Inventory Rationing in a Make‐to‐Stock System with Batch Production and Lost Sales

We address an inventory rationing problem in a lost sales make‐to‐stock (MTS) production system with batch ordering and multiple demand classes. Each production order contains a single batch of a fixed lot size and the processing time of each batch is random. Assuming that there is at most one order outstanding at any point in time, we first address the case with the general production time distribution. We show that the optimal order policy is characterized by a reorder point and the optimal rationing policy is characterized by time‐dependent rationing levels. We then approximate the production time distribution with a phase‐type distribution and show that the optimal policy can be characterized by a reorder point and state‐dependent rationing levels. Using the Erlang production time distribution, we generalize the model to a tandem MTS system in which there may be multiple outstanding orders. We introduce a state‐transformation approach to perform the structural analysis and show that both the reorder point and rationing levels are state dependent. We show the monotonicity of the optimal reorder point and rationing levels for the outstanding orders, and generate new theoretical and managerial insights from the research findings.     

Optimal Production and Admission Policies in Make‐to‐Stock/Make‐to‐Order Manufacturing Systems

In this article, we study optimal production and admission control policies in manufacturing systems that produce two types of products: one type consists of identical items that are produced to stock, while the other has varying features and is produced to order. The model is motivated by applications from various industries, in particular, the automobile industry, where a part supplier receives orders from both an original equipment manufacturer and the aftermarket. The product for the original equipment manufacturer is produced to stock, it has higher priority, and its demands are fully accepted. The aftermarket product is produced to order, and its demands can be either accepted or rejected. We characterize the optimal production and admission policies with a partial‐linear structure, and using computational analysis, we provide insights into the benefits of the new policies. We also investigate the impact of production capacity, cost structure, and demand structure on system performance.     

A Note on the Relationship Among Capacity,Pricing, and Inventory in a Make‐to‐Stock System

We address the simultaneous determination of pricing, production, and capacity investment decisions by a monopolistic firm in a multi‐period setting under demand uncertainty. We analyze the optimal decision with particular emphasis on the relationship between price and capacity. We consider models that allow for either bi‐directional price changes or models with markdowns only, and in the latter case we prove that capacity and price are strategic substitutes.     

产能和需求不确定情形下ATO系统最优库存和生产决策研究

通过将产成品的最终组装环节延迟到观察到实际需求以后进行,按订单组装(Assemble-to-Order,ATO)策略能够有效避免按库存生产(Make-to-Stock)策略下因为生产过剩而带来的损失和风险。然而,在未来产能有限的情形下,生产商必须提前组装部分产成品,以最大化自身收益。本文考虑一个未来需求和组装产能同时具有不确定性的单周期ATO系统:在观察到实际需求以前,生产商必须准备好所有的零部件库存,并组装适量的产成品;观察到实际需求和实际可用组装能力以后,则根据需要进一步追加产成品产量,以尽可能满足客户需求,从而最大化自身利益。通过构建一个两阶段规划模型,我们研究了生产商的最优零部件库存和最优生产决策;并进一步考察了生产商可以通过紧急外包来获取额外组装产能时的最优决策。研究结果为随机环境下生产商合理采购零部件库存和合理安排生产提供了有益的管理启示。     

Replenishment Policies for Multi‐Product Stochastic Inventory Systems with Correlated Demand and Joint‐Replenishment Costs

This study analyzes optimal replenishment policies that minimize expected discounted cost of multi‐product stochastic inventory systems. The distinguishing feature of the multi‐product inventory system that we analyze is the existence of correlated demand and joint‐replenishment costs across multiple products. Our objective is to understand the structure of the optimal policy and use this structure to construct a heuristic method that can solve problems set in real‐world sizes/dimensions. Using an MDP formulation we first compute the optimal policy. The optimal policy can only be computed for problems with a small number of product types due to the curse of dimensionality. Hence, using the insight gained from the optimal policy, we propose a class of policies that captures the impact of demand correlation on the structure of the optimal policy. We call this class (s, c, d, S)‐policies, and also develop an algorithm to compute good policies in this class, for large multi‐product problems. Finally using an exhaustive set of computational examples we show that policies in this class very closely approximate the optimal policy and can outperform policies analyzed in prior literature which assume independent demand. We have also included examples that illustrate performance under the average cost objective.     

Static and Dynamic Pricing of Excess Capacity in a Make‐to‐Order Environment

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.     

Managing Quality in the E‐Service System: Development and Application of a Process Model

In this paper, we develop a process model for assessing and managing e‐service quality based on the underlying components of the e‐service system and, in turn, address the growing need to look in more detail at the system component level for sources of poor quality. The proposed process model is comprised of a set of entities representing the e‐service system, a network defining the linking between all pairs of entities via transactions and product flows, and a set of outcomes of the processes in terms of quality dimensions. The process model is developed using Unified Modeling Language (UML), a pictorial language for specifying service designs that has achieved widespread acceptance among e‐service designers. Examples of applications of the process model are presented to illustrate how the model can be use to identify operational levers for managing and improving e‐service quality.