Modellgestützte Flexibilitätsanalyse von Strategien zur Ersatzteilversorgung in der Nachserienphase

An important task in after sales service is the provision of spare parts for durables. Due to its nature and dependence on earlier sales, the demand for spare parts is inherently dynamic and uncertain requiring for high procurement flexibility. During the product life cycle, inventory management of spare parts is performed efficiently under use of flexibility provided by existing production facilities. This situation completely changes once the OEM ceases production of the parent product. A prime option of procuring spare parts for the End-of-production period is to place a final order for parts when regular production ends. Besides low unit production costs, this option does not contain any flexibility, yielding a high risk of obsolescence of stored parts, and at the same time a high risk of not being able to satisfy all demand during the service period. In order to increase flexibility further options like extra production at higher unit cost or remanufacturing of components taken from used products could be used. After introducing the problem and a basic quantitative model, we evaluate flexibility properties of strategies using different combinations of the above options. In doing so we distinguish between quantity, time, and stock related flexibility. In a comprehensive numerical study it is investigated to which extent flexibility properties of the different strategies can contribute to their economic profitability.     

A Profit‐Maximizing Approach to Disposition Decisions for Product Returns*

Motivated by the asset recovery process at IBM, we analyze the optimal disposition decision for product returns in electronic products industries. Returns may be either remanufactured for reselling or dismantled for spare parts. Reselling a remanufactured unit typically yields higher unit margins. However, demand is uncertain. A common policy in many firms is to rank disposition alternatives by unit margins. We propose a profit‐maximization approach that considers demand uncertainty. We develop single period and multiperiod stochastic optimization models for the disposition problem. Analyzing these models, we show that the optimal allocation balances expected marginal profits across the disposition alternatives. A detailed numerical study reveals that our approach to the disposition problem outperforms the current practice of focusing exclusively on high‐margin options, and we identify conditions under which this improvement is the highest. In addition, we show that a simple myopic heuristic in the multiperiod problem performs well.     

Assessing End‐Of‐Supply Risk of Spare Parts Using the Proportional Hazard Model

Operators of long field‐life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end‐of‐supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end‐of‐supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end‐of‐supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross‐validation results and out‐of‐sample risk assessments show good performance of the method to identify spare parts with high end‐of‐supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high‐risk spare parts.     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

End‐of‐Life Inventory Problem with Phaseout Returns

We consider the service parts end‐of‐life inventory problem of a capital goods manufacturer in the final phase of its life cycle. The final phase starts as soon as the production of parts terminates and continues until the last service contract expires. Final order quantities are considered a popular tactic to sustain service fulfillment obligations and to mitigate the effect of obsolescence. In addition to the final order quantity, other sources to obtain serviceable parts are repairing returned defective items and retrieving parts from phaseout returns. Phaseout returns happen when a customer replaces an old system platform with a next‐generation one and returns the old product to the original equipment manufacturer (OEM). These returns can well serve the demand for service parts of other customers still using the old generation of the product. In this study, we study the decision‐making complications as well as cost‐saving opportunities stemming from phaseout occurrence. We use a finite‐horizon Markov decision process to characterize the structure of the optimal inventory control policy. We show that the optimal policy consists of a time‐varying threshold level for item repair. Furthermore, we study the value of phaseout information by extending the results to cases with an uncertain phaseout quantity or an uncertain schedule. Numerical analysis sheds light on the advantages of the optimal policy compared to some heuristic policies.     

End‐of‐Life Inventory Decisions for Consumer Electronics Service Parts

We consider a consumer electronics manufacturer's problem of controlling the inventory of spare parts in the final phase of the service life cycle. The final phase starts when the part production is terminated and continues until the last service contract or warranty period expires. Placing final orders for service parts is considered to be a popular tactic to satisfy demand during this period and to mitigate the effect of part obsolescence at the end of the service life cycle. Previous research focuses on repairing defective products by replacing the defective parts with properly functioning spare ones. However, for consumer electronic products there typically is considerable price erosion while repair costs stay steady over time. As a consequence, there might be a point in time at which the unit price of the product drops below the repair costs. If so, it is more cost effective to adopt an alternative policy to meet service demands toward the end of the final phase, such as offering customers a new product of the similar type or a discount on a next generation product. This study examines the cost trade‐offs of implementing alternative policies for the repair policy and develops an exact expression for the expected total cost function. Using this expression, the optimal final order quantity and switching time from repair to an alternative policy can be determined simultaneously. Numerical analysis of a real world case sheds light on the cost benefits of these policies and also yields insights into the quantitative importance of the various cost parameters.     

A Bayesian Inventory Model Using Real‐Time Condition Monitoring Information

Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real‐time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base‐stock policy, in which the optimal base‐stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.     

Information‐Sensitive Replenishment when Inventory Records Are Inaccurate

Inspired by recent empirical work on inventory record inaccuracy, we consider a periodic review inventory system with imperfect inventory records and unobserved lost sales. Record inaccuracies are assumed to arrive via an error process that perturbs physical inventory but is unobserved by the inventory manager. The inventory manager maintains a probability distribution around the physical inventory level that he updates based on sales observations using Bayes Theorem. The focus of this study is on understanding, approximating, and evaluating optimal forward‐looking replenishment in this environment. By analyzing one‐ and two‐period versions of the problem, we demonstrate several mechanisms by which the error process and associated record inaccuracy can impact optimal replenishment. Record inaccuracy generally brings an incentive for a myopic manager to increase stock to buffer the added uncertainty. On the other hand, a forward‐looking manager will stock less than a myopic manager, in part to improve information content for future decisions. Using an approximate partially observed dynamic programming policy and associated bound, we numerically corroborate our analytical findings and measure the effectiveness of an intelligent myopic heuristic. We find that the myopic heuristic is likely sufficiently good in practical settings targeting high service levels.     

Economic and Environmental Assessment of Remanufacturing Strategies for Product + Service Firms

This article provides a data‐driven assessment of economic and environmental aspects of remanufacturing for product + service firms. A critical component of such an assessment is the issue of demand cannibalization. We therefore present an analytical model and a behavioral study which together incorporate demand cannibalization from multiple customer segments across the firm's product line. We then perform a series of numerical simulations with realistic problem parameters obtained from both the literature and discussions with industry executives. Our findings show that remanufacturing frequently aligns firms' economic and environmental goals by increasing profits and decreasing the total environmental impact. We show that in some cases, an introduction of a remanufactured product leads to no changes in the new products' prices (positioning within the product line), implying a positive demand cannibalization and a decrease in the environmental impact; this provides support for a heuristic approach commonly used in practice. Yet in other cases, the firm can increase profits by decreasing the new product's prices and increasing sales—a negative effective cannibalization. With negative cannibalization the firm's total environmental impact often increases due to the growth in new production. However, we illustrate that this growth is nearly always sustainable, as the relative environmental impacts per unit and per dollar rarely increase.     

Pay‐Back‐Revenue‐Sharing Contract in Coordinating Supply Chains with Random Yield

We consider coordination issues in supply chains where supplier's production process is subject to random yield losses. For a simple supply chain with a single supplier and retailer facing deterministic demand, a pay back contract which has the retailer paying a discount price for the supplier's excess units can provide the right incentive for the supplier to increase his production size and achieve coordination. Building upon this result, we consider coordination issues for two other supply chains: one with competing retailers, the other with stochastic demand. When retailers compete for both demand and supply, they tend to over‐order. We show that a combination of a pay back and revenue sharing mechanism can coordinate the supply chain, with the pay back mechanism correcting the supplier's under‐producing problem and the revenue sharing mechanism correcting the retailers' over‐ordering problem. When demand is stochastic, we consider a modified pay‐back‐revenue‐sharing contract under which the retailer agrees to not only purchase the supplier's excess output (beyond the retailer's order), but also share with the supplier a portion of the revenue made from the sales of the excess output. We show that this contract, by giving the supplier additional incentives in the form of revenue share, can achieve coordination.     

Managing a Remanufacturing System with Random Yield: Properties,Observations, and Heuristics

We study a remanufacturing system that involves the ordering of a serviceable product and the remanufacturing of multiple types of returned products (cores) into the serviceable product. In addition to random demand for the serviceable product and random returned quantities of different types of cores in each time period, the remanufacturing yield of each type of core is also uncertain. By analyzing a multi‐period stochastic dynamic program, we derive several properties of the optimal ordering/remanufacturing policy. In addition to some insights, these properties can be used to reduce the search effort of the optimal policy. We also demonstrate that some existing results derived from related models no longer hold in remanufacturing systems with random yield. Recognizing the optimal ordering/remanufacturing policy is highly complex, we examine three simple heuristics that can be efficiently solved and implemented in practice. Among these three heuristics, our numerical analysis suggests that the heuristic that captures most of the yield uncertainty and future system evolvement as well as some of the properties of the optimal ordering/remanufacturing policy outperforms the other two heuristics.     

Sequencing and Scheduling Appointments with Potential Call‐In Patients

This paper studies appointment scheduling for a combination of routine patients who book well in advance and last‐minute patients who call for an appointment later that same day. We determine when these same‐day patients should be scheduled throughout the day, and how the prospect of their arrivals affects the appointment times of the routine patients. By formulating the problem as a stochastic linear program, we are able to incorporate random and heterogeneous service times and no‐show rates, ancillary physician tasks, and appointment delay costs for same‐day patients who prefer to see the doctor as early as possible. We find that the optimal patient sequence is quite sensitive to the no‐show probabilities and the expected number of same‐day patients. We also develop two simple heuristic solutions to this combinatorial sequencing problem.     

面临零部件过期的产品销售与售后服务供应链合同分析

备件库存管理对产品售后服务至关重要,而零部件过期会影响备件库存的正常补货,进而延长故障修复时间,降低产品可用率.终身购买作为应对零部件过期的重要策略,被广泛运用在实践中.然而,学术论文中关于零件过期对供应链管理影响的研究并不多见.本文建立了同时考虑产品销售和售后服务情况下的供应链动态博弈模型,分析了客户的产品订购批量、供应商的定价决策以及备件终身购买批量之间的相互作用,探讨了保修期和零部件过期采购成本对合同均衡策略和供应链利润的影响.结果表明,当零部件过期采购成本较低时,供应商可以在延长保修期的同时降低备件库存数量,并允许一定程度的备件缺货水平,从而最大化提升产品可用率所带来的收益.同时,应当适当降低对备件数量的技术标准要求.而当零部件过期 采购成本较高时,供应商应当避免提供较长的保修期,并增加备件终身购买批量以减小零部件过期带来的负面影响.此时,备件数量的技术标准则可以适当提高.     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

Hierarchical Multi‐skill Resource Assignment in the Telecommunications Industry

We formulate a discrete time Markov decision process for a resource assignment problem for multi‐skilled resources with a hierarchical skill structure to minimize the average penalty and waiting costs for jobs with different waiting costs and uncertain service times. In contrast to most queueing models, our application leads to service times that are known before the job is actually served but only after it is accepted and assigned to a server. We formulate the corresponding Markov decision process, which is intractable for problems of realistic size due to the curse of dimensionality. Using an affine approximation of the bias function, we develop a simple linear program that yields a lower bound for the minimum average costs. We suggest how the solution of the linear program can be used in a simple heuristic and illustrate its performance in numerical examples and a case study.     

基于再制造的多周期闭环营销投入及定价策略

针对再制造产品市场化艰难和缺少旧件来源的问题,结合"互联网+"战略提出了促进旧件回收与再制造零部件销售的闭环营销体系;闭环的营销投入可以同时发挥回收努力效应（增加单周期合格旧件回收量）和多周期广告效应（提升消费者对品牌的长期绿色认知度）,建立了多周期的闭环营销投入水平决策和产品差异定价模型并得到了最优决策;结合我国汽车零部件市场的现状,针对无闭环营销、初级闭环营销、闭环营销策略升级三种模式展开算例分析和对比。研究表明：闭环营销可以显著提高供应链总收益,营销策略的升级应考虑到回收努力效应和多周期广告效应的匹配,针对长生命周期的产品,闭环营销策略应经常调整。本研究结论有助于进一步完善再制造闭环供应链及营销理论基础。     

Cost Allocation in Manufacturing–Remanufacturing Operations

For firms remanufacturing their products, the total life‐cycle costs and revenues from new and remanufactured products determine their profitability. In many firms, manufacturing/sales and remanufacturing/remarketing operations are carried out in different divisions. Each division is responsible for only part of the product's life cycle. Practices regarding transfer pricing across divisions vary significantly among companies, affecting the life‐cycle profit performance of the product. In this research, we identify characteristics of transfer prices that achieve the firm‐wide optimal solution. To this end, we consider a manufacturer who also undertakes remanufacturing operations and we focus on price (quantity) decisions. We determine that a cost allocation mechanism that allocates a portion of the initial production cost to each of the two stages of the product life cycle should be used. We also conclude that cost allocation should be implemented as a fixed cost allocation, where charges to the remanufacturing division should be determined independently of the actual quantity of units remanufactured.     

The Value of Quality Grading in Remanufacturing

In this paper we consider a tactical production‐planning problem for remanufacturing when returns have different quality levels. Remanufacturing cost increases as the quality level decreases, and any unused returns may be salvaged at a value that increases with their quality level. Decision variables include the amount to remanufacture each period for each return quality level and the amount of inventory to carry over for future periods for both returns (unremanufactured), and finished remanufactured products. Our model is grounded with data collected at Pitney‐Bowes from their mailing systems remanufacturing operations. We derive some analytic properties for the optimal solution in the general case, and provide a simple greedy heuristic to computing the optimal solution in the case of deterministic returns and demand. Under mild assumptions, we find that the firm always remanufactures the exact demand in each period. We also study the value of a nominal quality‐grading system in planning production. Based on common industry parameters, we analyze, via a numerical study, the increase in profits observed by the firm if it maintains separate inventories for each quality grade. The results show that a grading system increases profit by an average of 4% over a wide range of parameter values commonly found in the remanufacturing industry; this number increases as the returns volume increases. We also numerically explore the case where there are capacity constraints and find the average improvement of a grading system remains around 4%.     

Using Frontier Portfolios to Improve Make‐to‐Order Operations

Make‐to‐order (MTO) manufacturers face a common problem of maintaining a desired service level for delivery at a reasonable cost while dealing with irregular customer orders. This research considers a MTO manufacturer who produces a product consisting of several custom parts to be ordered from multiple suppliers. We develop procedures to allocate orders to each supplier for each custom part and calculate the associated replenishment cost as well as the probability of meeting the delivery date, based on the suppliers' jobs on hand, availability, process speed, and defective rate. For a given delivery due date, a frontier of service level and a replenishment cost frontier are created to provide a range of options to meet customer requirements. This method can be further extended to the case when the delivery due date is not fixed and the manufacturer must “crash” its delivery time to compete for customers.