Shareholders' Wealth‐Maximizing Operating Decisions and Risk Management Practices in a Mixed Contracts Economy

This paper establishes a critically important positive role for operations management practices and financial hedging. We show that operations management decisions and financial hedging are intertwined, and we advance a framework that can identify their combined effects on investors' wealth. We show that: (a) firms (publicly traded corporations) will optimally hold adequate riskless working capital (e.g., cash) to minimize the cost of obtaining non‐financial inputs, and the magnitude of this cash holding depends on operating details, and (b) operations management and financial hedging can lower firms' cash requirements, and boost productivity, defined as the wealth created in the firm per dollar of invested capital. Productivity‐enhancing practices—by “freeing up” some of the firm's cash—can maximize the investors' wealth. We show that these results obtain because firms' contracts with many of the providers of non‐financial inputs are not traded, and because investors can invest not just in public corporations but also in businesses “outside the markets” (e.g., proprietorships, partnerships, and private equity).     

Economic Evaluation of Scale Dependent Technology Investments

We study the effect of financial risk on the economic evaluation of a project with capacity decisions. Capacity decisions have an important effect on the project̂s value through the up‐front investment, the associated operating cost, and constraints on output. However, increased scale also affects the financial risk of the project through its effect on the operating leverage of the investment. Although it has long been recognized in the finance literature that operating leverage affects project risk, this result has not been incorporated in the operations management literature when evaluating projects. We study the decision problem of a firm that must choose project scale. Future cash flow uncertainty is introduced by uncertain future market prices. The firm's capacity decision affects the firm's potential sales, its expected price for output, and its costs. We study the firm's profit maximizing scale decision using the CAPM model for risk adjustment. Our results include that project risk, as measured by the required rate of return, is related to the inverse of the expected profit per unit sold. We also show that project risk is related to the scale choice. In contrast, in traditional discounted cash flow analysis (DCF), a fixed prescribed rate is used to evaluate the project and choose its scale. When a fixed rate is used with DCF, a manager will ignore the effect of scale on risk and choose suboptimal capacity that reduces project value. S/he will also misestimate project value. Use of DCF for choosing scale is studied for two special cases. It is shown that if the manager is directed to use a prescribed discount rate that induces the optimal scale decision, then the manager will greatly undervalue the project. In contrast, if the discount rate is set to the risk of the optimally‐scaled project, the manager will undersize the project by a small amount, and slightly undervalue the project with the economic impact of the error being small. These results underline the importance of understanding the source of financial risk in projects where risk is endogenous to the project design.     

POM Forum: Transfer of Changing Production Know‐How

Even with the rich literature on knowledge management, we still don't know enough about how the rate of change in production‐know‐how affects the choice of mechanisms for its transfer. Codifying tacit know‐how helps, but codification becomes more challenging as the know‐how changes more frequently. Transfer of tacit know‐how becomes much more complicated when it changes often. We need more research in this area, particularly to help production and operations managers who must ultimately use the new know‐how and change their companies' production processes. The paper suggests a framework as a step in that direction. The framework focuses on the interplay between the level of codification and the rate of change of production know‐how, and identifies four zones for classifying production know‐how: “slow and codified,” “slow and tacit,” “fast and codified,” and “fast and tacit.” Examples from McDonald̂s, Club Med, Intel, and AOL are used to illustrate primary transfer mechanisms for each zone (manuals and systems, people, joint‐development, and projects, respectively). Appropriate absorptive capacities in the production units for each zone are also identified. Since the ultimate responsibility of operations managers is to improve (i.e., change) their production know‐how as fast as possible, they would be wise to adopt policies that are closer to those suited for the “fast and codified” zone. Intel and Toyota show good models.     

Updating Inventories of Substitutable Resources in Response to Forecast Updates

We show simple yet optimal results to update the inventory/capacity levels, expected profit, fill rates, and service levels of substitutable resources in response to an updating of the mean demand forecasts for the resources. We find that a change in the mean demand of one resource does not affect the optimal inventory level of any other resource. The results are obtained for demands with location‐scale distribution, and for a revenue structure satisfying a triangle property such that the manager will always use the inventory of a resource to meet her own demand first before using it for substitution. The results for updating the performance measures also extend to managers who maintain non‐optimal inventory/capacity levels. Implications for procurement, sales and operational planning, and multi‐store operations are discussed.     

Modeling Finite‐Time Failure Probabilities in Risk Analysis Applications

In this article, we introduce a framework for analyzing the risk of systems failure based on estimating the failure probability. The latter is defined as the probability that a certain risk process, characterizing the operations of a system, reaches a possibly time‐dependent critical risk level within a finite‐time interval. Under general assumptions, we define two dually connected models for the risk process and derive explicit expressions for the failure probability and also the joint probability of the time of the occurrence of failure and the excess of the risk process over the risk level. We illustrate how these probabilistic models and results can be successfully applied in several important areas of risk analysis, among which are systems reliability, inventory management, flood control via dam management, infectious disease spread, and financial insolvency. Numerical illustrations are also presented.     

Capacity Planning with Financial and Operational Hedging in Low‐Cost Countries

The authors of this article outline a capacity planning problem in which a risk‐averse firm reserves capacities with potential suppliers that are located in multiple low‐cost countries. While demand is uncertain, the firm also faces multi‐country foreign currency exposures. This study develops a mean‐variance model that maximizes the firm's optimal utility and derives optimal utility and optimal decisions in capacity and financial hedging size. The authors show that when demand and exchange rate risks are perfectly correlated, a risk‐averse firm, by using financial hedging, will achieve the same optimal utility as a risk‐neutral firm. In this study as well, a special case is examined regarding two suppliers in China and Vietnam. The results show that if a single supplier is contracted, financial hedging most benefits the highly risk‐averse firm when the demand and exchange rate are highly negatively related. When only one hedge is used, financial hedging dominates operational hedging only when the firm is very risk averse and the correlation between the two exchange rates have become positive. With both theoretical and numerical results, this study concludes that the two hedges are strategic tools and interact each other to maximize the optimal utility.     

The Impact of Variability and Patient Information on Health Care System Performance

In the delivery of health care services, variability in the patient arrival and service processes can cause excessive patient waiting times and poor utilization of facility resources. Based on data collected at a large primary care facility, this paper investigates how several sources of variability affect facility performance. These sources include ancillary tasks performed by the physician, patient punctuality, unscheduled visits to the facility's laboratory or X‐ray services, momentary interruptions of a patient's examination, and examination time variation by patient class. Our results indicate that unscheduled visits to the facility's laboratory or X‐ray services have the largest impact on a physician's idle time. The average patient wait is most affected by how the physician prioritizes completing ancillary tasks, such as telephone calls, relative to examining patients. We also investigate the improvement in system performance offered by using increasing levels of patient information when creating the appointment schedule. We find that the use of policies that sequence patients based on their classification improves system performance by up to 25.5%.     

A Decision Support Tool for Predicting Patients at Risk of Readmission: A Comparison of Classification Trees,Logistic Regression,Generalized Additive Models,and Multivariate Adaptive Regression Splines

The number of emergency (or unplanned) readmissions in the United Kingdom National Health Service (NHS) has been rising for many years. This trend, which is possibly related to poor patient care, places financial pressures on hospitals and on national healthcare budgets. As a result, clinicians and key decision makers (e.g., managers and commissioners) are interested in predicting patients at high risk of readmission. Logistic regression is the most popular method of predicting patient‐specific probabilities. However, these studies have produced conflicting results with poor prediction accuracies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting emergency readmissions within 45 days after been discharged from hospital. We also examined the predictive ability of two other types of data‐driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 963 patients readmitted to hospitals with chronic obstructive pulmonary disease and asthma. We used repeated split‐sample validation: the data were divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using a number of performance measures, such as area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1,000 times—the initial data set was divided into derivation and validation samples 1,000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1,000 derivation samples was .928, while the mean ROC curve area of a logistic regression model was .924. Our study shows that logistic regression model and regression trees had performance comparable to that of more flexible, data‐driven models such as GAMs and MARS. Given that the models have produced excellent predictive accuracies, this could be a valuable decision support tool for clinicians (healthcare managers, policy makers, etc.) for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.     

Design and Analysis of Hospital Admission Control for Operational Effectiveness

Variability in hospital occupancy negatively impacts the cost and quality of patient care delivery through increased emergency department (ED) congestion, emergency blockages and diversions, elective cancelations, backlogs in ancillary services, overstaffing, and understaffing. Controlling inpatient admissions can effectively reduce variability in hospital occupancy to mitigate these problems. Currently there are two major gateways for admission to a hospital: the ED and scheduled elective admission. Unfortunately, in highly utilized hospitals, excessive wait times make the scheduled gateway undesirable or infeasible for a subset of patients and doctors. As a result, this group often uses the ED gateway as a means to gain admission to the hospital. To better serve these patients and improve overall hospital functioning, we propose creating a third gateway: an expedited patient care queue. We first characterize an optimal admission threshold policy using controls on the scheduled and expedited gateways for a new Markov decision process model. We then present a practical policy based on insight from the analytical model that yields reduced emergency blockages, cancelations, and off‐unit census via simulation based on historical hospital data.     

Operational Risk in Financial Services: A Review and New Research Opportunities

We present a framework to describe and analyze operational risk in financial services from an operations management perspective, focusing in particular on process design, process management, and human behavior aspects. The financial services industry differs from other service industries in ways that affect the nature of the operational risks it is subject to. In recent decades, many books and papers have focused on operational risk in financial services; however, this literature has focused mainly on the conceptual and statistical aspects of operational risk management and not on its operational aspects. Operational risk in financial services has not received much attention from the operations management community. The framework presented here is based on the premise that operational risk in financial services can reap significant benefits from research done in the theory and practice of operations management in manufacturing industries as well as in other services industries. The objective of this study is to propose particular challenges and questions raised in the practice of operational risk management that may stimulate future research in this particular area of operations management.     

Foundations and Implications of a Proposed Unified Services Theory

Diverse businesses, such as garbage collection, retail banking, and management consulting are often tied together under the heading of “services”, based on little more than a perception that they are intangible and do not manufacture anything. Such definitions inadequately identify managerial and operational implications common among, and unique to, services. We present a “Unified Services Theory” (UST) to clearly delineate service processes from non‐service processes and to identify key commonalities across seemingly disparate service businesses. The UST defines a service production process as one that relies on customer inputs; customers act as suppliers for all service processes. Non‐services (such as make‐to‐stock manufacturing) rely on customer selection of outputs, payment for outputs, and occasional feedback, but production is not dependent upon inputs from individual customers. The UST reveals principles that are common to the wide range of services and provides a unifying foundation for various theories and models of service operations, such as the traditional “characteristics of services” and Customer Contact Theory. The UST has significant operational corollaries pertaining to capacity and demand management, service quality, services strategy, and so forth. The UST provides a common reference point to which services management researchers can anchor future theory‐building and theory‐testing research.     

A contingency approach to resource‐creation processes

The resource‐based view has provided valuable insights into sources of competitive advantage, but little attention has been paid to the processes of resource creation. To address this shortcoming, this paper reviews the strategy process literature, explaining the theoretical positions and assumptions that underpin different types of process. It then examines the mechanisms by which resources have been found to be created; luck, resource picking, internal development and alliances. Next, a series of resource‐creation pathways that illustrate the different routes firm inputs might take on the way to becoming unique and valuable resources is developed. These pathways are also discussed in terms of the strategy processes through which they are developed, and the appropriate resource‐creation processes. The review is then extended with the introduction of two contingent variables ? task complexity and environmental stability – and the resource‐creation processes that are congruent with different combinations of these variables are explored. From this review, one is able to identify the combination of complex task and stable environment likely to be the most conducive to resource creation. Finally, the paper explores opportunities that firms might have to engineer stability and complexity in some parts of their operations with the aim of developing a resource‐based advantage.     

Revisiting the relationship between ownership and control in international business operations: Lessons from transition economies

This article develops a conceptual model linking two interrelated dimensions of foreign business operations in transition economies: resources committed to the entry in terms of ownership, and resources committed to control the operations. The model outlines four types of foreign operations in transition economies: 1) arm's length contractor (low degree of foreign ownership and low level of foreign control over the operations); 2) hands-on contractor (low ownership and high control); 3) brand protector (high ownership and control); and 4) market share maximizer (high ownership and low control). These types are illustrated with examples of companies in four sectors of the Russian economy: metals, textiles, oil products, and beverages.We contribute to the literature on ownership and control in foreign operations, and their linkage to resource commitment at different stages of operations. In particular, we explicate situations characteristic of transition economies where ownership and control are not positively correlated. Moreover, we illustrate that the (financial) resources committed at the entry stage do not necessarily correlate with the managerial resources committed to the operations. For example, contractual collaborations in transition economies typically require few financial resources but more managerial resource commitment than in more developed market economies. On the other hand, operations with major commitment in the form of foreign equity investment may be managed autonomously by a local manager or a minority shareholder.     

Competition and The Operational Performance of Hospitals: The Role of Hospital Objectives

We examine the effect of a hospital's objective (i.e., non‐profit vs. for‐profit) in hospital markets for elective care. Using game‐theoretic analysis and queueing models to capture the operational performance of hospitals, we compare the equilibrium behavior of three market settings in terms of such criteria as waiting times and patient costs from waiting and hospital payments. In the first setting, a monopoly, patients are served exclusively by a single non‐profit hospital; in the second, a homogeneous duopoly, patients are served by two competing non‐profit hospitals. In our third setting, a heterogeneous duopoly, the market is served by one non‐profit hospital and one for‐profit hospital. A non‐profit hospital provides free care to patients, although they may have to wait; for‐profit hospitals charge a fee to provide care with minimal waiting. A comparison between the monopolistic and each of the duopolistic settings reveals that the introduction of competition can hamper a hospital's ability to attain economies of scale and can also increase waiting times. Moreover, the presence of a for‐profit sector may be desirable only when the hospital market is sufficiently competitive. A comparison across the duopolistic settings indicates that the choice between homogeneous and heterogeneous competition depends on the patients' willingness to wait before receiving care and the reimbursement level of the non‐profit sector. When the public funder is not financially constrained, the presence of a for‐profit sector may allow the funder to lower both the financial costs of providing coverage and the total costs to patients. Finally, our analysis suggests that the public funder should exercise caution when using policy tools that support the for‐profit sector—for example, patient subsidies—because such tools may increase patient costs in the long run; it might be preferable to raise the non‐profit sector's level of reimbursement.     

Surgical Suites' Operations Management

Surgical suites are a key driver of a hospital's costs, revenues, and utilization of postoperative resources such as beds. This article describes some commonly occurring operations management problems faced by the managers of surgical suites. For three of these problems, the article also provides preliminary models and possible solution approaches. Its goal is to identify open challenges to spur further research by the operations management community on an important class of problems that have not received adequate attention in the literature, despite their economic importance.     

Performance Metric Portfolios: A Framework and Empirical Analysis

The widespread adoption of supply chain management principles suggests that managers recognize the importance of evaluating operational decisions holistically. However, it is often difficult to link specific operational practices to strategic level outcomes and in turn to corporate financial results. This presents problems for both managers and academic researchers attempting to justify the often high cost of operational improvement initiatives in terms of objective accounting metrics. This study provides evidence that it is possible to demonstrate linkages between carefully chosen portfolios of tactical, strategic, and financial metrics. Survey data from 118 manufacturers are used to evaluate hypotheses linking multilevel metrics of performance across three well‐established strategic foci. We present portfolios of metrics drawn from the literature and from the Supply Chain Counciľs supply‐chain operations reference model and related design and customer chain models. Our analysis suggests that metric portfolios in which tactical metrics are designed to match strategic‐level metrics, based on alignment with a specific strategic focus, provide clearer mechanisms for understanding performance linkages.     

Benefits of Surgical Smoothing and Spare Capacity: An Econometric Analysis of Patient Flow

In this study, we examine the hospital's ability to admit patients from its emergency department. From a medical perspective, the number of patients being admitted should depend solely on the patients’ clinical conditions. Using a large‐scale econometric study that includes detailed operational and clinical data on all cardiac patient encounters from a set of 128 hospitals over a period of four years, we show that this is not the case. In particular, we find that independent of their medical condition, many emergency patients are denied hospital admission because of a lack of inpatient beds. Our analysis suggests that having one more inpatient bed at the start of a day can increase the likelihood of an emergency room patient admission by around 3% on average. We examine two policies – active discharge and demand smoothing – that can help hospitals improve patient access. We find that some hospitals actively discharge inpatients when beds become scarce; hospitals that follow such an active discharge protocol are, on average, able to admit more patients. We also investigate to what extent the hospital's ability to smooth its surgical schedule impacts hospital admissions. Hospitals tend to schedule their elective patients early in the week (Mondays and Tuesdays), and discharge them by the weekend in order to minimize weekend staffing, effectively maximizing bed occupancy during the middle of the week. This “weekend effect” artificially induces variability, and reduces effective system capacity. We find that by scheduling patients more uniformly over the week, hospitals can dramatically increase patient access, obviating the need for active discharges or additional capacity investment. Our analysis quantifies these effects, and can help hospitals make effective capacity management decisions in order to improve patient flow.     

Looking Inside Glitch Mitigation Capability: The Effect of Intraorganizational Communication Channels

Manufacturers can reduce the occurrence of glitches in their operations by building capabilities to prevent them, yet mitigation capabilities are also needed to contain the effects of the glitches that will still inevitably occur every now and then. We examine the glitch mitigation capability of a production process from an information‐processing perspective and propose that (i) the impact of operational glitches on delivery performance is contingent on the formalization of intrafirm communication channels and (ii) this effect is stronger when formal communication channels are complemented with informal channels. We test our model in a sample of 163 make‐to‐order production processes and find support for the first hypothesis and partial support for the second hypothesis. The statistical analyses also reveal nonhypothesized empirical regularities, which we explore through an additional qualitative study based on 34 site visits and 30 interviews with production planners. The results have practical implications for the design of intraorganizational communication channels, and they also contribute to the research on organizational resilience and communications by showing that when coping with disruptions, the formal communication channels have advantages that are seldom discussed in the literature or recognized by practitioners.     

The Role of Operations Management Across the Entrepreneurial Value Chain

This special issue contains articles that exemplify the role of operations management across the entrepreneurial value chain. This value chain encompasses all stages of the entrepreneurial phenomenon, including technology commercialization, where discovery, commitment, organization, and growth must take place. We report on a literature search that identifies research questions categorized with respect to topics crucial to operations management scholars and classify these questions under each stage of this value chain. The search guides the development of an evolutionary path for the use of resources, routines, and reputation (3Rs), often lacking in this process, and enables us to propose modeling and topical gaps in the literature. We offer a framework to set up exemplars for operational tradeoffs uniquely associated with the entrepreneurial value chain. We also articulate how five contributed articles in this issue tackle some of these tradeoffs, prior to introducing four perspective pieces. We hope this discussion motivates follow‐on work and triggers a significant increase in the flow of articles that make it to both entrepreneurship and operations management top‐tier academic and practitioner publications.