Product Design for Life‐Cycle Mismatch

Life‐cycle mismatch occurs when the life cycle of a product does not coincide with the life cycles of the parts used in that product. This is particularly a problem with products that contain electronic components that sometimes have life spans of only two years. The cost of mitigating component obsolescence, which may require redesigning the product, is often considerable. Thus, prudent product design necessitates the selection of electronic components and product architecture, considering the cost of mitigating an obsolete design and other costs related to the design and manufacture of a product. Accordingly, we develop and analyze a model that shows how a product design can be effectively tailored to a particular product's life cycle.     

Joint Life‐Cycle Dynamics of New and Remanufactured Products

Many products considered for remanufacturing are durables that exhibit a well‐pronounced product life cycle—they diffuse gradually through the market. The remanufactured product, which is a cheaper substitute for the new product, is often put on the market during the life cycle of the new product and affects its sales dynamics. In this paper, we study the integrated dynamic management of a portfolio of new and remanufactured products that progressively penetrate a potential market over the product life cycle. To this end, we extend the Bass diffusion model in a way that maintains the two essential features of remanufacturing settings: (a) substitution between new and remanufactured products, and (b) a constraint on the diffusion of remanufactured products due to the limited supply of used products that can be remanufactured. We identify characteristics of the diffusion paths of new and remanufactured products. Finally, we analyze the impact of levers such as remanufacturability level, capacity profile and reverse channel speed on profitability.     

Product Life‐Cycle Management of Packaged Software

A software product becomes less valuable for its consumers over time due to technological and economic obsolescence. As a result, firms have an opportunity to introduce and sell upgrades that provide higher utility to consumers compared to an older and out‐of‐date software product. In a market that is growing and consists of homogeneous customers, we prove that the optimal upgrade intervals are monotonically increasing throughout the product's life cycle solely because of demand and cost considerations. This finding is in conformity with empirical evidence, thus validating our theoretical model. We then present comparative statics results to show that increase in the rate of obsolescence or network externalities may sometimes increase upgrade intervals for early upgrades and decrease these for later upgrades in the product's life cycle, but increase in market growth rate always decreases these intervals. Further, when successive software upgrades are forward compatible, upgrade intervals are longer than when they are not. Finally, we present three separate extensions of our model to showcase the robustness of our results. Since upgrade development costs depend on upgrade intervals, these insights help managers understand how costing for upgrades changes over the product's life cycle.     

Design for the Environment: Life‐Cycle Approach Using a Newsvendor Model

Introducing environmental innovations in product and process design can affect the product's cost and demand, as well as the environmental impact in different stages of its life cycle (such as manufacturing and use stages). In this article, we advance understanding on where such design changes can be most effective economically to the firm and examine their corresponding environmental consequences. We consider a profit maximizing firm (newsvendor) deciding on the production quantity as well as its environmentally focused design efforts. We focus our results along the two dimensions of demand characteristics and life‐cycle environmental impact levels, specifically functional vs. innovative products, and higher manufacturing stage environmental impact vs. higher use stage environmental impact. We also discuss the environmental impact of overproduction and how it relates to the different types of products and their salvage options. We find that although the environmental impact per unit always improves when firms use eco‐efficient or demand‐enhancing innovations, the total environmental impact can either increase or decrease due to increased production quantities. We identify the conditions for such cases by looking at the environmentally focused design efforts needed to compensate for the increase in production. We also show that the environmental impact of overproduction plays an important role in the overall environmental impact of the firm. We conclude by applying our model to different product categories.     

Optimal Pricing,Ordering, and Return Policies for Consumer Goods

Our research addresses a firm that sells a product to consumers who are sensitive to both price and return policy. The operational decisions of interest are the selling price, return policy, and quantity of new product to purchase. We model a single selling season that is split into two periods where the boundary between periods is delineated by the opportunity to recover product returns and resell them. That is, returns in the first period can be recovered and sold in the second period. Returns also arise in the second period, but these may only be salvaged. We first analyze both deterministic and stochastic models, finding that the deterministic results largely carry over to the stochastic case. In addition, our results indicate that the model is quite insensitive to errors in the estimates of the parameter values, except for purchase cost and parameters related to demand. Finally, we perform an analysis on the value of various investments to improve financial performance. Results indicate that investments to reduce the recovery cost of returns or reduce returns uncertainty are minimal, while investments to increase recovery speed, reduce market uncertainty, and reduce the return rate can be quite valuable.     

Stabilized‐Cycle Strategy for Capacitated Lot Sizing with Multiple Products: Fill‐Rate Constraints in Rolling Schedules

In practice, deterministic, multi‐period lot‐sizing models are implemented in rolling schedules since this allows the revision of decisions beyond the frozen horizon. Thus, rolling schedules are able to take realizations and updated forecasts of uncertain data (e.g., customer demands) into account. Furthermore, it is common to hold safety stocks to ensure given service levels (e.g., fill rate). As we will show, this approach, implemented in rolling schedules, often results in increased setup and holding costs while (over‐)accomplishing given fill rates. A well‐known alternative to deterministic planning models are stochastic, static, multi‐period planning models used in the static uncertainty strategy, which results in stable plans. However, these models have a lack of flexibility to react to the realization of uncertain data. As a result, actual costs may differ widely from planned costs, and downside deviations of actual fill rates from those given are very high. We propose a new strategy, namely the stabilized cycle. This combines and expands upon ideas from the literature for minimizing setup and holding costs in rolling schedules, while controlling actual product‐specific fill rates for a finite reporting period. A computational study with a multi‐item capacitated medium‐term production planning model has been executed in rolling schedules. On the one hand, it demonstrates that the stabilized‐cycle strategy yields a good compromise between costs and downside deviations. Furthermore, the stabilized‐cycle strategy weakly dominates the order‐based strategy for both constant and seasonal demands.     

Optimal Purchase and Transportation Cost Lot Sizing for a Single Item

A review of the literature indicates that the traditional approach for evaluating quantity discount offerings for purchased items has not adequately considered the effect that transportation costs may have on the optimal order quantity; despite the general fact that purchased materials must bear transportation charges. The transportation cost structure for less-than-truckload (LTL) shipments reflects sizable reductions in freight rates when the shipment size exceeds one of the nominal rate breakpoints. However, the shipper must also be aware of the opportunity to reduce total freight costs by artificially inflating the actual shipping weight to the next rate breakpoint, in order that a lower marginal tariff is achieved for the entire shipment. Such over-declared shipments result in an effective freight rate schedule that is characterized by constant fixed charge segments in addition to the nominal marginal rates. Over-declared shipments are economical when the shipment volume is less than the rate breakpoint, but greater than a cost indifference point between the two adjacent marginal rates. This paper presents a simple analytical procedure for finding the order quantity that minimizes total purchase costs which reflect both transportation economies and quantity discounts. After first solving for the series of indifference points that apply to a particular freight rate schedule, a total purchase cost expression is presented that properly accounts for the actual transportation cost structure. The optimal purchase order quantity will be one of the four following possibilities: (1) the valid economic order quantity (EOQ), QC; (2) a purchase price breakpoint in excess of QC; (3) a transportation rate breakpoint in excess of QC; and (4) a modified EOQ which provides an over-declared shipment in excess of QC. Finally, an algorithm which systematically explores these four possibilities is presented and illustrated with a numerical example.     

A Sales Forecast Model for Short‐Life‐Cycle Products: New Releases at Blockbuster

We develop, in this article, a sales model for movie and game products at Blockbuster. The model assumes that there are three sales components: the first is from consumers who have already committed to purchasing (or renting) a product (e.g., based on promotion of, or exposure to, the product prior to its launch); the second comes from consumers who are potential buyers of the product; and the third comes from either a networking effect on closely tied (as in a social group) potential buyers from previous buyers (in the case of movie rental and all retail products) or re‐rents (in the case of game rental). In addition, we explicitly formulate into our model dynamic interactions between these sales components, both within and across sales periods. This important feature is motivated by realism, and it significantly contributes to the accuracy of our model. The model is thoroughly tested against sales data for rental and retail products from Blockbuster. Our empirical results show that the model offers excellent fit to actual sales activity. We also demonstrate that the model is capable of delivering reasonable sales forecasts based solely on environmental data (e.g., theatrical sales, studio, genre, MPAA ratings, etc.) and actual first‐period sales. Accurate sales forecasts can lead to significant cost savings. In particular, it can improve the retail operations at Blockbuster by determining appropriate order quantities of products, which is critical in effective inventory management (i.e., it can reduce the extent of over‐stocking and under‐stocking). While our model is developed specifically for product sales at Blockbuster, we believe that with context‐dependent modifications, our modeling approach could also provide a reasonable basis for the study of sales for other short‐Life‐Cycle products.     

Robust Stochastic Lot‐Sizing by Means of Histograms

Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control using the estimated distributions. These approaches often lead to fragile solutions whenever the preselected family of distributions was inadequate. In this article, we propose a minimax robust model that integrates data fitting and inventory optimization for the single‐item multi‐period periodic review stochastic lot‐sizing problem. In contrast with the standard assumption of given distributions, we assume that histograms are part of the input. The robust model generalizes the Bayesian model, and it can be interpreted as minimizing history‐dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular, we analyze the robust model based on the chi‐square goodness‐of‐fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under generous conditions. Its effectiveness is also validated by numerical experiments.     

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.     

Development and analysis of mathematical and simulation models of decision-making tools for remanufacturing

This article presents decision-making tools for remanufacturing. The first decision-making tool was used to address inventory lot-sizing problems in a hybrid remanufacturing–manufacturing system with varying remanufacturing fraction. In this article, the new inventory lot-sizing model with variable remanufacturing lot sizes has been shown to exhibit better performance than the benchmark model with fixed remanufacturing lot sizes. The new inventory lot-sizing model is anticipated to become a valuable decision-making tool in companies that are planning to adopt remanufacturing. The second decision-making tool was applied to address a production and inventory planning problem in a remanufacturing system considering different remanufacturing policies for a given remanufacturing strategy. For a remanufacture-to-stock system with two quality remanufacturables groups four alternative policies were examined, a policy which specifies simultaneous processing utilising dedicated resources was shown to be the best policy to achieve a shorter remanufacturing cycle time. For a remanufacture-to-order system with two quality remanufacturables groups, the three relevant policies of the four alternative policies were examined, a policy which specifies sequential processing and switching between various quality remanufacturables groups was shown to be the best policy to achieve a shorter remanufacturing cycle time. The production and inventory planning simulation models in a remanufacturing system are expected to become significant decision-making tools in remanufacturing operations.     

Price,Rebate, and Returns Supply Contracts for Coordinating Supply Chains with Price‐Dependent Demands

Channel rebates and returns policies are common mechanisms for manufacturers to entice retailers to increase their order quantities and sales ultimately. However, when the underlying demand depends on the retail price, it has been known that channel coordination cannot be achieved if only one of these mechanisms is deployed. In this article, we show that a policy that combines the use of wholesale price, channel rebate, and returns can coordinate a channel with both additive and multiplicative price‐dependent demands. In addition to determining the sufficient conditions for the contract parameters associated with the equilibrium policy, we show that multiple equilibrium policies for channel coordination exist. We further explore how the equilibrium policy can be adjusted to achieve Pareto improvement. Other issues such as the maximum amount of expected profit that the manufacturer can share under the coordinated channel, the structural properties of the contracts under both the additive and multiplicative price‐dependent demand functions are also discussed.     

面向短生命周期产品的供应链订购批量与交货期协调决策模型

考虑了由一个分销商和制造商构成的单一短生命周期产品的两级供应链系统,当市场需求和制造商的交货期都是随机变化时,建立了分散决策和协调决策模式下交货期与批量的决策模型,解析分析了两种决策模式下供应链的优化决策;解析分析和算例分析表明,双方合作不仅可以缩短产品交货期、增加供应链系统的整体收益,而且还能增加产品的市场供给量。     

Optimal Policies for a Two‐Product Inventory System under a Flexible Substitution Scheme

We study a two‐product inventory model that allows substitution. Both products can be used to supply demand over a selling season of N periods, with a one‐time replenishment opportunity at the beginning of the season. A substitution could be offered even when the demanded product is available. The substitution rule is flexible in the sense that the seller can choose whether or not to offer substitution and at what price or discount level, and the customer may or may not accept the offer, with the acceptance probability being a decreasing function of the substitution price. The decisions are the replenishment quantities at the beginning of the season, and the dynamic substitution‐pricing policy in each period of the season. Using a stochastic dynamic programming approach, we present a complete solution to the problem. Furthermore, we show that the objective function is concave and submodular in the inventory levels—structural properties that facilitate the solution procedure and help identify threshold policies for the optimal substitution/pricing decisions. Finally, with a state transformation, we also show that the objective function is ‐concave, which allows us to derive similar structural properties of the optimal policy for multiple‐season problems.     

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.     

Looking Upstream: Optimal Policies for a Class of Capacitated Multi‐Stage Inventory Systems

We consider a multi‐stage inventory system with stochastic demand and processing capacity constraints at each stage, for both finite‐horizon and infinite‐horizon, discounted‐cost settings. For a class of such systems characterized by having the smallest capacity at the most downstream stage and system utilization above a certain threshold, we identify the structure of the optimal policy, which represents a novel variation of the order‐up‐to policy. We find the explicit functional form of the optimal order‐up‐to levels, and show that they depend (only) on upstream echelon inventories. We establish that, above the threshold utilization, this optimal policy achieves the decomposition of the multidimensional objective cost function for the system into a sum of single‐dimensional convex functions. This decomposition eliminates the curse of dimensionality and allows us to numerically solve the problem. We provide a fast algorithm to determine a (tight) upper bound on this threshold utilization for capacity‐constrained inventory problems with an arbitrary number of stages. We make use of this algorithm to quantify upper bounds on the threshold utilization for three‐, four‐, and five‐stage capacitated systems over a range of model parameters, and discuss insights that emerge.     

Mitigating Inventory Overstocking: Optimal Order‐up‐to Level to Achieve a Target Fill Rate over a Finite Horizon

Service level agreements (SLAs) are widely adopted performance‐based contracts in operations management practice, and fill rate is the most common performance metric among all the measurements in SLAs. Traditional procedures characterizing the order‐up‐to level satisfying a specified fill rate implicitly assume an infinite performance review horizon. However, in practice, inventory managers are liable to maintain and report fill rates over a finite performance review horizon. This horizon discrepancy leads to deviation between the target fill rate and actual achieved fill rate. In this study, we first examine the behavior of the fill rate distribution over a finite horizon with positive lead time. We analytically prove that the expected fill rate assuming an infinite performance review horizon exceeds the expected fill rate assuming a finite performance review horizon, implying that there exists some inventory “waste” (i.e., overstocking) when the traditional procedure is used. Based on this observation and the complexity of the problem, we propose a simulation‐based algorithm to reduce excess inventory while maintaining the contractual target fill rate. When the lead time is significant relative to the length of the contract horizon, we show that the improvement in the inventory system can be over 5%. Further, we extend our basic setting to incorporate the penalty for failing to meet a target, and show how one can solve large‐scale problems via stochastic approximation. The primary managerial implication of our study is that ignoring the performance review horizon in an SLA will cause overstocking, especially when the lead time is large.     

考虑不同回收质量等级的再制造系统回收生产决策

废旧产品的回收再制造过程往往在回收质量、再制造成本、再制造产出率、再制造产品需求等方面存在不确定性因素,极大地增加了再制造生产管理决策的复杂性。本文在一个回收再制造系统中研究了存在多种回收质量等级时的两阶段回收—再制造联合优化决策问题,并扩展到需求与价格相关和再制造产出率随机两种情形。在最大化再制造商期望利润的决策目标下,基于每种回收质量等级的单位回收和再制造成本构造出再制造系统的有效生产前沿面,给出了不同决策情形下再制造商的最优回收数量、销售定价的解析解,并且分析了一些主要的参数对再制造商最优决策的影响。本文的研究结果表明：（1）含有多种回收质量等级的再制造系统中存在一个下凸的有效生产前沿面,不在该前沿面上的任何质量等级的回收产品都将不会被用于再制造;（2）在同等的政府补贴额度下,回收补贴方式对再制造商决策的影响程度大于再制造补贴方式;（3）当再制造品的市场需求与价格相关时,最优销售价格至少大于第一种被使用的回收质量等级的边际回收和再制造成本;（4）任意两种回收质量等级之间存在着替代或互补效应,由其成本差异决定,并且这种效应随着需求不确定性的增大而增大;（5）再制造产出率的不确定性和再制造品需求的不确定性之间存在"对冲"效应,这种效应随着再制造产出率不确定性的降低而减弱。本文的研究可为不确定性环境下再制造企业的回收、生产管理决策提供有益的管理启示。     

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.     

制造商竞争环境下基于产品生命周期的闭环供应链的定价和生产策略研究

研究制造商竞争环境下基于产品生命周期的闭环供应链的定价和生产策略:产品第一个生命周期中,只有一个制造商利用原材料生产新制造产品,从第二个周期开始制造商生产新品的同时进行回收再制造、并且出现替代品生产商的竞争。建立两周期双寡头垄断的闭环供应链定价和生产优化模型,得到最优策略,然后将其扩展到多周期和无限周期的情况。结果表明:两周期中制造商应根据再制造成本节约的大小而采取不同的定价和生产策略;多周期中,除第一和最后一个周期外,制造商应采取相同的策略;无限周期中,制造商应在出现竞争后一直采用相同策略。三种情况下,制造商都应在第一周期中低价销售产品来保证第二周期中能回收更多的产品用于再制造以取得低成本的竞争优势;而且随着再制造产品成本节约的增大,制造商旳利润和销售量增大,并且竞争者的利润和销售量减小。算例验证了上述结论。