Physical–cyber–human framework-based resilience evaluation toward urban power system: Case study from China

Because the increased frequency, intensity, and duration of extreme weather events have significantly challenged power systems, there has been an increased interest in resilient power systems. This article establishes a multicriteria resilience evaluation framework for urban power systems from a physical–cyber–human system perspective, in which the two principal elements responsible for power system function degradation are described, the three major domains comprising urban power systems are explained, four core capacities that positively contribute to power system resilience are proposed, and 15 (11 objective and four subjective) power system resilience evaluation indicators are identified. Fuzzy hesitant judgment and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) aggregation method are employed to minimize the expert divergence and maximize the group consensus. A validation method is designed and a comparison with commonly applied performance-based and attributes-based evaluation methods is conducted. The applicability of the evaluation framework is verified using data from four Chinese municipalities: Shanghai, Beijing, Chongqing, and Tianjin. It was found that Shanghai's resilience was the best, and Chongqing's physical resistance disadvantages would result in the greatest difficulties in coping with extreme event disturbances. Physical, cyber, and human domain resilience enhancement strategies are given for different cities separately. This study provides a practical tool to evaluate, compare, and enhance power system resilience for governments and public utilities.     

Perspectives on Cybersecurity Information Sharing among Multiple Stakeholders Using a Decision‐Theoretic Approach

The government, private sectors, and others users of the Internet are increasingly faced with the risk of cyber incidents. Damage to computer systems and theft of sensitive data caused by cyber attacks have the potential to result in lasting harm to entities under attack, or to society as a whole. The effects of cyber attacks are not always obvious, and detecting them is not a simple proposition. As the U.S. federal government believes that information sharing on cybersecurity issues among organizations is essential to safety, security, and resilience, the importance of trusted information exchange has been emphasized to support public and private decision making by encouraging the creation of the Information Sharing and Analysis Center (ISAC). Through a decision‐theoretic approach, this article provides new perspectives on ISAC, and the advent of the new Information Sharing and Analysis Organizations (ISAOs), which are intended to provide similar benefits to organizations that cannot fit easily into the ISAC structure. To help understand the processes of information sharing against cyber threats, this article illustrates 15 representative information sharing structures between ISAC, government, and other participating entities, and provide discussions on the strategic interactions between different stakeholders. This article also identifies the costs of information sharing and information security borne by different parties in this public‐private partnership both before and after cyber attacks, as well as the two main benefits. This article provides perspectives on the mechanism of information sharing and some detailed cost–benefit analysis.     

Similarities and Differences between Leadership and Management: High‐Performance Competencies in the British Royal Navy

This paper describes the research, analysis and development of a model clarifying the similarities and differences in competencies and personality factors 1 associated with effective Leadership and Management in the Royal Navy. A questionnaire study was conducted on a sample of 261 Officers and Ratings (Sailors). Their performance was rated through the organization's rigorous appraisal process, whilst competency and personality data were gathered through the Occupational Personality Questionnaire and the Leadership Dimensions Questionnaire. The results identify the common and unique relevance of specific competencies and personality factors and so provide an illuminating insight into the differences between the constructs of leadership and management. The critical factors related to effective leadership and management performance are also identified.     

Global supplier development considering risk factors using fuzzy extended AHP-based approach

Global supplier development is a multi-criterion decision problem which includes both qualitative and quantitative factors. The global supplier selection problem is more complex than domestic one and it needs more critical analysis. The aim of this paper is to identify and discuss some of the important and critical decision criteria including risk factors for the development of an efficient system for global supplier selection. Fuzzy extended analytic hierarchy process (FEAHP) based methodology will be discussed to tackle the different decision criteria like cost, quality, service performance and supplier's profile including the risk factors involved in the selection of global supplier in the current business scenario. FEAHP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the customers and experts for each comparison are tapped in the form triangular fuzzy numbers to construct fuzzy pair-wise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria and attributes at wider perspective. The proposed model can provide not only a framework for the organization to select the global supplier but also has the capability to deploy the organization's strategy to its supplier.     

Understanding the Participation of Critical Task Specialists in Strategic Decision Making*

This paper explores the role of critical task specialists in strategic decision making and presents a theoretical model relating critical task specialist participation in decision making to the organization's overall strategy and the nature of the decision. This exploratory study examines scope and intensity of physician participation in hospital decision making. Intensity of critical task specialist participation is explained by content of the decision and by the organization's strategy, while scope of participation is explained by decision content. The findings suggest the need for more complex models of participation than are normally used in decision-making research. The findings also suggest that executives, in managing strategic decision-making processes, should pay attention to questions of both scope and intensity of participation. The results suggest that critical task specialists play a different role in the decision process, depending on specific decision content and organization strategy.     

Network closure's impact on firms’ competitive advantage: The mediating roles of knowledge processes

This study draws on the knowledge management and social network disciplines to examine the effect of network closure on organizations’ competitive advantage. We hypothesize that the level of network closure affects an organization's capability of knowledge identification, knowledge transfer, knowledge protection and knowledge institutionalization; these capabilities in turn affect an organization's competitive advantage. Thus, we model network closure as indirectly affecting an organization's competitive advantage. A Partial Least Square (PLS) analysis of the survey data of 78 Chinese petrochemical firms shows that network closure can both enhance and decrease an organization's competitive advantage. Network closure enhances an organization's competitive advantage by facilitating knowledge protection and transfer (via institutionalization), but decreases competitive advantage by hindering knowledge identification. Environmental dynamics that the organizations encounter are observed to moderate the effect of network closure. Combining our results with the findings from the literature, we propose that organizations operating in a dynamic environment, where the domain knowledge is in a state of flux, need to place great importance on knowledge identification; such organizations should choose a sparse network that allows them to receive diverse knowledge. In contrast, organizations operating in a stable environment should opt for a dense network to protect their knowledge and facilitate transfer of required knowledge.     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Rethinking Resilience Analytics

The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.     

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

A Goal Programming Model for Effective Segment Determination: A Comment and Application

This note presents a model for the effective market segment determination problem. The integer goal programming model was developed for a nonprofit health care organization. The approach is an alternative application of a model presented by McClure and Wells [7]. The model includes input from the organization's clients as well as the organization's decision makers. A distinctive element is the simultaneous development of market segments and the consideration of management, institutional, and resource constraints. The purpose of the model was to aid the decision makers in determining if effective market segments exist which can be reached with information and educational materials concerning infant nutrition and breastfeeding. The model was tested and found to be effective in helping the decision makers segment their clients.     

Decoding the Signal Effects of Job Candidate Attraction to Corporate Social Practices

This article seeks to go beyond the implied assumption from previous research that job candidate attraction to corporate social practices is equivalent across individuals. To this end, we propose a framework for categorizing individuals' attraction to different corporate social performance profiles. Our framework is grounded in relational models theory and Mitroff's model of managers' “ideal organizations.” An inductive approach was used to elaborate upon the model and assess the extent to which candidates preferences vary. Data were collected from prospective job seekers regarding their attraction to social practices that benefit or harm various stakeholders, and these responses were used to develop profiles of job candidates' attraction to distinct profiles of organizations' social practices. The results provide a guide for managers who wish to improve the likelihood that an organization's social practices reflect what is best about its culture.     

Resilienz und Selbstsorge im Coaching

Resilience is the internal power of resistance to stress and to threats to physical and mental health. Self-care refers to an active, self-governing organization of one’s own life, in order to deal with stress and threats. When we take these two concepts as polarities, we can understand self-care as an endeavor towards realizing and using of one’s own power of resistance. The author discusses the mental resources of resilience which may be useful in coaching processes in order to deal with situations of conflict and crisis. This can be understood as a form of training in self-care.     

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

Resilience is the capability of a system to adjust its functionality during a disturbance or perturbation. The present work attempts to quantify resilience as a function of reliability, vulnerability, and maintainability. The approach assesses proactive and reactive defense mechanisms along with operational factors to respond to unwanted disturbances and perturbation. This article employs a Bayesian network format to build a resilience model. The application of the model is tested on hydrocarbon‐release scenarios during an offloading operation in a remote and harsh environment. The model identifies requirements for robust recovery and adaptability during an unplanned scenario related to a hydrocarbon release. This study attempts to relate the resilience capacity of a system to the system's absorptive, adaptive, and restorative capacities. These factors influence predisaster and postdisaster strategies that can be mapped to enhance the resilience of the system.     

Resilience Analysis of Countries under Disasters Based on Multisource Data

Disasters occur almost daily in the world. Because emergencies frequently have no precedent, are highly uncertain, and can be very destructive, improving a country's resilience is an efficient way to reduce risk. In this article, we collected more than 20,000 historical data points from disasters from 207 countries to enable us to calculate the severity of disasters and the danger they pose to countries. In addition, 6 primary indices (disaster, personal attribute, infrastructure, economics, education, and occupation) including 38 secondary influencing factors are considered in analyzing the resilience of countries. Using these data, we obtained the danger, expected number of deaths, and resilience of all 207 countries. We found that a country covering a large area is more likely to have a low resilience score. Through sensitivity analysis of all secondary indices, we found that population density, frequency of disasters, and GDP are the three most critical factors affecting resilience. Based on broad‐spectrum resilience analysis of the different continents, Oceania and South America have the highest resilience, while Asia has the lowest. Over the past 50 years, the resilience of many countries has been improved sharply, especially in developing countries. Based on our results, we analyze the comprehensive resilience and provide some optimal suggestions to efficiently improve resilience.     

Workforce/Population,Economy, Infrastructure,Geography, Hierarchy,and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.     

Escape from organizational nihilism: Leadership core values for Business Excellence

An earlier contribution to this journal focused on Leadership for Business Excellence, as reflected in business and performance excellence models which provide the criteria for various international quality prizes. Consideration of leadership in such models is bent toward competencies and their deployment and, also, to involvement in non-delegable activities deemed critical to the organization's competitive position. Herein a leadership core value set is developed, deployment of which can contribute to systemic leadership throughout the organization. Two subsequent contributions to this journal will examine leadership core value deployment and assessment of Leadership for Business Excellence.     

Strategic Preparedness for Recovery from Catastrophic Risks to Communities and Infrastructure Systems of Systems

Natural and human‐induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human–organizational–cyber–physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta‐modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision‐making process and its associated actions. These must be: implemented in advance of a natural or human‐induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers’ implicit and explicit acceptance of various risks and tradeoffs). The inoperability input‐output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies.     

Developing a questionnaire for the four-stage model of operations strategy

The development of a questionnaire to classify an organization's operations in accordance with the Hayes and Wheelwright four-stage model is described. Despite its widespread acceptance, little practical application of the model is reported in the literature. This questionnaire offers the means of analysing the strategic role played by operations in an organization, and will be of use to managers, consultants, researchers and others conducting a strategic audit of an organization's operations. The paper uses the results of a large-scale postal survey using the questionnaire to test its usefulness as a research instrument in practice. The survey indicated that the questionnaire is a useful research instrument across a wide range of UK organizations, services as well as manufacturers, not for profits as well as profit seekers. The results of follow-up interviews with multiple respondents in three organizations are also reported. These highlighted a number of ways that the questionnaire might be improved.     

Leveraging Supply Chain Integration through Planning Comprehensiveness: An Organizational Information Processing Theory Perspective

This study extends prior research on supply chain planning and integration by examining the underlying capabilities by which firms exploit the information they gain from integration activities. We use organizational information processing theory (OIPT) to develop hypotheses that identify the comprehensiveness of an organization's supply chain planning capabilities as an important mediator in the relationship between its supply chain integration activities and its operational performance. Further, our interpretation of OIPT suggests that an organization's usage of technology‐enabled supply chain management systems (SCMS) moderates these effects. Using survey data from 445 global firms, we estimate the corresponding moderated‐mediation structural model. The results indicate that usage of SCMS enables organizations to better utilize the information they gain from external integration efforts (relationships with customers and suppliers), thus improving the comprehensiveness of their supply chain planning capabilities. In contrast, the use of SCMS appears to be a partial substitute for internal integration as a driver of planning comprehensiveness. Most importantly, the results suggest that planning comprehensiveness is a significant generative means by which integration and technology investments produce superior operational performance. These findings provide a richer and more theoretically grounded explanation of relationships between supply chain integration, supply chain planning, and operational performance.