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.     

Evaluation of Embedded Intelligent Real‐time Systems*

Over the past two decades, questions have surfaced about the effectiveness and contribution of intelligent systems to decision makers in a variety of settings. This paper focuses on the evaluation challenges associated with intelligent real‐time software systems that are embedded in larger host systems. With the proliferation of such systems in operational settings such as aerospace, medical, manufacturing, and transportation systems, increased attention to evaluations of such systems, and to resulting software safety, is warranted. This paper describes one such evaluation and proposes a set of evaluation criteria for embedded intelligent real‐time systems (EIRTS). Implications of the evaluation and the evaluation criteria are discussed.     

The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research

Call centers are an increasingly important part of today's business world, employing millions of agents across the globe and serving as a primary customer‐facing channel for firms in many different industries. Call centers have been a fertile area for operations management researchers in several domains, including forecasting, capacity planning, queueing, and personnel scheduling. In addition, as telecommunications and information technology have advanced over the past several years, the operational challenges faced by call center managers have become more complicated. Issues associated with human resources management, sales, and marketing have also become increasingly relevant to call center operations and associated academic research. In this paper, we provide a survey of the recent literature on call center operations management. Along with traditional research areas, we pay special attention to new management challenges that have been caused by emerging technologies, to behavioral issues associated with both call center agents and customers, and to the interface between call center operations and sales and marketing. We identify a handful of broad themes for future investigation while also pointing out several very specific research opportunities.     

A Resource‐Based View of Strategic IT Alignment: How Knowledge Sharing Creates Competitive Advantage

A critical decision problem for top management, and the focus of this study, is whether the CEO (chief executive officer) and CIO (chief information officer) should commit their time to formal planning with the expectation of producing an information technology (IT)‐based competitive advantage. Using the perspective of the resource‐based view, a model is presented that examines how strategic IT alignment can produce enhanced organizational strategies that yield competitive advantage. One hundred sixty‐one CIOs provided data using a postal survey. Results supported seven of the eight hypotheses. They showed that information intensity is an important antecedent to strategic IT alignment, that strategic IT alignment is best explained by multiple constructs which operationalize both process and content measures, and that alignment between the IT plan and the business plan is significantly related to the use of IT for competitive advantage. Study results raise questions about the effect of CEO participation, which appears to be the weak link in the process, and also about the perception of the CIO on the importance of CEO involvement. The paper contributes to our understanding of how knowledge sharing in the alignment process contributes to the creation of superior organizational strategies, provides a framework of the alignment‐performance relationship, and furnishes several new constructs.     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

Knowledge Management Using Information Technology: Determinants of Short‐Term Impact on Firm Value*

The importance of knowledge management (KM) processes for organizational performance is now well recognized. Seeking to better understand the short‐term impact of KM on firm value, this article focuses on public announcements of information technology (IT)‐based KM efforts, and uses cumulative abnormal return (CAR) associated with an announcement as the dependent variable. This article employs a contingency approach, arguing that the KM announcement would have a positive short‐term impact on firm value in some conditions but not in others. Thus, it pursues the following research question: What are the effects of contextual factors on the CAR associated with the announcement of an IT‐based KM effort? Specific hypotheses are proposed based on information‐processing theory, organizational learning theory, the knowledge‐based theory of the firm, and the theory of knowledge creation. These hypotheses link CARs to alignment between industry innovativeness and the KM process, alignment between firm efficiency and the KM process, firm‐specific instability, and firm diversification. The empirical study utilizes secondary data on 89 KM announcements from 1995 to 2002. The results largely support the hypotheses. Overall, this article provides empirical support for the theory‐based arguments, and helps develop a contingency framework of the effectiveness of KM efforts.     

AbSRiM: An Agent‐Based Security Risk Management Approach for Airport Operations

Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent‐based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent‐based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent‐based modeling and Monte Carlo simulation at its core. Agent‐based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks. The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.     

Risk‐Based Sampling: I Don't Want to Weight in Vain

Recently, there has been considerable interest in developing risk‐based sampling for food safety and animal and plant health for efficient allocation of inspection and surveillance resources. The problem of risk‐based sampling allocation presents a challenge similar to financial portfolio analysis. Markowitz (1952) laid the foundation for modern portfolio theory based on mean‐variance optimization. However, a persistent challenge in implementing portfolio optimization is the problem of estimation error, leading to false “optimal” portfolios and unstable asset weights. In some cases, portfolio diversification based on simple heuristics (e.g., equal allocation) has better out‐of‐sample performance than complex portfolio optimization methods due to estimation uncertainty. Even for portfolios with a modest number of assets, the estimation window required for true optimization may imply an implausibly long stationary period. The implications for risk‐based sampling are illustrated by a simple simulation model of lot inspection for a small, heterogeneous group of producers.     

Experiential and Informational Knowledge,Architectural Marketing Capabilities,and the Adaptive Performance of Export Ventures: A Cross‐National Study

Knowledge‐based view (KBV) theory posits that the acquisition and use of relevant knowledge is key to understanding organizational performance. However, there is relatively little empirical evidence to support or refute several important propositions underlying KBV theory explanations of organizational performance. In particular, the extant literature has focused on individual technical and scientific components of the knowledge bases of firms in dynamic industries, and largely ignored both different levels of informational and experiential knowledge relevant to the market environment, and the increasingly important context of exporting. Our study addresses these knowledge gaps by developing a framework for export venture knowledge management and empirically examining relationships between different types of individual‐level and organizational‐level knowledge relevant to the market environment, architectural marketing capabilities, and the adaptive performance of export ventures. Using primary data collected in the United Kingdom and China, our study indicates that export ventures' organizational‐level experiential and informational knowledge, and individual‐level experiential knowledge relevant to the market environment, is positively associated with export ventures' architectural marketing capabilities, which are in turn associated with the adaptive performance of export ventures.     

Assemble‐to‐Order Inventory Management via Stochastic Programming: Chained BOMs and the M‐System

We study an inventory management mechanism that uses two stochastic programs (SPs), the customary one‐period assemble‐to‐order (ATO) model and its relaxation, to conceive control policies for dynamic ATO systems. We introduce a class of ATO systems, those that possess what we call a “chained BOM.” We prove that having a chained BOM is a sufficient condition for both SPs to be convex in the first‐stage decision variables. We show by examples the necessity of the condition. For ATO systems with a chained BOM, our result implies that the optimal integer solutions of the SPs can be found efficiently, and thus expedites the calculation of control parameters. The M system is a representative chained BOM system with two components and three products. We show that in this special case, the SPs can be solved as a one‐stage optimization problem. The allocation policy can also be reduced to simple, intuitive instructions, of which there are four distinct sets, one for each of four different parameter regions. We highlight the need for component reservation in one of these four regions. Our numerical studies demonstrate that achieving asymptotic optimality represents a significant advantage of the SP‐based approach over alternative approaches. Our numerical comparisons also show that outside of the asymptotic regime, the SP‐based approach has a commanding lead over the alternative policies. Our findings indicate that the SP‐based approach is a promising inventory management strategy that warrants further development for more general systems and practical implementations.     

Supply Chain Knowledge and Performance: A Meta‐Analysis

Many firms make significant investments into developing and managing knowledge within their supply chains. Such investments are often prudent because studies indicate that supply chain knowledge (SCK) has a positive influence on performance. Key questions still surround the SCK–performance relationship, however. First, what is the overall relationship between SCK and performance? Second, under what conditions is the relationship stronger or weaker? To address these questions, we applied meta‐analysis to 35 studies of the SCK–performance relationship that collectively include more than 8,400 firms. Our conservative estimate is that the effect size of the overall relationship is  = .39. We also find that the SCK–performance relationship is stronger when (i) examining operational performance, (ii) gathering data from more than one supply chain node, (iii) gathering data from multiple countries, (iv) examining service industries, and (v) among more recently published studies. We also found that studies that embraced a single theory base (as opposed to using multiple ones) had a stronger SCK–performance relationship. Looking to the future, our meta‐analysis highlights the need for studies to (i) include lags between the measurement of SCK and performance, (ii) gather upstream data when examining innovation, (iii) examine SCK within emerging countries, and (iv) provide much more information relative to the nuances of the SCK examined.     

The Dynamics of Pre‐ and Post‐purchase Service and Consumer Evaluation of Online Retailers: A Comparative Analysis of Dissonance and Disconfirmation Models*

This article explores the theoretical underpinnings of the dissonance framework in online consumer satisfaction formation process. Specifically, we suggest that any discrepancy between pre‐ and post‐purchase service performance would help determine consumers’ evaluations of online vendors. Drawing upon cognitive dissonance theory, a conceptual model is developed and tested in two different studies (preliminary and main studies). Using data from 191 college students collected longitudinally, the preliminary study demonstrates the validity and reliability of the measurements. Using a comparative analysis, the main study then tests our conceptual model as well as various competing models, including the expectation–confirmation model, with a sample of 292 online consumers. The results in both studies support our main prediction that the service encountered in different stages establishes dissonance. Specifically, we find that dissonance explains online consumers’ satisfaction process to a substantial extent, as compared with disconfirmation under the same conditions in online retailers. This study contributes to providing an alternative yet substantial approach for expectation–confirmation theory, reflecting the overarching nature of online shopping.     

E‐Business: A Review of Research Published in Production and Operations Management (1992–2008)

We review, classify, consolidate, and synthesize the contributions to the expanding field of e‐business that have been published in Production and Operations Management. We classify e‐business research in the following four categories: (1) e‐auctions, (2) radio frequency identification, (3) e‐business system design, and (4) competition, conflict, collaboration, and coordination (C⁴ in e‐business). We identify important research themes, research methodologies, and research techniques within each of these categories. We also provide directions for future research and discuss the managerial implications of the e‐business research reported in our paper.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.     

Lean Control for Make‐to‐Order Companies: Integrating Customer Enquiry Management and Order Release

A lead time that is short, predictable, and reliable is an increasingly important criterion in supplier selection. Although many companies may achieve this through lean implementation, high‐variety manufacturers, for example, small and medium‐sized make‐to‐order companies, have found that lean's planning and control techniques do not apply. This article outlines a planning and control concept known as workload control (WLC) that integrates customer enquiry management, including a due‐date setting rule, with order release control. Simulation is then used to assess its impact on shop performance. Results demonstrate that an integrated WLC concept can reduce the percentage of tardy jobs—so short lead times can be realistically quoted—while also reducing and stabilizing workloads. WLC can level demand and production over time when work is not standardized and it is not possible to synchronize flows on the shop floor. Results are shown to be robust to changes in routing characteristics, the mix of orders with due dates specified by the customer and proposed internally, and the strike rate (or order‐winning probability). Hence, an integrated approach to WLC represents an important step toward achieving lean in make‐to‐order companies.     

Measuring Modularity‐Based Manufacturing Practices and Their Impact on Mass Customization Capability: A Customer‐Driven Perspective

As uncertainty in markets and technology intensifies, more companies are adopting modular product and process architectures to cope with increasing demands for individually customized products. Modularity‐based manufacturing is the application of unit standardization or substitution principles to create modular components and processes that can be configured into a wide range of end products to meet specific customer needs. This study defines modularity‐based manufacturing practices (MBMP), develops a valid and reliable instrument to measure MBMP, builds a framework that relates customer closeness, MBMP, and mass customization capability, and tests structural relationships within this framework using LISREL. Based on 303 responses from members of the Society of Manufacturing Engineers, statistically significant and positive relationships were found among customer closeness, modularity‐based manufacturing practices, and mass customization capability. Managerial implications of the empirical findings of this study and future research directions are also discussed.     

DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation

Historical data analysis shows that escalation accidents, so‐called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent‐based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent‐based modeling technique explains the domino effects from a bottom‐up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher‐level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large‐scale complicated cases.     

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.     

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.