The Economic Payout Model for Service Guarantees

Service guarantees consist of a promise to a customer (marketing), the delivery of a service to the customer (operations), and actions to appease the customer when service failures happen (recovery). A part of recovery involves offering the customer an economic and/or noneconomic payout when things go wrong. When the economic payout is too high or low, the impact on the organization and the customer is usually negative. Therefore, determining the size of the economic payout is of critical strategic and tactical importance in businesses. Yet, no systematic quantitative methods are found in the literature to help managers determine the economic payout for service failures. The current ways an economic payout is determined are management judgment, the consensus of customer focus groups, competitive benchmarking, and the use of simple expected value methods. In this article, we define the Economic Payout Model for Service Guarantees (EPMSG) that provides an optimal service guarantee economic payout under certain conditions. The EPMSG and its objective function considers customer revenue over the short‐ and long‐term, the cost of creating and providing the service, the cost of recovery, the probability of a service failure, and the probability of customer retention as a function of economic payout. A numerical example is provided of how EPMSG works. Customer retention probability distributions are examined assuming normal and gamma distributions. We end the article by describing the theoretical contributions, model limitations, managerial implications, and opportunities for future research.     

Managing the Reverse Channel with RFID‐Enabled Negative Demand Information

We analyze the inventory decisions of a manufacturer who has ample production capacity and also uses returned products to satisfy customer demand. All returned items go through an evaluation process, at the end of which the decision of disposal, direct reselling, or rework is made for each unit according to a predetermined procedure. We quantify the value of information/visibility on the reverse channel for the manufacturer by making comparisons among three approaches: No information‐naive; no visibility‐enlightened; and full visibility. We find the value of visibility increases with the comparative length of the reverse channel and volume, volatility, and usability of returns. Furthermore, the smarter the manufacturer, the less benefit visibility brings to the system. By this analysis, we quantify the visibility savings of radio frequency identification (RFID) in the reverse channel as a candidate enabler technology. We also provide numerical examples to show that practical approximations in inventory management may have acceptable penalties to the manufacturer with visibility.     

Emerging Market Penetration,Inventory Supply,and Financial Performance

Realizing potential benefits from emerging market penetration requires firms to address inherent supply chain challenges. A major challenge is for firms to manage costly inventories to address demand and supply risks in emerging markets. However, emerging market penetration may offer opportunities for firms to lower inventory levels, reduce costs, and improve operating performance. Using data for 482 manufacturing firms over the 5‐year period, 2003–2007, obtained from the COMPUSTAT Industrial and Segment Databases, this article examines the relationships between emerging market penetration, inventory supply, and financial performance. Our results show that a multinational firm's sales penetration into emerging markets is associated with fewer days of inventory supply and improved financial performance. As emerging market penetration may allow firms to operate with lower inventory supply, the positive effect from emerging market penetration, such as labor cost reductions, may be enhanced due to inventory cost savings.     

On‐Call Overtime for Service Workforce Scheduling when Demand Is Uncertain

This article builds on prior research to develop shift scheduling models that include on‐call overtime for service environments where demand is uncertain. The research is motivated by recent developments in nurse scheduling, such as laws prohibiting mandatory overtime and the popularity of self‐scheduling systems. For single‐period scenarios, models are developed, solution methods are described, and results are explored for a variety of environments. Results show that the use of on‐call overtime can reduce costs slightly, with the amount of savings dependent on characteristics of the scheduling environment. The factor that most significantly affects cost savings is the cost of outside agency workers relative to overtime workers. In addition to lowering costs, on‐call overtime greatly reduces reliance on outside agency workers, which can have important practical implications in terms of quality of service and workforce morale. Results based on single‐period models motivate multiperiod formulations for single‐ and multidepartment scenarios, and solution methods are outlined for those cases. The possibility of using multiperiod models within a rolling horizon framework with forecast updating is discussed. This goes along with an extension of the traditional workforce management hierarchy that separates overtime and regular‐time scheduling, as seen in practice with self‐scheduling and shift‐bidding systems.     

A Design Theory Approach to Building Strategic Network‐Based Customer Service Systems*

Customer service is a key component of a firm's value proposition and a fundamental driver of differentiation and competitive advantage in nearly every industry. Moreover, the relentless coevolution of service opportunities with novel and more powerful information technologies has made this area exciting for academic researchers who can contribute to shaping the design and management of future customer service systems. We engage in interdisciplinary research—across information systems, marketing, and computer science—in order to contribute to the service design and service management literature. Grounded in the design‐science perspective, our study leverages marketing theory on the service‐dominant logic and recent findings pertaining to the evolution of customer service systems. Our theorizing culminates with the articulation of four design principles. These design principles underlie the emerging class of customer service systems that, we believe, will enable firms to better compete in an environment characterized by an increase in customer centricity and in customers' ability to self‐serve and dynamically assemble the components of solutions that fit their needs. In this environment, customers retain control over their transactional data, as well as the timing and mode of their interactions with firms, as they increasingly gravitate toward integrated complete customer solutions rather than single products or services. Guided by these design principles, we iterated through, and evaluated, two instantiations of the class of systems we propose, before outlining implications and directions for further cross‐disciplinary scholarly research.     

Inventory management with variable lead-time dependent procurement cost

A reduction in the inventory replenishment lead-time allows reducing safety stock requirements and improving customer service. However, it might be accompanied by increased procurement costs because of premium charges imposed by suppliers, or higher transportation costs. This paper studies a single-stage variable lead-time inventory system with lead-time dependent procurement cost. Selection of the lead-time value represents finding the trade-off between benefits of lead-time reduction and increase in the procurement cost. A model for joint optimization of inventory and procurement costs is developed. Numerical studies are conducted to identify conditions under which lead-time reduction is favorable compared to procuring at the lowest cost.     

MODELS FOR OPTIMAL LEAD TIME REDUCTION

Most inventory and production planning models in the academic literature treat lead times either as constants or random variables with known distributions outside of management control. However, a number of recent articles in the popular press have argued that reducing lead times is a dominant issue in manufacturing strategy. The benefits of reducing customer lead times that are frequently cited include increased customer demand, improved quality, reduced unit cost, lower carrying cost, shorter forecast horizon, less safety stock inventory, and better market position. Although the costs of reducing lead times in the long term may be relatively insignificant compared with the benefits, in the short term these costs can have a significant impact on the profitability of a firm. This article develops a conceptual framework within which the costs and benefits of lead time reduction can be compared. Mathematical models for optimal lead time reduction are developed within this framework. The solutions to these models provide methods for calculating optimal lead times, which can be applied in practice. Sensitivity analysis of the optimal solutions provides insight into the structure of these solutions.     

A NUMERICAL ANALYSIS OF CAPACITATED POSTPONEMENT

Customer satisfaction can be achieved by providing rapid delivery of a wide variety of products. High levels of product variety require correspondingly high levels of inventory of each item to quickly respond to customer demand. Delayed product differentiation has been identified as a strategy to reduce final product inventories while providing the required customer service levels. However, it is done so at the cost of devoting large production capacities to the differentiation stage. We study the impact of this postponement capacity on the ability to achieve the benefits of delayed product differentiation. We examine a single‐period capacitated inventory model and consider a manufacturing system that produces a single item that is finished into multiple products. After assembly, some amount of the common generic item is completed as non‐postponed products, whereas some of the common item is kept as in‐process inventory, thereby postponing the commitment to a specific product. The non‐postponed finished‐goods inventory is used first to meet demand. Demand in excess of this inventory is met, if possible, through the completion of the common items. Our results indicate that a relatively small amount of postponement capacity is needed to achieve all of the benefits of completely delaying product differentiation for all customer demand. This important result will permit many firms to adopt this delaying strategy who previously thought it to be either technologically impossible or prohibitively expensive to do so.     

Managing Internet Product Returns: A Focus on Effective Service Operations

Product returns present one of the biggest operational challenges in the world of Internet retailing due to the sheer volume and cost of processing returns. But returns also represent an often‐missed opportunity to manage customer relationships and build customer loyalty to the retailer. Based upon data from a survey of 464 customers of five different Internet retailers, this article explores how firms' returns management systems affect loyalty intentions. We draw upon extant literature in the fields of Internet retailing, service quality, supply chain management, and customer satisfaction/loyalty to develop a model and a set of hypotheses relating ten latent variables in the service returns offering area. Our resulting structural equation model provides evidence of the impact of the returns management system upon customer loyalty intentions. The model also identifies effects on loyalty intentions arising from customers' satisfaction with, and perceptions of, the value of the returns service offered. These findings will help inform managers' choices regarding investment in the returns management system as an element of service quality improvement and a potential means of improved profitability. In addition, this study's empirical exploration and testing of a returns management model in the Internet retailing environment is a contribution to the currently underrepresented body of academic literature linking marketing and supply chain management in the context of end consumers.     

Customer‐Base Concentration and Inventory Efficiencies: Evidence from the Manufacturing Sector

What is the link between customer‐base concentration and inventory efficiencies in the manufacturing sector? Using hand‐collected data from 10‐K Filings, we find that manufacturers with more concentrated customer bases hold fewer inventories for less time and are less likely to end up with excess inventories, as indicated by the lower likelihood and magnitude of inventory write‐downs and reversals. Using disaggregated inventory disclosures, we find that inventory efficiencies primarily flow through the finished goods inventory account, while raw material efficiencies are offset by higher work‐in‐process holdings and longer work‐in‐process cycles. In additional analysis, we document a valuation premium for more concentrated manufacturers after controlling for other firm characteristics, including default risk and cost of capital estimates. We conclude that investors trade off the costs and benefits of relationships with a limited number of major customers and, on balance, consider customer‐base concentration as a net positive for firm valuation. Overall, our study adds to interdisciplinary research in accounting and operations management by shedding new light on the relevance of major customer disclosures for fundamental analysis and valuation in the manufacturing sector.     

Inventory Management for Customers with Alternative Lead Times

It is common for suppliers operating in batch‐production mode to deal with patient and impatient customers. This paper considers inventory models in which a supplier provides alternative lead times to its customers: a short or a long lead time. Orders from patient customers can be taken by the supplier and included in the next production cycle, while orders from impatient customers have to be satisfied from the on‐hand inventory. We denote the action to commit one unit of on‐hand inventory to patient or impatient customers as the inventory‐commitment decision, and the initial inventory stocking as the inventory‐replenishment decision. We first characterize the optimal inventory‐commitment policy as a threshold type, and then prove that the optimal inventory‐replenishment policy is a base‐stock type. Then, we extend our analysis to models to consider cases of a multi‐cycle setting, a supply‐capacity constraint, and the on‐line charged inventory‐holding cost. We also evaluate and compare the performances of the optimal inventory‐commitment policy and the inventory‐rationing policy. Finally, to further investigate the benefits and pitfalls of introducing an alternative lead‐time choice, we use the customer‐choice model to study the demand gains and losses, known as demand‐induction and demand‐cannibalization effects, respectively.     

Adaptive versus Proactive Behavior in Service Recovery: The Role of Self‐Managing Teams

In this article, we develop a conceptual model of adaptive versus proactive recovery behavior by self‐managing teams (SMTs) in service recovery operations. To empirically test the conceptual model a combination of bank employee, customer, and archival data is collected. The results demonstrate support for independent group‐level effects of intrateam support on adaptive and proactive recovery behavior, indicating that perceptual consensus within service teams has incremental value in explaining service recovery performance. In addition, we provide evidence that adaptive and proactive recovery behavior have differential effects on external performance measures. More specifically, higher levels of adaptive performance positively influence customer‐based parameters (i.e., service recovery satisfaction and loyalty intentions), while employee proactive recovery behavior contributes to higher share of customer rates.     

Service Personnel,Technology, and Their Interaction in Influencing Customer Satisfaction*

Managing both the technologies and the personnel needed for providing high‐quality, multichannel customer support creates a complex and persistent operational challenge. Adding to this difficulty, it is still unclear how service personnel and these new communication technologies interact to influence the customer's perceptions of the service being provided. Motivated by both practical importance and inconsistent findings in the academic literature, this exploratory research examines the interaction of media richness, represented by three different technology contexts (telephone, e‐mail, and online chat), with six customer service representative (CSR) characteristics and their influences on customer satisfaction. Using a large‐sample customer survey data set, the article develops a multigroup structural equation model to analyze these interactions. Results suggest that CSR characteristics influence customer service satisfaction similarly across all three technology‐mediated contexts. Of the characteristics studied, service representatives contribute to customer satisfaction more when they exhibit the characteristics of thoroughness, knowledgeableness, and preparedness, regardless of the richness of the medium used. Surprisingly, while three other CSR characteristics studied (courtesy, professionalism, and attentiveness) are traditionally believed to be important in face‐to‐face encounters, they had no significant impact on customer satisfaction in the technology‐mediated contexts studied. Implications for both practitioners and researchers are drawn from the results and future research opportunities are discussed.     

Multiobjective Prioritization Methodology and Decision Support System for Evaluating Inventory Enhancement Strategies for Disrupted Interdependent Sectors

Disruptions in the production of commodities and services resulting from disasters influence the vital functions of infrastructure and economic sectors within a region. The interdependencies inherent among these sectors trigger the faster propagation of disaster consequences that are often associated with a wider range of inoperability and amplified losses. This article evaluates the impact of inventory‐enhanced policies for disrupted interdependent sectors to improve the disaster preparedness capability of dynamic inoperability input‐output models (DIIM). In this article, we develop the dynamic cross‐prioritization plot (DCPP)—a prioritization methodology capable of identifying and dynamically updating the critical sectors based on preference assignments to different objectives. The DCPP integrates the risk assessment metrics (e.g., economic loss and inoperability), which are independently analyzed in the DIIM. We develop a computer‐based DCPP tool to determine the priority for inventory enhancement with user preference and resource availability as new dimensions. A baseline inventory case for the state of Virginia revealed a high concentration of (i) manufacturing sectors under the inoperability objective and (ii) service sectors under the economic loss objective. Simulation of enhanced inventory policies for selected critical manufacturing sectors has reduced the recovery period by approximately four days and the expected total economic loss by $33 million. Although the article focuses on enhancing inventory levels in manufacturing sectors, complementary analysis is recommended to manage the resilience of the service sectors. The flexibility of the proposed DCPP as a decision support tool can also be extended to accommodate analysis in other regions and disaster scenarios.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

A Mathematical Model of Service Failure and Recovery Strategies*

Understanding the nature of service failures and their impact on customer responses and designing cost‐effective recovery strategies have been recognized as important issues by both service researchers and practitioners. We first propose a conceptual framework of service failure and recovery strategies. We then transform it into a mathematical model to assist managers in deciding on appropriate resource allocations for outcome and process recovery strategies based on customer risk profiles and the firm's cost structures. Based on this mathematical model we derive optimal recovery strategies, conduct sensitivity analyses of the optimal solutions for different model parameters, and illustrate them through numerical examples. We conclude with a discussion of managerial implications and directions for future research.     

Inventory Record Inaccuracy,Double Marginalization,and RFID Adoption

Most retailers suffer from substantial discrepancies between inventory quantities recorded in the system and stocks truly available to customers. Promising full inventory transparency, radio frequency identification (RFID) technology has often been suggested as a remedy to the problem. We consider inventory record inaccuracy in a supply chain model, where a Stackelberg manufacturer sets the wholesale price and a retailer determines how much to stock for sale to customers. We first analyze the impact of inventory record inaccuracy on optimal stocking decisions and profits. By contrasting optimal decisions in a decentralized supply chain with those in an integrated supply chain, we find that inventory record inaccuracy exacerbates the inefficiencies resulting from double marginalization in decentralized supply chains. Assuming RFID technology can eliminate the problem of inventory record inaccuracy, we determine the cost thresholds at which RFID adoption becomes profitable. We show that a decentralized supply chain benefits more from RFID technology, such that RFID adoption improves supply chain coordination.     

Misplaced Inventory and Radio‐Frequency Identification (RFID) Technology: Information and Coordination

Misplaced inventory is a major operational problem in many supply chains. Radio‐frequency identification (RFID) technology has been publicized as a promising solution for the misplaced inventory. Adoption of this technology has a fixed cost and variable cost of implementation, which can cause incentive issues in the supply chain. In this paper, we consider a supply chain under misplacement of inventory subject to uncertain demand. We study both centralized and decentralized cases and identify the conditions to coordinate the supply chain under implementation of RFID. We show that the incentives of the parties for investing in the technology are not perfectly aligned in the existence of the fixed cost of investment. Based on the relative payments of the parties for the fixed cost of investment, the incentives to adopt RFID can be characterized into regions, where we observe only one party or two parties benefiting from the technology when the tag price falls in a region specified in the paper. We further establish the effects of changes in mean and variance of a uniform demand on the incentives for investing in RFID and find that the incentives of the firms may indeed decrease as demand becomes more variable.     

零售商主导下生鲜农产品供应链的定价策略对比研究

运用RFID技术来监控与管理产品,零售商可以依据记录、传递的产品实时价值信息制定更为灵活的定价和订货策略,提升自身以及供应链的利润。针对生鲜农产品在价值损耗及消费者需求等方面进行特征分析,分别在固定定价、动态定价以及带有降价时点考虑的定价三种不同模式下,构建了单周期零售商的决策模型,得到各自的最优定价与订货策略,通过分析最优策略与关键参数之间的关系,发现:价值衰减系数越大,库存成本参数越大,三种模式下的总体零售价格都越小,价格的下调幅度越大;而两个参数对降价折扣时间点的影响却截然相反。进一步,着力在最优销售价格、订货总量与利润值三个方面对三种定价策略进行对比分析,结果显示基于RFID技术的灵活定价模式有助于零售商获取更大的市场需求量及更大的收益。数值实验进一步验证了理论研究的结果,为生鲜农产品供应链中对RFID技术的应用以及相应定价模式的选择提供了参考依据。     

An Analysis of Dual‐Kanban Just‐In‐Time Systems in a Non‐Repetitive Environment

Recent advances in approaches and production technologies for the production of goods and services have made just‐in‐time (JIT) a strong alternative for use in intermittent and small batch production systems, especially when time‐based competition is the norm and a low inventory is a must. However, the conventional JIT system is designed for mass production with a stable master production schedule. This paper suggests supplementing the information provided by production kanbans with information about customer waiting lines to be used by operators to schedule production in each work‐station of intermittent and small batch production systems. This paper uses simulation to analyze the effect of four scheduling policy variables—number of kanbans, length of the withdrawal cycle, information about customer waiting lines, and priority rules on two performance measures—customer wait‐time and inventory. The results show that using information about customer waiting lines reduces customer wait‐time by about 30% while also reducing inventory by about 2%. In addition, the effect of information about customer waiting lines overshadows the effect of priority rules on customer wait‐time and inventory.