demand seasonality in retail inventory management abstract we investigate the value of accounting for demand seasonality in inventory control our problem is motivated by discussions with retailers who admitted to not taking perceived seasonality patterns into account in their replenishment systems we consider a single location single item periodic review lost sales inventory problem with seasonal demand in a retail environment customer demand has seasonality with a known season length the lead time is shorter than the review period and orders are placed as multiples of a fixed batch size the cost structure comprises of a fixed cost per order a cost per batch and a unit variable cost to model retail handling costs we consider four different settings which differ in the degree of demand seasonality that is incorporated in the model with or without within review period variations and with or without across review periods variations in each case we calculate the policy which minimizes the long run average cost and compute the optimality gaps of the policies which ignore part or all demand seasonality we find that not accounting for demand seasonality can lead to substantial optimality gaps yet incorporating only some form of demand seasonality does not always lead to cost savings we apply the problem to a real life setting using point of sales data from a european retailer we show that a simple distinction between weekday and weekend sales can lead to major cost reductions without greatly increasing the complexity of the retailer s automatic store ordering system our analysis provides valuable insights on the tradeoff between the complexity of the automatic store ordering system and the benefits of incorporating demand seasonality

on the structure of lost sales inventory models we provide a new approach to the structural analysis of the standard lost sales inventory system this approach is we think easier to work with than the original one we also derive new bounds on the optimal policy then we show that more variable demand leads to higher cost finally we extend the analysis to several important variations of the basic model

the volcker rule and corporate bond market making in times of stress abstract focusing on downgrades as stress events that drive the selling of corporate bonds we show that the illiquidity of stressed bonds has increased after the volcker rule dealers regulated by the rule have curtailed their market making activities and non volcker affected dealers have not offset the decreased activities of volcker affected dealers furthermore even volcker affected dealers that are not constrained by basel iii and comprehensive capital analysis and review regulations change their behavior inconsistent with the effects being driven by these other regulations because volcker affected dealers have been the main liquidity providers bonds have become less liquid during times of stress due to the volcker rule

optimal policies for a capacitated two echelon inventory system this paper demonstrates optimal policies for capacitated serial multiechelon production inventory systems extending the clark and scarf 1960 model to include installations with production capacity limits we demonstrate that a modified echelon base stock policy is optimal in a two stage system when there is a smaller capacity at the downstream facility this is shown by decomposing the dynamic programming value function into value functions dependent upon individual echelon stock variables we show that the optimal structure holds for both stationary and nonstationary stochastic customer demand finite horizon and infinite horizon results are included under discounted cost and average cost criteria

estimating the lead time demand distribution when the daily demand is non normal and autocorrelated hon shiang lau abstract this paper develops convenient formulas for estimating the probability distribution of lead time demand when the inventory item s daily demand follows a probability distribution of any arbitrary shape and when simple univariate models can be found for the autocorrelated daily demand series numerical examples are presented to illustrate the use of these formulas the numerical results also indicate that significant error may be incurred when inventory decisions are made without proper consideration of the autocorrelations or the arbitrary distribution shapes of daily demands

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

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

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

on the 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

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

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

dealer balance sheets and bond liquidity provision abstract do regulations decrease dealer ability to intermediate trades using a unique dataset of dealer bond level transactions we link changes in liquidity of individual u s corporate bonds to dealers transaction activity and balance sheet constraints we show that prior to the financial crisis bonds traded by more levered institutions and institutions with investment bank like characteristics were more liquid but this relationship reverses after the financial crisis in addition institutions that face more regulations after the crisis both reduce their overall volume of trade and have less ability to intermediate customer trades

a single item inventory model for expected inventory order crossovers abstract expected inventory order crossovers occur if at the moment of ordering it is expected that orders will not arrive in the sequence they are ordered recent research has shown that a expected inventory order crossovers will be encountered more frequently in future and that b use of a myopic order up to policy based on a stochastic dynamic programming approach leads to improved performance compared to the classical approach in this paper we show that the improved policy is still heuristic in nature as it neglects several control options that are available on the various ordering moments and makes some restrictive assumptions with respect to the separability i e decomposability of the stochastic dynamic programming problem we propose further improvements in the policy for situations where a quadratic cost function is appropriate

note the newsvendor model with endogenous demand this paper considers a firm s price and inventory policy when it faces uncertain demand that depends on both price and inventory level the authors extend the classic newsvendor model by assuming that expected utility maximizing consumers choose between visiting the firm and consuming an exogenous outside option the outside option represents the utility the consumer forgoes when she chooses to visit the firm before knowing whether or not the product will be available the authors investigate both the case in which the firm s price is exogenous and the case in which price is chosen optimally the paper makes two contributions first the authors show that the firm holds more inventories provides a higher fill rate attracts more customers and earns higher profits when it internalizes the effect of its inventory on demand second the authors show that in the endogenous price case the firm s two dimensional decision problem can be reduced to two sequential single variable optimizations as a result the endogenous price case is as easy to solve as the exogenous price case

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

a periodic review model with serially correlated inventory level dependent demand abstract it has been established that the demand rates of many consumer products are serially correlated and are dependent on the amount of inventory displayed to the customer a periodic review model is developed that considers both of these effects an adaptive base stock policy based on the critical fractile is presented and a solution methodology is developed the effect of this demand pattern on the safety stock requirement is investigated

some insights regarding the optimal reorder period in periodic review inventory systems abstract the periodic review inventory system is not only pervasive but also has an extensive literature dealing with various aspects from its theoretical underpinnings through to its performance however the selection of the best review period and its behaviour with respect to basic inventory parameters such as demand and supply variability appears to be poorly understood in this paper we treat the normal and gamma distributions which are probably the two most commonly used distributions of demand over the key protection time interval in a periodic review system we show that the total cost function is not necessarily well behaved as a function of the review period in particular it can be non convex which complicates the determination of the best value of the review interval also we demonstrate and explain how the best review period changes as the values of various parameters are modified some of these changes are initially counterintuitive in nature managerially oriented interpretations should also be helpful to organisations seeking improvement in inventory system performance

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

do investment banks matter for m a returns w e document a significant investment bank fixed effect in the announcement returns of m a deals the interquartile range of bank fixed effects is 1 26 compared with a fullsample average return of 0 72 the results remain significant after controlling for the component of returns attributable to the acquirer our findings suggest that investment banks matter for m a outcomes and contrast earlier studies that show no positive link between various measures of advisor quality and m a returns differences in average returns across banks are also persistent over time and predictable from prior performance clients do not chase past returns which may explain why persistence exists in m a performance while it is absent in mutual funds jel g24 g34 mer gers and acquisitions m a are among the most critical decisions a ceo can make successful mergers can create substantial synergies while misguided acquisitions can lead to misallocation of companies to parents unable to reap their full potential in addition to these large effects on shareholder value a bad acquisition also increases the ceo s risk of being fired lehn and zhao 2006 a prominent example is the departure of carly fiorina from hewlett packard which was widely attributed to her acquisition of compaq the quality of m a transactions is also of great importance to the economy as a whole the total value of m a announced by a u s acquirer in 2007 was 2 1 trillion around 15 of gdp

dynamic inventory management with total minimum order commitments and two supply options we study optimal policies for dual supply inventory systems where a firm commits to buying a total minimum quantity from both supplies or two separate total minimum quantities from each supply over a finite planning horizon the two supply options differ in their costs and lead times for the system under the joint commitment the optimal policy has a simple structure and can be fully characterized by three critical numbers in each period for the system under the separate commitments we show that the optimal order quantities in each period are monotonic in the starting inventory and the remaining commitments with bounded sensitivity moreover we establish convergence results on the optimal order quantities when the committed quantity from either supply approaches infinity these findings highlight the effects of order commitments on the optimal inventory policies on the basis of these results we develop a simple heuristic for the system under separate commitments and show numerically that it performs

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

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

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

investment banks as corporate monitors in the early twentieth century united states we study the effect of financial relationships on firms investment decisions and access to external finance in the early twentieth century securities underwriters commonly held directorships with american corporations section 10 of the clayton antitrust act prohibited bankers from serving on the boards of railroads for which they underwrote securities we find that following the implementation of section 10 railroads with strong preexisting relationships with underwriters saw declines in their investment rates valuations and leverage and increases in their costs of external funds reassuringly we do not observe similar effects among industrials and utilities which were not subject to section 10 our results are consistent with underwriters on corporate boards acting as delegated monitors and highlight the potential for regulations intended to address conflicts of interest to disrupt valuable information flows

an inventory model with deteriorating items under inflation when a delay in payment is permissible abstract this study develops an inventory model for initial stock dependent consumption rate when a delay in payment is permissible in the inventory model shortages are not allowed the effect of the inflation rate deterioration rate initial stock dependent consumption rate and delay in payment are discussed in the study mathematical models are also derived under two different circumstances i e case i the credit period is less than or equal to the cycle time for settling the account and case ii the credit period is greater than the cycle time for settling the account besides expressions for an inventory system s total cost are derived for these two cases moreover a computational procedure and gino lasdon et al acm transactions mathematical software 4 1978 34 50 are proposed to obtain the optimal order size and cycle time the results can help managers determine the optimal total cost finally a numerical example demonstrates the applicability of the proposed model

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

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

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

on the optimal policy structure in serial inventory systems with lost sales we study a periodically reviewed serial inventory system in which excess demand from external customers is lost we derive elementary properties of the vector of optimal order quantities in this system in particular we derive bounds on the sensitivity or more mathematically the derivative of the optimal order quantity at each stage to the vector of the current inventory levels our analysis uses the concept of l natural convexity which was studied in discrete convex analysis and recently used in the study of single stage inventory systems with lost sales we also remark on how our analysis extends to models with capacity constraints and or backordering

a model for optimizing retail space allocations the allocation of scarce shelf space among competing products is a central problem in retailing space allocation affects store profitability through both the demand function where both main and cross space elasticities have to be considered and through the cost function procurement carrying and out of stock costs a model is developed which uniquely incorporates both effects a case study is used to estimate the parameters and the problem is solved within a geometrical programming framework an extensive comparison with alternative procedures suggests this general model leads to significantly different allocation rules and superior profit performance

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

safety stock determination with correlated demands and arbitrary lead times abstract in this new paper a new method is proposed for determining the safety stock when daily demands are autocorrelated and lead times follow an arbitrary distribution the method is then used as a basis for examining effects on the safety stock when the usual assumptions namely independence and normality of daily demand and normality of lead times are violated the new method is derived by using inequalities on the basis of probability arguments it is simple to use and provides an upper bound of the safety stock further numerical investigation based on the new method shows that the effect of autocorrelation is more pronounced than departure from normality in determining the safety stock substantial error may be incurred if such autocorrelation is ignored in the test cases

a decomposition approach for a class of capacitated serial systems we study a class of two echelon serial systems with identical ordering production capacities or limits for both echelons demands are assumed to be integer valued for the case where the lead time to the upstream echelon is one period the optimality of state dependent modified echelon base stock policies is proved using a decomposition approach for the case where the upstream lead time is two periods we introduce a new class of policies called two tier base stock policies and prove their optimality some insight about the inventory control problem in n echelon serial systems with identical capacities at all stages and arbitrary lead times everywhere is also provided we argue that a generalization of two tier base stock policies which we call multitier base stock policies are optimal for these systems we also provide a bound on the number of parameters required to specify the optimal policy

coordinating inventory control and pricing strategies under batch ordering in this paper we investigate joint pricing and inventory control problems in a finite horizon single product periodic review setting with certain uncertain supply capacities the demands in different periods are random variables whose distributions depend on the posted price exhibiting the additive form the order quantity in each period is required to be of integral multiples of a given specific batch size denoted by q inventory replenishment incurs a linear ordering cost referred to as the cost rate function the sum of holding and backorder costs can either be convex or quasi convex the objective is to determine a joint ordering and pricing decision that can maximize the total expected profit over the planning horizon we first consider the case in which the cost rate function is convex and show that the modifiedr q list price policy is optimal for the system with certain and limited capacities a special case of which is the r q list price policy when capacities become unlimited as supply capacities become random the optimal policy follows a new structure wherein the optimal order up to level and posted price must be coordinated to make the optimal safety stock level follow the r q policy we further consider the case of a quasi convex cost rate function which may arise when a service level constraint is used as a surrogate for the shortage cost we demonstrate that the r q list price policy is optimal for the system without supply capacity constraints in addition extensions to several other models are discussed the enabling technique is based on the notion of q jump convexity and its variants

technical note a note on the structure of joint inventory pricing control with leadtimes we consider a joint inventory pricing control problem for a periodic review single stage inventory system with a positive order leadtime and a linear order cost demands in consecutive periods are independent but their distributions depend on the price in accordance with a stochastic demand function of additive form pricing and ordering decisions are made simultaneously at the beginning of each period the objective is to maximize the total expected discounted profit over a finite horizon we partially characterize the structure of the optimal joint ordering and pricing policies we also show that our structural analysis can be extended to a multistage or serial inventory system with constant or stochastic leadtimes and an assemble to order system with price sensitive demand

optimal centralized ordering policies in multi echelon inventory systems with correlated demands this paper treats a depot warehouse system in which demand occurs at the warehouse or retail level this work differs from a number of other studies in that we allow item demands to be correlated both across warehouses and also correlated in time our motivation for this generalization arises from our experience with an actual system of this type used by a major national producer and distributor of consumer products we observed both high correlations between successive monthly demands around 0 7 and correlations between demands for an item at different locations also about 0 7 in a given time period we derive an explicit expression for the optimal safety stock as a function of the level of correlation through time the analysis requires two assumptions 1 the allocation assumption and 2 the equal coefficient of variation assumption similar assumptions have been used by other researchers finally numerical evaluations are included to illustrate the impact of the various magnitudes of correlation

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

coordinating inventory control and pricing strategies for perishable products we analyze a joint pricing and inventory control problem for a perishable product with a fixed lifetime over a finite horizon in each period demand depends on the price of the current period plus an additive random term inventories can be intentionally disposed of and those that reach their lifetime have to be disposed of the objective is to find a joint pricing ordering and disposal policy to maximize the total expected discounted profit over the planning horizon taking into account linear ordering cost inventory holding and backlogging or lost sales penalty cost and disposal cost employing the concept of l concavity we show some monotonicity properties of the optimal policies our results shed new light on perishable inventory management and our approach provides a significantly simpler proof of a classical structural result in the literature moreover we identify bounds on the optimal order up to levels and develop an effective heuristic policy numerical results show that our heuristic policy performs well in both stationary and nonstationary settings finally we show that our approach also applies to models with random lifetimes and inventory rationing models with multiple demand classes

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