in the manufacturing and supply environment croston operational research quarterly 23 1972 289 proposed a method according to which intermittent demand estimates can be built from constituent events croston s method has been reported to be a robust method but has shown more modest benefits in forecasting accuracy than expected in this research one of the causes of this unexpected performance has been identified as a first step towards improving croston s method certain limitations are identified in croston s approach and a correction in his derivation of the expected estimate of demand per time period is presented in addition a modification to his method that gives approximately unbiased demand per period estimates is introduced all the conclusions are confirmed by means of an extended simulation experiment where croston s and revised croston s methods are compared the forecasting accuracy comparison corresponds to a situation of an inventory control system employing a re order interval or product group review

an improved method for forecasting spare parts demand using extreme value theory inventory control for spare parts is essential for many organizations due to the trade off between preventing high holding cost and stockouts the lead time demand distribution plays a central role in inventory control the estimation of this distribution is problematic as the spare part demand is often intermittent and as a consequence often only a limited number of non zero data points are available in practice the well known empirical method uses historical demand data to construct the lead time demand distribution although it performs reasonably well when service requirements are relatively low it has difficulties in achieving high target service levels in this paper we improve the empirical method by applying extreme value theory to model the tail of the lead time demand distribution to make the most out of a limited number of demand observations we establish that extreme value theory can be applied to lead time demand periods computed over overlapping intervals we consider two service levels the expected waiting time and cycle service level our experiments show that our method improves the inventory performance compared to the empirical method and is competitive with the wss method croston s method and sba for a range of demand distributions

style goods pricing with demand learning for many industries e g apparel retailing managing demand through price adjustments is often the only tool left to companies once the replenishment decisions are made a significant amount of uncertainty about the magnitude and price sensitivity of demand can be resolved using the early sales information in this study a bayesian model is developed to summarize sales information and pricing history in an efficient way this model is incorporated into a periodic pricing model to optimize revenues for a given stock of items over a finite horizon a computational study is carried out in order to find out the circumstances under which learning is most beneficial the model is extended to allow for replenishments within the season in order to understand global sourcing decisions made by apparel retailers some of the findings are empirically validated using data from u s apparel industry

returns to seniority among public school teachers returns to seniority account for a substantial share of public k 12 expenditures over the first ten to 15 years of a career public school teachers enjoy average wage growth at least equivalent to that of other white collar workers explanations for this structure in terms of human capital or costly monitoring lack theoretical and empirical support a steeper wage tenure profile reduces turnover but it is doubtful that the costs of turnover are high enough to make this an optimal use of school resources we conclude that the structure of teacher pay in public education is more consistent with rent seeking than efficient contracting

demand forecasting for multiple slow moving items with short requests history and unequal demand variance modeling the lead time demand for the multiple slow moving inventory items in the case when the available requests history is very short is a challenge for inventory management the classical forecasting technique which is based on the aggregation of the stock keeping units to overcome the mentioned historical data peculiarity is known to lead to very poor performance in many cases important for industrial applications an alternative approach to the demand forecasting for the considered problem is based on the bayesian paradigm when the initially developed population averaged demand probability distribution is modified for each item using its specific requests history this paper follows this approach and presents a new model which relies on the beta distribution as a prior for the request probability and allows to account for disparity in variance of demand between different stock keeping units to estimate the model parameters a special computationally effective technique based on the generalized method of moments is developed simulation results indicate the superiority of the proposed model over the known ones while the computational burden does not increase

short term booking of air cargo space this paper proposes a stochastic dynamic programming model for a short term capacity planning model for air cargo space long term cargo space is usually acquired by freight forwarders or shippers many months ahead on a contract basis and usually the forecasted demand is unreliable a re planning of cargo space is needed when the date draws nearer to the flight departure time hence for a given amount of long term contract space the decision for each stage is the quantity of additional space required for the next stage and the decision planning model evaluates the optimal cost policy based on the economic trade off between the cost of backlogged shipment and the cost of acquiring additional cargo space under certain conditions we show that the return function is convex with respect to the additional space acquired for a given state and the optimal expected cost for the remaining stages is an increasing convex function with respect to the state variables these two properties can be carried backward recursively and therefore the optimal cost policy can be determined efficiently

the accuracy of a modified croston procedure a modified croston procedure to forecast intermittent demand is reviewed the modified procedure published in this journal in 2004 consists of a new method for estimating the mean demand and a smoothing method for estimating the variance of the forecasted demand rate it is found that the smoothed variance estimate is based on an invalid measure of forecast accuracy moreover it is shown that the method of forecasting mean demand produces biased forecasts the accuracy of this method is assessed using simulated data and the mean square error measure and compared with croston s method and single exponential smoothing it is found that croston s method is generally more accurate than its modification particularly for strongly intermittent series

a target level of risk model of respiratory pathologies and smoking behaviour levels of morbidity and healthcare utilisation related to respiratory pathologies are correlated firstly with an indicator of the level of risk that individuals are prepared to take namely their individual target level of risk and then secondly with smoking behaviour the results based on a french survey with a sample of 13 150 individuals show that higher target levels of risk are associated with a higher probability of being ill with higher levels of tobacco consumption but with lower levels of consumption of preventive medicine more generally this article shows how a target level of risk model can be formalized in an economic optimizing approach

adaptive data driven inventory control with censored demand based on kaplan meier estimator using the well known product limit form of the kaplan meier estimator from statistics we propose a new class of nonparametric adaptive data driven policies for stochastic inventory control problems we focus on the distribution free newsvendor model with censored demands the assumption is that the demand distribution is not known and there are only sales data available we study the theoretical performance of the new policies and show that for discrete demand distributions they converge almost surely to the set of optimal solutions computational experiments suggest that the new policies converge for general demand distributions not necessarily discrete and demonstrate that they are significantly more robust than previously known policies as a by product of the theoretical analysis we obtain new results on the asymptotic consistency of the kaplan meier estimator for discrete random variables that extend existing work in statistics to the best of our knowledge this is the first application of the kaplan meier estimator within an adaptive optimization algorithm in particular the first application to stochastic inventory control models we believe that this work will lead to additional applications in other domains

a comparison of the optimal ordering levels of bayesian and non bayesian inventory models although it is often the case that the parameters of the distribution of demand are not known with certainty and that a bayesian formulation would be appropriate such an approach is generally not used in inventory calculations for computational reasons since one often resorts to a non bayesian formulation it is of interest to compare bayesian policies with a comparable non bayesian policy using the concept of flexibility it was anticipated that the quantity ordered under the non bayesian policy would be greater than or equal to that under a bayesian policy this result is established for the n period nondepletive inventory model however a two period counterexample is given for the standard depletive inventory model

stopped myopic policies in some inventory models with generalized demand processes this paper considers single item inventory systems with immediate delivery and no economies of scale bounds are provided on the value loss relative to optimal cost for restricting attention to the class of inventory stocking policies that behave myopically up to a specified stopping time the stopping time may be random and may depend on demand histories as well as information exogenous to the firm the bounds are robust to the nature of the demand process faced after the stopping time so are applicable when the statistics of demand after the stopping time are unknown stopping times that are large with high probability imply that near term decisions are completely specified with high probability the general bounding results allow one to consider demand processes that may otherwise be analytically intractable it is shown that the class of demand models for which the assumption of additive i i d shocks is appropriate is the same class admitting effective myopic stocking policies the bounds also make rigorous the intuitive notion that myopic policies are least effective in systems with precipitous drops in demand coupled with an inability to recover cash invested in inventory the option of selling inventory at discount is a reality in many real systems and enhances the attractiveness of the myopic stocking policy in numerical examples the myopic policy is shown to be an effective competitor in a range of systems for which optimal policies are not known the results suggest that for systems with immediate delivery no economies of scale and no currently known optimal policy the myopic stocking rule is a reasonable default policy to adopt

stalking information bayesian inventory management with unobserved lost sales retailers are frequently uncertain about the underlying demand distribution of a new product when taking the empirical bayesian approach of scarf 1959 they simultaneously stock the product over time and learn about the distribution assuming that unmet demand is lost and unobserved this learning must be based on observing sales rather than demand which differs from sales in the event of a stockout using the framework and results of braden and freimer 1991 the cumulative learning about the underlying demand distribution is captured by two parameters a scale parameter that reflects the predicted size of the underlying market and a shape parameter that indicates both the size of the market and the precision with which the underlying distribution is known an important simplification result of scarf 1960 and azoury 1985 which allows the scale parameter to be removed from the optimization is shown to extend to this setting we present examples that reveal two interesting phenomena 1 a retailer may hope that compared to stocking out realized demand will be strictly less than the stock level even though stocking out would signal a stochastically larger demand distribution and 2 it can be optimal to drop a product after a period of successful sales we also present specific conditions under which the following results hold 1 investment in excess stocks to enhance learning will occur in every dynamic problem and 2 a product is never dropped after a period of poor sales the model is extended to multiple independent markets whose distributions depend proportionately on a single unknown parameter we argue that smaller markets should be given better service as an effective means of acquiring information

self selection in the market of teachers public school teachers are usually paid according to centralized earning schedules in which their income depends mainly on experience by contrast in private schools there is high wage dispersion and salaries correspond mainly to teachers performance that dichotomous labour regulation encourages teachers with better unobservable skills to self select into private schools because the likelihood of earning higher wages is higher than in public schools the other side of the coin is the self selection of bad teachers into public schools using a representative sample of chilean teachers we estimate a two sector roy model to test self selection we find evidence of negative self selection of teachers into public schools

exploiting elapsed time for managing intermittent demand for spare parts we present an intermittent demand forecasting method that conditions on the elapsed time since the last demand occurrence to anticipate incoming demand and show using empirical data that this can substantially reduce both stock investment and lost revenue for spare parts management we extensively benchmark our method against existing forecasting and bootstrapping methods on forecast accuracy and inventory performance and demonstrate that its performance is robust under general conditions our method is the first to incorporate that activities at the demand side such as aggregation of demand preventive and corrective maintenance can lead to a positive relation between demand size and inter arrival time of demand occurrences by anticipating incoming demand our method offers substantial financial gains

the effect of patient s asymmetric information problem on elderly use of medical care it has been proposed that the patient s relative ignorance about medicine asymmetry of information between doctor and patient should place a patient in a disadvantaged position when purchasing medical care even if rapidly developing information technologies can enhance the patient s access to health information a patient doesn t have enough ability to understand interpret and evaluate it so the doctor s effort at sincerely helping patient understand and utilize health information by effective communication with patient might improve the patient s asymmetric information problem and affect the patient s use of medical care by way of its being a source of patient s solid trust on doctor this research seeks to determine whether a doctor s effort mentioned earlier can affect the elderly use of medical care this study used data from a survey sample of people aged more than 65 living in seoul and chuncheon korea the results suggest the doctor s effort level has a statistically significant positive

information acquisition for capacity planning via pricing and advance selling when to stop and act this paper investigates a capacity planning strategy that collects commitments to purchase before the capacity decision and uses the acquired advance sales information to decide on the capacity in particular we study a profit maximization model in which a manufacturer collects advance sales information periodically prior to the regular sales season for a capacity decision customer demand is stochastic and price sensitive once the capacity is set the manufacturer produces and satisfies customer demand to the extent possible from the installed capacity during the regular sales period we study scenarios in which the advance sales and regular sales season prices are set exogenously and optimally for both scenarios we establish the optimality of a control band or a threshold policy that determines when to stop acquiring advance sales information and how much capacity to build we show that advance selling can improve the manufacturer s profit significantly we generate insights into how operating conditions such as the capacity building cost and market characteristics such as demand variability affect the value of information acquired through advance selling from this analysis we identify the conditions under which advance selling for capacity planning is most valuable finally we study the joint benefits of acquiring information for capacity planning through advance selling and revenue management of installed capacity through dynamic pricing

let them eat cake a note on comparing alternative models of the demand for medical care this article comments on the rand econometric approach to estimating and testing models of the demand for medical care as typified in duan et al 1983 the rand methodology is contrasted with more mainstream econometric approaches to similar empirical problems as developed in the sample selection literature it is shown that despite rand s claim their approach requires some fairly unusual assumptions on the model joint error distribution and functional form alternatively their model likelihood functions may be interpreted as being nested in more general sample selection model likelihood functions with easily testable parameter restrictions

the effects of integrating management judgement into intermittent demand forecasts empirical research suggests that quantitatively derived forecasts are very frequently judgementally adjusted nevertheless little work has been conducted to evaluate the performance of these judgemental adjustments in a practical demand sales context in addition the relevant analysis does not distinguish between slow and fast moving items currently there are neither conceptual developments nor empirical evidence on the issue of integrating judgements and statistical forecasts for slow intermittent demand items moreover no results have ever been reported on the stock control implications of these human judgements our work analyses monthly intermittent demand forecasts for the uk branch of a major international pharmaceutical company the company relies upon a commercially available statistical forecasting system to produce forecasts that are subsequently judgementally adjusted based on marketing intelligence gathered by the company forecasters the benefits of the intervention are evaluated by comparing the actual sales to system and final forecasts using both forecast accuracy and inventory control accuracy implication metrics our study allows insights to be gained on potential improvements to intermittent demand forecasting processes and subsequently the design effectiveness of forecasting support systems

lumpy demand forecasting using neural networks abstract the current study applies neural network nn modeling in forecasting lumpy demand it is to the best of our knowledge the first such study our study compares the performance of nn forecasts to those using three traditional time series methods single exponential smoothing croston s method and the syntetos boylan approximation we find nn models to generally perform better than the traditional methods using three different performance measures we also independently validate earlier findings that the syntetos boylan approximation performs better than the croston s and single exponential smoothing methods in lumpy demand forecasting

optimal health insurance for prevention and treatment this paper reexamines the efficiency based arguments for optimal health insurance extending the classic analysis to consider optimal coverage for prevention and treatment separately our paper considers the tradeoff between individuals risk reduction on the one hand and both ex ante and ex post moral hazard on the other we demonstrate that it is always desirable to offer at least some insurance coverage for preventive care if individual consumers ignore the impact of their preventive care on the health premium using a utility based framework we reconfirm the conventional tradeoff between risk avoidance by risk sharing and moral hazard for insuring treatment goods uncompensated losses that reduce effective income provide a new efficiency based argument for more generous insurance coverage for prevention and treatment of health conditions the optimal coinsurance rates for prevention and for treatment are not identical

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

a comparison of alternative models for the demand for medical care we have tested alternative models of the demand for medical care using experimental data the estimated response of demand to insurance plan is sensitive to the model used we therefore use a split sample analysis and find that a model that more closely approximates distributional assumptions and uses a nonparametric retransformation factor performs better in terms of mean squared forecast error simpler models are inferior either because they are not robust to outliers e g anova anocova or because they are inconsistent when strong distributional assumptions are violated e g a two parameter box cox transformation

multi period inventory games with information update this paper addresses the multi period inventory game of two substitutable products with learning in which the demand distribution form in each period is known but one or more parameters are unknown in advance the retailers update the estimates of the unknown parameters using the historical demand information as the transaction proceeds at each period the retailers check the initial inventories and update the forecasts about their demands first then they place the orders which are delivered immediately finally the demands are realized and satisfied by the available inventories the unsatisfied demand of one retailer switches to another and is satisfied by the other s left inventory unsatisfied demand is lost and the excess inventories incur a holding cost all the information is common knowledge the objectives of the two retailers are to make proper ordering decisions to maximize their total expected discount profits respectively we model the problem as a dynamic bayesian game with multi dimension state space we give the conditions under which the state space can be reduced we prove the existence and uniqueness of nash equilibrium and show that the equilibrium ordering strategy is the base stock policy the computation of the base stock levels can be reduced to a game with one dimension state space moreover for the case with perishable product the computation of the base stock levels can be reduced to the newsboy game

multi level approaches to demand management in complex environments an analytical model abstract recent studies have shown that as demand becomes irregular and complex i e lumpy a possible approach for managing such uncertainty is to collect information directly from customers this implies that the sales units have to move closer to customers analyse their likely requirements and collect quantitative and structured data as well as qualitative and subjective insights however as integration with individual customers increases and data collection capabilities improve the organisational configuration of most companies becomes ever more complex and the aggregation of forecasts more difficult this paper discusses two approaches to managing demand uncertainty in complex environments in the first termed decentralised order overplanning sales units are responsible for forecasting the demand of each customer and defining requirements in the second termed centralised order overplanning forecasts provided by sales units are aggregated and further elaborated by manufacturing to define item requirements by means of an analytical model which describes the forecasting and planning process as a bayesian markovian process we show that the centralised method out performs the decentralised approach by virtue of the ability to exploit the additional information provided by commonalities between customers requests however this advantage has to be balanced against organisational costs since the centralised method splits responsibilities for forecasting and slack control between sales and manufacturing units major conflicts are likely to arise the focus and commitment on forecasting accuracy may be compromised and information may be lost when individual forecasts are sent to the manufacturing unit

adaptive inventory control for nonstationary demand and partial information this paper examines several different policies for an inventory control problem in which the demand process is nonstationary and partially observed the probability distribution for the demand in each period is determined by the state of a markov chain the core process however the state of this core process is not directly observed only the actual demand is observed by the decision maker given this demand process the inventory control problem is a composite state partially observed markov decision process pomdp which is an appropriate model for a number of dynamic demand problems in practice managers often use certainty equivalent control cec policies to solve such a problem however this paper presents results that demonstrate that there are other practical control policies that almost always provide much better solutions for this problem than the cec policies commonly used in practice the computational results also indicate how specific problem characteristics influence the performance of each of the alternative policies

control of inventories with intermittent demand abstract the problem of controlling inventories with intermittent demands is considered a method for determining re order levels consistent with a specified customer service level is proposed the distinguishing feature is the use of a probability distribution with a spike at zero to represent the relative frequency of periods with no transactions

inventory management based on target oriented robust optimization we propose a target oriented robust optimization approach to solve a multiproduct multiperiod inventory management problem subject to ordering capacity constraints we assume the demand for each product in each period is characterized by an uncertainty set that depends only on a reference value and the bounds of the demand our goal is to find an ordering policy that maximizes the sizes of all the uncertainty sets such that all demand realizations from the sets will result in a total cost lower than a prespecified cost target we prove that a static decision rule is optimal for an approximate formulation of the problem which significantly reduces the computation burden by tuning the cost target the resultant policy can achieve a balance between the expected cost and the associated cost variance numerical experiments suggest that although only limited demand information is used the proposed approach performs comparably to traditional methods based on dynamic programming and stochastic programming mo