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

dynamic pricing with real time demand learning in many service industries the firm adjusts the product price dynamically by taking into account the current product inventory and the future demand distribution because the firm can easily monitor the product inventory the success of dynamic pricing relies on an accurate demand forecast in this paper we consider a situation where the firm does not have an accurate demand forecast but can only roughly estimate the customer arrival rate before the sale begins as the sale moves forward the firm uses real time sales data to fine tune this arrival rate estimation we show how the firm can first use this modified arrival rate estimation to forecast the future demand distribution with better precision and then use the new information to dynamically adjust the product price in order to maximize the expected total revenue numerical study shows that this strategy not only is nearly optimal but also is robust when the true customer arrival rate is much different from the original forecast finally we extend the results to four situations commonly encountered in practice unobservable lost customers time dependent arrival rate batch demand and discrete set of allowable prices

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

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

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

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

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

optimal policies for multiechelon inventory problems with markov modulated demand this paper considers a multistage serial inventory system with markov modulated demand random demand arises at stage 1 stage 1 orders from stage 2 etc and stage n orders from an outside supplier with unlimited stock the demand distribution in each period is determined by the current state of an exogenous markov chain excess demand is backlogged linear holding costs are incurred at every stage and linear backorder costs are incurred at stage 1 the ordering costs are also linear the objective is to minimize the long run average costs in the system the paper shows that the optimal policy is an echelon base stock policy with state dependent order up to levels an efficient algorithm is also provided for determining the optimal base stock levels the results can be extended to serial systems in which there is a fixed ordering cost at stage n and to assembly systems with linear ordering costs

reducing the cost of demand uncertainty through accurate response to early sales traditionally fashion products have incurred high losses due to stockouts and inventory obsolence because long lead times coupled with a concentrated selling season force all or at least most production to be committed before demand information is available under a quick response system lead times are shortened sufficiently to allow a greater portion of production to be scheduled in response to initial demand we model and analyze the decisions required under quick response and give a method for estimating the demand probability distributions needed in our model we applied these procedures with a major fashion skiwear firm and found that cost relative to the current informal response system was reduced by enough to increase profits by 60 relative to the cost that would have been incurred if no response were used optimized response reduces cost by enough to roughly quadruple profits

forecasting and inventory management of short life cycle products in this paper we provide an integrated framework for forecasting and inventory management of short life cycle products the literature on forecasting and inventory management does not adequately address issues relating to short life cycle products we first propose a growth model that can be used to obtain accurate monthly forecasts for the entire life cycle of the product the model avoids limiting data requirements of traditional methods instead it extracts relevant information from past product histories and utilizes the information on total life cycle sales and the peak sales timing using disguised demand data from a personal computer pc manufacturer we validate the model next we model the inventory management problem for the short life cycle environment the uncertainty in demand is modeled through the uncertainty in the realized values of the parameters of the forecasting model the high cost of terminal inventory shortages and rapidly changing procurement costs are all included in the model extensions to the basic model are also developed using optimal control theory we derive a solution that provides valuable information on procurement cutoff time and terminal service levels a detailed example explains the characteristics of the policy and its relevance in decision making many of the issues covered in the models were brought to our attention while implementing a forecasting model at a pc manufacturer the benchmark monthly forecasts and the associated inventory levels provide information that can be very helpful in planning and controlling marketing sales and production

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

a non stationary periodic review production inventory model with uncertain production capacity and uncertain demand abstract in this paper we consider a nonstationary periodic review dynamic production inventory model with uncertain production capacity and uncertain demand the maximum production capacity varies stochastically it is known that order up to or base stock critical number policies are optimal for both finite horizon problems and infinite horizon problems we obtain upper and lower bounds of the optimal order up to levels and show that for an infinite horizon problem the upper and the lower bounds of the optimal order up to levels for the finite horizon counterparts converge as the planning horizons considered get longer furthermore under mild conditions the differences between the upper and the lower bounds converge exponentially to zero

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

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

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

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

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

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

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

adapting the newsvendor model for infinite horizon inventory systems abstract we address inventory decisions in the context of the reorder point order quantity policy in infinite horizon stochastic lead time demand inventory systems in which the parameters may be non stationary we develop a heuristic policy based on the simple economic order quantity eoq model and the one period newsvendor model using illustrative numerical examples we demonstrate that our simple and easy to understand heuristic policy performs nearly as well as the optimal policy derived from complicated mathematical procedures our heuristic policy is simple easy to implement and flexible in that it can be easily adapted to situations when the parameters of the inventory system may change analytical modeling under non stationary parameters would be extremely complex and the derivation of closed form optimal policies is mathematically intractable in most cases in such cases practitioners will find our heuristic policy an attractive option

privatization and economic growth in australia the shorthand of a long process the impact of privatization on economic growth has been little investigated relative to disaggregated approaches a growth accounting framework is used here to investigate the impact of privatization on growth for the australian economy the contribution of public capital to the private sector and whether the growth process is endogenous or solow is evaluated separate measures of public and private capital are computed in order to estimate their impacts with labour on australian gross domestic product gdp growth for the period 1960 to 2003 a simple growth rates version is found preferred by stationarity and other tests labour growth appears to strongly positively influence the growth of gdp in contrast public capital growth has no statistically significant effect on gdp growth or on private capital productivity the data are consistent with the hypothesis that the coefficients of the growth equation are the same before and during privatization

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

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

optimal policy for a muti product multi period non stationary stochastic production and customization problem abstract this paper is concerned with a multi product multi period non stationary stochastic production and customization problem in which a firm wishes to satisfy multi period stochastic demands for m products the firm produces a generic product that can be customized into the m products the lead time to produce the generic product is one period the generic product can be customized into any of the m products immediately assume that the costs of production customization storage and shortage are linear also assume that the unsatisfied demands for the m products at the firm in each period are lost the objective of the firm is to find a production customization policy that minimizes the expected cost over a planning horizon we obtain the conditions that ensure that a myopic production customization policy is optimal

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

loss averse newsvendor model with two ordering opportunities and market information updating abstract a fashion supply chain characterized by a long lead time and a short selling season is considered in this paper facing demand uncertainty the risk averse retailer has two opportunities to make order decisions before the demand is realized the risk aversion is modelled as a penalty to the decision maker the retailer if a target profit is not attained we derive the retailer s optimal ordering decisions and analyze the monotonicity behaviours of the critical market signal the optimal first stage order quantity and the optimal expected payoff with respect to the penalty coefficient we also examine the impact of demand forecast quality on the retailer s decisions and extend the study to the case where order cancellation is allowed

optimal policy structure characterization for a two period dual sourcing inventory control model with forecast updating a proposed single product stochastic two period inventory control model combines demand forecast updating with the flexibility of two supply sources demand is modeled by two independent random variables over a two period selling season at the beginning of the first period two quantities are ordered using two different supply options a local supplier who delivers the ordered quantity immediately and a second supplier who delivers the ordered quantity at the beginning of the second period the local supplier charges a higher cost per ordered unit the model considers an initial inventory so the decision maker has an opportunity at the beginning of the first period to return part of the available inventory to the supplier or sell it in a parallel market at the end of the first period any unsatisfied demand is backlogged to be satisfied in the next period at the beginning of the second period exogenous market information updates the second period demand forecast according to this updated forecast and the actual inventory level an additional quantity is ordered using the local procurement source or another quantity is returned to the supplier or sold in a parallel market with a dynamic programming approach this research exhibits the structure of the optimal policy characterized by order up to and salvage up to levels the findings provide the structure of the second period conditional optimal policy and analytical insights that characterize the first period optimal policy furthermore a numerical study reveals the impact of information quality on the optimal policy and the trade off between the two procurement options

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

myopic solutions of markov decision processes and stochastic games sufficient conditions are presented for a markov decision process to have a myopic optimum and for a stochastic game to possess a myopic equilibrium point an optimum or an equilibrium point is said to be myopic if it can be deduced from an optimum or an equilibrium point of a static optimization problem or a static nash game the principal conditions are a each single period reward is the sum of terms due to the current state and action b each transition probability depends on the action taken but not on the state from which the transition occurs and c an appropriate static optimum or equilibrium point is ad infinitum repeatable these conditions are satisfied by several dynamic oligopoly models and numerous markov decision processes

human development and alcohol abuse in adolescence the aim of this article is to analyse the development of human capital in adolescents as represented by their state of health and educational attainment when this capital is affected by the abusive consumption of alcohol measured by grams of ethanol to that end we adopt a theoretical framework derived from the model of rational addiction the model of health demand and the model of schooling demand which is then estimated by using data drawn from the spanish national survey on drug use in the school population 1996 1998 2000 results confirm that higher alcohol consumption leads to worse health states and lower academic efficiency among adolescents which suggests the need to implement effective policies aimed at reducing alcohol abuse among this population group

managing inventory with multiple products lags in delivery resource constraints and lost sales a mathematical programming approach this paper develops an order up tos inventory model that is designed to handle multiple items resource constraints lags in delivery and lost sales without sacrificing computational simplicity mild conditions are shown to ensure that the expected average holding cost and the expected average shortage cost are separable convex functions of the order up to levels we develop nonparametric estimates of these costs and use them in conjunction with linear programming to produce what is termed the lp policy the lp policy has two major advantages over traditional methods first it can be computed in complex environments such as the one described above and second it does not require an explicit functional form of demand something that is difficult to specify accurately in practice in two numerical experiments designed so that optimal policies could be computed the lp policy fared well differing from the optimal profit by an average of 2 20 and 1 84 respectively these results compare quite favorably with the errors incurred in traditional methods when a correctly specified distribution uses estimated parameters our findings support the effectiveness of this mathematical programming technique for approximating complex real world inventory control problems

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

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