Introduction to Special Issue on POM Research on Emerging Markets

This special issue presents production and operations management research papers from emerging markets worldwide, focusing on their unique contexts, institutions and markets. Collectively, they offer insights into the unique operations management strategies and practices that firms face in emerging markets. The magnitude and pace of change is staggering. As a result, execution and managing growth in these emerging markets represents both huge operational and supply chain risks, and simultaneously vast opportunities. We are confident that these papers stimulate new operations management research while highlighting the effective use of different research methodologies. We welcome you to enjoy this special issue of POMS. We express our deep gratitude to editorial team for their support. We especially wish to thank the authors for their contributions to this special issue.     

Martin K. Starr: A Visionary Proponent for System Integration,Modular Production,and Catastrophe Avoidance

Martin K. Starr facilitated the creation of an identity for production and operations management (POM) as an academic discipline. This paper aims to summarize Starr's substantial contributions to scholarly inquiry on system integration and interfunctional coordination, modular production, and catastrophe avoidance. Even after four decades, we describe how his legacy in these areas continues to define several major drivers of operations and supply chain management research and practice. Starr has influenced several generations of students, professors, and executives with his writings, teaching, and leadership roles in the POM community that include 32 years on the faculty of the Columbia School of Business, 15 years as Editor‐in‐Chief of Management Science, and presidency of the Production and Operations Management Society.     

Researchers' Perspectives on Supply Chain Risk Management

Supply chain risk management (SCRM) is a nascent area emerging from a growing appreciation for supply chain risk by practitioners and by researchers. However, there is diverse perception of research in supply chain risk because these researchers have approached this area from different domains. This paper presents our study of this diversity from the perspectives of operations and supply chain management scholars: First, we reviewed the researchers' output, i.e., the recent research literature. Next, we surveyed two focus groups (members of Supply Chain Thought Leaders and International SCRM groups) with open‐ended questions. Finally, we surveyed operations and supply chain management researchers during the 2009 INFORMS meeting in San Diego. Our findings characterize the diversity in terms of three “gaps”: a definition gap in how researchers define SCRM, a process gap in terms of inadequate coverage of response to risk incidents, and a methodology gap in terms of inadequate use of empirical methods. We also list ways to close these gaps as suggested by the researchers.     

Advancing Risk‐Informed Decision Making in Managing Defense Nuclear Waste in the United States: Opportunities and Challenges for Risk Analysis

An omnibus spending bill in 2014 directed the Department of Energy to analyze how effectively Department of Energy (DOE) identifies, programs, and executes its plans to address public health and safety risks that remain as part of DOE's remaining environmental cleanup liabilities. A committee identified two dozen issues and associated recommendations for the DOE, other federal agencies, and the U.S. Congress to consider, as well as other stakeholders such as states and tribal nations. In regard to risk assessment, the committee described a risk review process that uses available data, expert experience, identifies major data gaps, permits input from key stakeholders, and creates an ordered set of risks based on what is known. Probabilistic risk assessments could be a follow‐up from these risk reviews. In regard to risk management, the states, in particular, have become major drivers of how resources are driven. States use different laws, different priorities, and challenge DOE's policies in different ways. Land use decisions vary, technology choices are different, and other notable variations are apparent. The cost differences associated with these differences are marked. The net result is that resources do not necessarily go to the most prominent human health and safety risks, as seen from the national level.     

Supply chain outsourcing under exchange rate risk and competition

This paper studies the impacts of foreign exchange risk and competition intensity on supply chain companies who are involved in offshore-outsourcing activities. In particular, we develop a variational inequality model that considers firms' decision-making regarding pricing, offshore outsourcing, transportation, and in-house production under competition and foreign exchange uncertainty. We also use a series of simulation examples to answer questions regarding outsourcing and pricing strategies of supply chain firms with different risk attitudes, and explore numerically their associated profits and incurred risks.     

In Search of Perfect Foresight? Policy Bias,Management of Unknowns,and What Has Changed in Science Policy Since the Tohoku Disaster

The failure to foresee the catastrophic earthquakes, tsunamis, and nuclear accident of 2011 has been perceived by many in Japan as a fundamental shortcoming of modern disaster risk science. Hampered by a variety of cognitive and institutional biases, the conventional disaster risk management planning based on the “known risks” led to the cascading failures of the interlinked disaster risk management (DRM) apparatus. This realization led to a major rethinking in the use of science for policy and the incorporations of lessons learned in the country's new DRM policy. This study reviews publicly available documents on expert committee discussions and scientific articles to identify what continuities and changes have been made in the use of scientific knowledge in Japanese risk management. In general, the prior influence of cognitive bias (e.g., overreliance on documented hazard risks) has been largely recognized, and increased attention is now being paid to the incorporation of less documented but known risks. This has led to upward adjustments in estimated damages from future risks and recognition of the need for further strengthening of DRM policy. At the same time, there remains significant continuity in the way scientific knowledge is perceived to provide sufficient and justifiable grounds for the development and implementation of DRM policy. The emphasis on “evidence‐based policy” in earthquake and tsunami risk reduction measures continues, despite the critical reflections of a group of scientists who advocate for a major rethinking of the country's science‐policy institution respecting the limitations of the current state science.     

Dynamic Supply Risk Management with Signal‐Based Forecast,Multi‐Sourcing,and Discretionary Selling

We examine the critical role of advance supply signals—such as suppliers’ financial health and production viability—in dynamic supply risk management. The firm operates an inventory system with multiple demand classes and multiple suppliers. The sales are discretionary and the suppliers are susceptible to both systematic and operational risks. We develop a hierarchical Markov model that captures the essential features of advance supply signals, and integrate it with procurement and selling decisions. We characterize the optimal procurement and selling policy, and the strategic relationship between signal‐based forecast, multi‐sourcing, and discretionary selling. We show that higher demand heterogeneity may reduce the value of discretionary selling, and that the mean value‐based forecast may outperform the stationary distribution‐based forecast. This work advances our understanding on when and how to use advance supply signals in dynamic risk management. Future supply risk erodes profitability but enhances the marginal value of current inventory. A signal of future supply shortage raises both base stock and demand rationing levels, thereby boosting the current production and tightening the current sales. Signal‐based dynamic forecast effectively guides the firm's procurement and selling decisions. Its value critically depends on supply volatility and scarcity. Ignoring advance supply signals can result in misleading recommendations and severe losses. Signal‐based dynamic supply forecast should be used when: (a) supply uncertainty is substantial, (b) supply‐demand ratio is moderate, (c) forecast precision is high, and (d) supplier heterogeneity is high.     

ISO 31000:2009—Setting a New Standard for Risk Management

Last year saw the publication of IS0 31000:2009, a new globally accepted standard for risk management together with a new, associated vocabulary in ISO Guide 73:2009. These were developed through a consensus‐driven process over four years, through seven drafts, and involving the input of hundreds of risk management professionals around the world. The new standard supports a new, simple way of thinking about risk and risk management and is intended to begin the process of resolving the many inconsistencies and ambiguities that exist between many different approaches and definitions. While most decisionmakers seem to welcome the new standard and it has so far received very good reviews, it does create challenges for those who use language and approaches that are unique to their area of work but different from the new standard and guide. The need for compromise and change is the inevitable consequence of standardization     

Supply Chain Strategies and International Tax Arbitrage

What motivates the geographic footprint of the supply chains that multinational firms (MNFs) deploy? Traditional research in the operations and supply chain management literature tends to recommend locations primarily based on differentials in production costs and the ramifications of physical distance ignoring the role of taxation. MNFs that strategically position parts of their supply chains in low‐tax locations can allocate the profits across the divisions to improve post‐tax profits. For the profit allocation to be defensible to tax authorities, the divisional operations must possess real decision authority and bear meaningful risks. Generally speaking, the greater the transfer of risk and control, the larger the allowable allocation of profit. These transfers may also create inefficiencies due to misalignment of business goals and attitudes toward risk. We model these trade‐offs in the context of placing in a low‐tax region a subsidiary that oversees product distribution (as a limited risk distributor commissionnaire, limited risk distributor, or fully fledged distributor). Our analysis demonstrates that the MNF's preferences regarding the operating structures are not necessarily an obvious ordering based on the amount of risk and decision authority transferred to the division in the low‐tax jurisdiction. We derive and analyze threshold values of the performance parameters that describe the main trade‐offs involved in selecting an operating structure. We find some of the optimal decisions to exhibit interesting non‐monotone behavior. For instance, profits can increase when the tax rate in the low‐tax jurisdiction increases. Numerical analysis shows that the Limited‐Risk Distributor structure is rarely optimal and quantifies when each alternative dominates it.     

考虑退货风险的制造商双渠道供应链定价决策研究

针对事前不确定性导致的退货问题,从消费者行为出发,建立考虑定价差异和退货风险双重因素的市场需求函数,通过Stackelberg博弈模型研究四种不同市场需求情形在不同定价模式下双渠道供应链的最优决策。在数值仿真部分,对不同情形决策之间比较、供应链收益进行比较分析。研究表明：电子渠道销售价格与电子渠道的市场基本需求成正比,批发价和传统渠道销售价格与传统渠道的市场基本需求成正比,实体店体验服务水平和传统渠道的市场基本需求的关系（线性关系）则根据某些条件而定;退货风险对供应链决策的影响与定价模式、市场需求影响因素相关;制造商偏好于定价不相等模式,零售商偏好于定价相等模式;退货风险与定价差异对供应链成员收益的影响与定价模式相关,对整体供应链收益的影响与定价模式和退货率大小有关。     

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.     

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.     

Cyber Security Risk Management: Public Policy Implications of Correlated Risk,Imperfect Ability to Prove Loss,and Observability of Self‐Protection

The correlated nature of security breach risks, the imperfect ability to prove loss from a breach to an insurer, and the inability of insurers and external agents to observe firms’ self‐protection efforts have posed significant challenges to cyber security risk management. Our analysis finds that a firm invests less than the social optimal levels in self‐protection and in insurance when risks are correlated and the ability to prove loss is imperfect. We find that the appropriate social intervention policy to induce a firm to invest at socially optimal levels depends on whether insurers can verify a firm's self‐protection levels. If self‐protection of a firm is observable to an insurer so that it can design a contract that is contingent on the self‐protection level, then self‐protection and insurance behave as complements. In this case, a social planner can induce a firm to choose the socially optimal self‐protection and insurance levels by offering a subsidy on self‐protection. We also find that providing a subsidy on insurance does not provide a similar inducement to a firm. If self‐protection of a firm is not observable to an insurer, then self‐protection and insurance behave as substitutes. In this case, a social planner should tax the insurance premium to achieve socially optimal results. The results of our analysis hold regardless of whether the insurance market is perfectly competitive or not, implying that solely reforming the currently imperfect insurance market is insufficient to achieve the efficient outcome in cyber security risk management.     

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.     

Perceptions of Risk and Vulnerability Following Exposure to a Major Natural Disaster: The Calgary Flood of 2013

Many studies have examined the general public's flood risk perceptions in the aftermath of local and regional flooding. However, relatively few studies have focused on large‐scale events that affect tens of thousands of people within an urban center. Similarly, in spite of previous research on flood risks, unresolved questions persist regarding the variables that might influence perceptions of risk and vulnerability, along with management preferences. In light of the opportunities presented by these knowledge gaps, the research reported here examined public perceptions of flood risk and vulnerability, and management preferences, within the city of Calgary in the aftermath of extensive flooding in 2013. Our findings, which come from an online survey of residents, reveal that direct experience with flooding is not a differentiating factor for risk perceptions when comparing evacuees with nonevacuees who might all experience future risks. However, we do find that judgments about vulnerability—as a function of how people perceive physical distance—do differ according to one's evacuation experience. Our results also indicate that concern about climate change is an important predictor of flood risk perceptions, as is trust in government risk managers. In terms of mitigation preferences, our results reveal differences in support for large infrastructure projects based on whether respondents feel they might actually benefit from them.     

Promotion Planning and Supply Chain Contracting in a High–Low Pricing Environment

Demand forecast errors threaten the profitability of high–low price promotion strategies. This article shows how to match demand and supply effectively by means of two‐segment demand forecasting and supply contracts. We find that demand depends on the path of past retail prices, which leads to only a limited number of reachable demand states. However, forecast errors cannot be entirely eliminated because competitive promotions entail some degree of random (i.e., last‐minute) pricing. A hedging approach can be deployed to distribute demand risk efficiently over multiple promotional campaigns and within the supply chain. A retailer that employs a portfolio of forward, option, and spot contracts can avoid both stockouts and excess inventories while achieving the first‐best solution and Pareto improvements. We provide an improved forecasting method as well as stochastic programs to solve for optimal production and purchasing policies such that the right amount of inventory is available at the right time. By connecting a stockpiling model of demand with the supply side, we derive insights on optimal risk management strategies for both manufacturers and retailers in a market environment characterized by frequent price promotions and multiple discount levels. We employ a data set of the German retail market for a key generator of store traffic—namely, diapers.     

An Empirical Model of Perceived Mortality Risks for Selected U.S. Arsenic Hot Spots

Researchers have long recognized that subjective perceptions of risk are better predictors of choices over risky outcomes than science‐based or experts’ assessments of risk. More recent work suggests that uncertainty about risks also plays a role in predicting choices and behavior. In this article, we develop and estimate a formal model for an individual's perceived health risks associated with arsenic contamination of his or her drinking water. The modeling approach treats risk as a random variable, with an estimable probability distribution whose variance reflects uncertainty. The model we estimate uses data collected from a survey given to a sample of people living in arsenic‐prone areas in the United States. The findings from this article support the fact that scientific information is essential to explaining the mortality rate perceived by the individuals, but uncertainty about the probability remains significant.     

Risk Management for Development—Assessing Obstacles and Prioritizing Action

Throughout the process of economic and social development, decisionmakers from the household to the state level are confronted with a multitude of risks: from health and employment risks, to financial and political crises, as well as environmental damages and from the local to global level. The World Bank's 2014 World Development Report (WDR) provides an in‐depth analysis of how the management of such risks can be improved. In particular, it argues that a proactive and integrated approach to risk management can create opportunities for fighting poverty and achieving prosperity—but also acknowledges substantial obstacles to its implementation in practice. This article presents and discusses these obstacles with respect to their causes, consequences, interlinkages, and solutions. In particular, these include obstacles to individual risk management, the obstacles that are beyond the control of individuals and thus require collective action, and, finally, the obstacles that affect the ability of governments and public authorities to manage risks. From these obstacles, this article derives a policy roadmap for the development of risk management strategies that are designed not only around the risk they have to cope with, but also around the practical obstacles to policy implementation.     

Radiological Risk Assessment by Convergence Methodology Model in RDD Scenarios

A radiological dispersal device (RDD) is a simple weapon capable of causing human harm, environmental contamination, disruption, area denial, and economic cost. It can affect small, large, or long areas depending on atmospheric stability. The risk of developing a radio‐induced cancer depends on exposure, and an effective response depends upon available timely guidance. This article proposes and demonstrates a convergence of three different capabilities to assess risk and support rapid safe resource efficient response. The three capabilities that are integrated are Hotspot for dispersion, RERF for epidemiological risk, and RESRAD‐RDD for response guidance. The combined methodology supports decisions on risk reduction and resource allocation through work schedules, the designation and composition of response teams, and siting for operations. In the illustrative RDD scenario, the contamination area for sheltering, evacuation, and long‐term public concern was greatest for calm atmospheric conditions, whilst close‐quarter responders faced highest dose rates for neutral atmospheric conditions. Generally, the risks to women responders were found to be significantly greater than for men, and the risks to 20‐year‐old responders were three times that of their 60‐year‐old counterparts for similar exposure.     

A Framework for Understanding Risk Perception,Explored from the Perspective of the Water Practitioner

Sustainable urban water systems are likely to be hybrids of centralized and decentralized infrastructure, managed as an integrated system in water‐sensitive cities. The technology for many of these systems is available. However, social and institutional barriers, which can be understood as deeply embedded risk perceptions, have impeded their implementation. Risk perceptions within the water sector are often unrecognized or unacknowledged, despite their role in risk management generally in informing value judgments and specifically in ranking risks to achieve management objectives. There has been very little examination of the role of these risk perceptions in advancing more sustainable water supply management through the adoption of alternative sources. To address this gap, this article presents a framework that can be used as a tool for understanding risk perceptions. The framework is built on the relational theory of risk and presents the range of human phenomena that might influence the perception of an “object at risk” in relation to a “risk object.” It has been synthesized from a critical review of theoretical, conceptual, and empirical studies of perception broadly and risk perception specifically, and interpreted in relation to water practitioners. For a water practitioner, the risk object might be an alternative water system, a component, a process, or a technology, and the object at risk could be public or environmental health, profitability, or professional reputation. This framework has two important functions: to allow practitioners to understand their own and others’ risk perceptions, which might differ, and to inform further empirical research.