A framework for optimizing managerial decision

There have been numerous attempts to analyze and formulate management problems within the general framework of Management Science and Operations Research, so as to help managers in their decision making problems. This paper focuses on the fundamental aspects of management decision making and introduces a conceptual framework in which problems of management and its different levels of functioning are systematically identified. Management problems are considered within two major categories: Enterprise and Operations problems. In spite of the difficulties in defining a clear-cut boundary between Enterprise and Operations problems, such a broad classification leads to three basic levels of management functioning, namely: Policy making, coordinating and implementation. The universal relationship among these three basic levels of functioning provides a basic functional element called the Management Triad. In contrast to classical higherarchial structure, triad structure provides a methodology to analyze relationships among different levels of management functions in terms of a given decision problem. It is demonstrated that for different decision making problems individuals can shift from one end of the triad to another and perform a different management function.A quantitative approach has been introduced for operations management decision making by adapting Shewhart's control chart philosophy which in turn provides for quasi-automated decisions for corrective action and also for filtering significant signals of change to appropriate management functioning levels.     

Performance measurement of supply chain management using the analytical hierarchy process

Performance measurement of supply chain management (SCM) is a rapidly growing multi-criteria decision-making problem owing to the large number of factors affecting decision-making. The right choice of performance metrics and measures is critical to the success and competitiveness of the firms in the era of globalisation. Recognising the multiple objective nature of the problem, this paper proposes the use of the analytical hierarchy process (AHP) methodology as aid in making SCM evaluation decisions. For pair-wise comparison in AHP, a survey methodology is used. The methodology presented can help firms to prioritise and formulate viable performance measurement strategies in the volatile and complex global decision environment from different balanced scorecard (BSC) perspectives. A demonstration of the application of this methodology in a real life problem is presented.     

Development of Economic Consequence Methodology for Process Risk Analysis

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies.     

基于悲观度的双边匹配决策问题研究

双边匹配决策问题一直是经济管理和计算几何等领域研究的热点和难点问题之一。针对双边主体偏好信息为序值的双边匹配决策问题,提出基于悲观度的新方法,给出具有序值形式信息的双边匹配决策问题的描述,引入能够反映功利型中介悲观度的满意度和支付的计算公式。在此基础上,考虑到匹配主体对之间的满意度和功利型中介的收益,构建求解双边匹配决策问题的多目标优化模型。运用基于隶属函数的加权和方法将多目标优化模型转化为单目标优化模型,运用Hungarian法进行求解获得双边匹配方案,通过算例说明给出方法的有效性。计算结果表明,悲观度取值不同,运用该方法获得的双边匹配方案也可能会不同,即双边匹配方案能反映不同功利型中介的不同风险偏好。     

Risk-Adjusted Mission Value: Trading Off Mission Risk for Mission Value

This article develops a decision-theoretic methodology for the risk-adjusted mission value (RAMV) for selecting between alternative missions in the presence of uncertainty in the outcomes of the missions. This methodology permits trading off mission risk for mission value, something that probabilistic risk analysis cannot do unless it explicitly incorporates both mission value and risk aversion of the project management. The methodology, in its complete implementation, is consistent with the decision theory known as expected utility theory, although it differs from conventional decision theory in that the probabilities and all but one of the utilities are not those of the decision maker. The article also introduces a new interpretation of risk aversion. The methodology is consistent with the elementary management concept concerning division of labor. An example is presented for selecting between discrete alternatives-four landing sites on Mars. A second example is presented for selecting among a set of continuous alternatives-a comet flyby distance. The methodology is developed within the context of scientific missions, but the methodology is equally applicable to any situation requiring outcome value judgments, probability judgments, and risk aversion judgments by different constituencies.     

Using Structured Decision Making to Help Implement a Precautionary Approach to Endangered Species Management

Endangered species protection is a significant risk management concern throughout North America. An extensive conceptual literature emphasizes the role to be played by precautionary approaches. Risk managers, typically working in concert with concerned stakeholders, frequently cite the concept as key to their efforts to prevent extinctions. Little has been done, however, to evaluate the multidimensional impacts of precautionary frameworks or to assist in the examination of competing precautionary risk management options as part of an applied risk management decision framework. In this article we describe how decision-aiding techniques can assist in the creation and analysis of alternative precautionary strategies, using the example of a multistakeholder committee charged with protection of endangered Cultus Lake salmon on the Canadian west coast. Although managers were required to adopt a precautionary approach, little attention had been given to how quantitative analyses could be used to help define the concept or to how a precautionary approach might be implemented in the face of difficult economic, social, and biological tradeoffs. We briefly review key steps in a structured decision-making (SDM) process and discuss how this approach was implemented to help bound the management problem, define objectives and performance measures, develop management alternatives, and evaluate their consequences. We highlight the role of strategy tables, employed to help participants identify, alternative management options. We close by noting areas of agreement and disagreement among participants and discuss the implications of decision-focused processes for other precautionary resource management efforts.     

Import Security: Assessing the Risks of Imported Food

We use data on food import violations from the FDA Operational and Administrative System for Import Support (OASIS) to address rising concerns associated with imported food, quantify import risks by product and by country of origin, and explore the usefulness of OASIS data for risk assessment. In particular, we assess whether there are significant trends in violations, whether import violations can be used to quantify risks by country and by product, and how import risks depend on economic factors of the country of origin. The results show that normalizing import violations by volume of imports provides a meaningful indicator of risk. We then use regression analysis to characterize import risks.  Using this model, we analyze import risks by product type, violation type, and economic factors of the country of origin.  We find that OASIS data are useful in quantifying food import risks, and that the rate of refusals provides a useful decision tool for risk management.  Furthermore, we find that some economic factors are significant indicators of food import risk by country.     

Presenting Geographic Information: Effects of Data Aggregation,Dispersion, and Users' Spatial Orientation

Geographic information systems (GIS) have taken on an increasingly important role supporting decision making in many organizations. GIS have been used to support a breadth of tasks including oil and mineral exploration, facility location, logistics support, and facilities management decisions. The effectiveness of GIS as a decision support tool comes primarily from the visual display of data in the form of maps. When presenting information as a geographic map, the level of data aggregation potentially affects aspects of task complexity such as information load and the potential for pattern recognition by the user. Other task attributes expected to be related to data aggregation effects include problem size, the degree of data dispersion, and users' spatial orientation skills. We conducted an experiment to study these effects and their interactions. Subjects used a GIS including map-based information characterized by different levels of problem size, data dispersion, and data aggregation. Spatial orientation skill was examined as a covariate in the experimental treatments. The results indicate that all of these geographic information characteristics have significant impacts on decision performance. Moreover, many interactions are present among the factors. We evaluate these interactions in order to derive implications for practice and for future research.     

When Can Scientific Studies Promote Consensus Among Conflicting Stakeholders?

While scientific studies may help conflicting stakeholders come to agreement on a best management option or policy, often they do not. We review the factors affecting trust in the efficacy and objectivity of scientific studies in an analytical‐deliberative process where conflict is present, and show how they may be incorporated in an extension to the traditional Bayesian decision model. The extended framework considers stakeholders who differ in their prior beliefs regarding the probability of possible outcomes (in particular, whether a proposed technology is hazardous), differ in their valuations of these outcomes, and differ in their assessment of the ability of a proposed study to resolve the uncertainty in the outcomes and their hazards—as measured by their perceived false positive and false negative rates for the study. The Bayesian model predicts stakeholder‐specific preposterior probabilities of consensus, as well as pathways for increasing these probabilities, providing important insights into the value of scientific information in an analytic‐deliberative decision process where agreement is sought. It also helps to identify the interactions among perceived risk and benefit allocations, scientific beliefs, and trust in proposed scientific studies when determining whether a consensus can be achieved. The article provides examples to illustrate the method, including an adaptation of a recent decision analysis for managing the health risks of electromagnetic fields from high voltage transmission lines.     

Developing a scale for assessing risk attitudes of agricultural decision makers

This study adapts a methodology formulated in the social sciences to develop a scale for measuring an economic agent’s attitude toward risk. The scale assesses risk attitudes by eliciting farmers’ opinions towards risk management tools using a Likert procedure. The methodology validates the scale with a scientific risk attitude measure and compares the scale to the farmers’ self-assessment of their risk attitudes. The resulting scale methodology could be administered to people without the need for personal interviews. The subjects for this study were Midwestern farmers, but the methodology can be applied to any sector of the agricultural industry.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

Are Adult Patients More Tolerant of Treatment Risks Than Parents of Juvenile Patients?

Understanding patient-specific differences in risk tolerance for new treatments that offer improved efficacy can assist in making difficult regulatory and clinical decisions for new treatments that offer both the potential for greater effectiveness in relieving disease symptoms, but also risks of disabling or fatal side effects. The aim of this study is to elicit benefit-risk trade-off preferences for hypothetical treatments with varying efficacy and risk levels using a stated-choice (SC) survey. We derive estimates of "maximum acceptable risk" (MAR) that can help decisionmakers identify welfare-enhancing alternatives. In the case of children, parent caregivers are responsible for treatment decisions and their risk tolerance may be quite different than adult patients' own tolerance for treatment-related risks. We estimated and compared the willingness of Crohn's disease (CD) patients and parents of juvenile CD patients to accept serious adverse event (SAE) risks in exchange for symptom relief. The analyzed data were from 345 patients over the age of 18 and 150 parents of children under the age of 18. The estimation results provide strong evidence that adult patients and parents of juvenile patients are willing to accept tradeoffs between treatment efficacy and risks of SAEs. Parents of juvenile CD patients are about as risk tolerant for their children as adult CD patients are for themselves for improved treatment efficacy. SC surveys provide a systematic method for eliciting preferences for benefit-risk tradeoffs. Understanding patients' own risk perceptions and their willingness to accept risks in return for treatment benefits can help inform risk management decision making.     

Analyzing Disaster Loss Trends: A Comparison of Normalization Methodologies in South Korea

Increasing concern for climate change adaptation and disaster risk reduction is driving the need for more accurate and sophisticated tools of analysis to protect populations. Standards of analysis that can normalize measurements under various contexts are particularly valuable in the global arena of disaster management. One concern that may benefit from normalizing is the analysis of disaster loss trends. Previous studies have used a combination of inflation, wealth, and societal factors in their normalization of disaster loss methodologies. This study examines the various normalization methods in previous research and applies a selection of eight formulae to 50 years of disaster data in South Korea. The results show both decreasing and increasing trends in disaster damage losses based on the methods, but there are curious biases under the results that may be artifacts of Korea's unique experiences in economic development. The conclusion discusses how the case of Korea may help to clarify the optimal normalization methodology for other countries.     

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

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

A Methodology for Determining Interactions in Probabilistic Safety Assessment Models by Varying One Parameter at a Time

In risk analysis problems, the decision‐making process is supported by the utilization of quantitative models. Assessing the relevance of interactions is an essential information in the interpretation of model results. By such knowledge, analysts and decisionmakers are able to understand whether risk is apportioned by individual factor contributions or by their joint action. However, models are oftentimes large, requiring a high number of input parameters, and complex, with individual model runs being time consuming. Computational complexity leads analysts to utilize one‐parameter‐at‐a‐time sensitivity methods, which prevent one from assessing interactions. In this work, we illustrate a methodology to quantify interactions in probabilistic safety assessment (PSA) models by varying one parameter at a time. The method is based on a property of the functional ANOVA decomposition of a finite change that allows to exactly determine the relevance of factors when considered individually or together with their interactions with all other factors. A set of test cases illustrates the technique. We apply the methodology to the analysis of the core damage frequency of the large loss of coolant accident of a nuclear reactor. Numerical results reveal the nonadditive model structure, allow to quantify the relevance of interactions, and to identify the direction of change (increase or decrease in risk) implied by individual factor variations and by their cooperation.     

Empirical Analysis of Farmers' Drought Risk Perception: Objective Factors,Personal Circumstances,and Social Influence

Drought‐induced water shortage and salinization are a global threat to agricultural production. With climate change, drought risk is expected to increase as drought events are assumed to occur more frequently and to become more severe. The agricultural sector's adaptive capacity largely depends on farmers’ drought risk perceptions. Understanding the formation of farmers’ drought risk perceptions is a prerequisite to designing effective and efficient public drought risk management strategies. Various strands of literature point at different factors shaping individual risk perceptions. Economic theory points at objective risk variables, whereas psychology and sociology identify subjective risk variables. This study investigates and compares the contribution of objective and subjective factors in explaining farmers’ drought risk perception by means of survey data analysis. Data on risk perceptions, farm characteristics, and various other personality traits were collected from farmers located in the southwest Netherlands. From comparing the explanatory power of objective and subjective risk factors in separate models and a full model of risk perception, it can be concluded that farmers’ risk perceptions are shaped by both rational and emotional factors. In a full risk perception model, being located in an area with external water supply, owning fields with salinization issues, cultivating drought‐/salt‐sensitive crops, farm revenue, drought risk experience, and perceived control are significant explanatory variables of farmers’ drought risk perceptions.     

基于公众偏好大数据分析的大群体应急决策质量动态演化研究

针对突发事件大群体应急决策质量及其影响因素的问题，探讨了公众参与视角下突发事件演化的全生命周期，以及各阶段下公众风险感知对应急决策质量的影响，并以“8·12天津港爆炸”为背景案例进行分析。首先，阐述了公众偏好大数据的获取及处理方式，并以此对“8·12天津港爆炸”案例的公众偏好大数据进行预处理和可视化；其次，通过TF-IDF算法提取并量化公众风险感知，建立大群体应急决策质量打分函数，衡量不同阶段的应急决策质量；最后，基于“8·12天津港爆炸”案例的微博数据进行大群体应急决策质量的动态演化分析，得出研究结论和启示。实证表明，所提的模型与方法具有较新颖的研究视角，能够为大数据背景下的应急响应和政府决策提供理论支持和方法辅助。     

基于ERM框架的商业银行内部审计机制研究

内部审计作为公司治理、风险管理和控制结构的一部分,是对其它控制进行的再控制.由原来的"独立评价职能"转变为"风险管理和公司治理",内部审计在银行业中的作用越来越重要.在风险与效益并存的时代,现代银行业倡导全面风险管理理念.本文选取了全面风险管理的研究视角,对全面风险管理体系下的商业银行内部审计角色定位及作用机制进行研究.     

Water resources management under multi-parameter interactions: A factorial multi-stage stochastic programming approach

The paper proposes a factorial multi-stage stochastic programming (FMSP) approach to support water resources management under uncertainty. This approach was developed based on the conventional inexact multi-stage stochastic programming method. Five alternative inexact multi-stage stochastic programming algorithms in addition to the conventional algorithm were introduced and bundled to offer multiple decision options that reflect decision makers' perspectives and the complexities in system uncertainties. More importantly, factorial analysis, a multivariate inference method, was introduced into the modeling framework to analyze the potential interrelationships among a variety of uncertain parameters and their impacts on system performance. The proposed approach was applied to a water resources management case. The desired water-allocation schemes were obtained to assist in maximizing the total net benefit of the system. Multiple uncertain parameters and their interactions were examined, and those that had significant influences on system performance were identified. For example, the medium flow in the third planning period was the system objective's most influential factor. Any variation of this factor would significantly influence the acquisition of the total net benefit in the community. The significant interactions were also identified, such as the interaction between the agricultural sector's penalty and the medium flow in the third planning period. Through the analysis of multi-parameter interactions, the interrelationships among the uncertain parameters could be further revealed.     

考虑风险关联和决策者偏好的海外投资国家风险评估研究

国家风险是我国企业海外投资优先考虑的外部因素,其构成的多维度性、不同维度风险之间关联的客观存在性以及企业决策者差异化的偏好等特点,加剧了海外投资国家风险评估的难度。本文引入风险关联和决策者偏好,提出一种二元语义DEMATEL法、基尼系数客观赋权法与VIKOR法相结合的海外投资国家风险评估方法来量化风险关联,并从主客观集成视角确定风险评估指标权重,以期得到不同决策机制下的投资风险国别排序。随即,通过采集“一带一路”沿线63个国家的数据开展实证分析,验证所提出方法的有效性,并对结果进行分析与讨论。研究结果能够为海外投资区位选择提供必要的决策支持,也能够对我国企业海外投资风险防范、推动共建“一带一路”高质量发展有所裨益。