突发公共事件应急信息系统框架与功能

突发公共事件信息具有海量、分散、多样和复杂等特性。现代化管理信息系统的信息采集、存储、分析和管理功能可以提高突发公共事件的处理效率和决策水平。本文探讨了国内外基于数据挖掘和智能知识的突发公共事件信息管理研究情况及如何建立一个系统的公共事件信息管理框架,为突发公共事件的变化趋势进行预测和事件信息收集、处理、知识获取、事件评估、决策和应急管理工作提供科学依据和支持。     

企业项目管理中的企业文化

企业价值目标是企业文化的核心,对其它文化元素具有决定性的影响,。现代项目管理发展成为一系列的工作价值观和一种独特的、与传统管理有显著区别的管理哲学。企业项目管理是根植于企业文化的土壤之中的,企业文化为企业项目管理提供软环境,是企业项目管理的重要支柱。     

Bob Hayes: Forty Years of Leading Operations Management Into Uncharted Waters

The enormous contributions of Bob Hayes to Operations Management (OM) are reviewed. His early work made innovative contributions to probability theory and utility estimation that enabled existing Operations Research theory to be applicable to real problems. Later, inspired by field trips to Japanese and German manufacturers, he joined Kim Clark to conduct an ambitious study of 12 plants in three companies, establishing the impact on productivity of factors such as reject rate, work‐in‐process, and production rate variation. In the 1980s, when adoption of advanced manufacturing automation was in vogue, Bob joined Jai Jaikumar to offer a caution, that new manufacturing technologies required new ways of managing and that advanced technology coupled with obsolete management would produce poorer, not better, results. Perhaps Bob's greatest contribution was to raise OM to a more strategic level. With numerous coauthors, notably Steve Wheelwright, he provided a framework for corporate and manufacturing strategy and showed how to achieve alignment between the two, particularly in the choice of production processes. Recent papers articulated a vision for OM in which a focus on the issues of operating managers provides a consistent framework, but enables our research agenda to evolve as the world's economy changes.     

基于QFD矩阵的竞争战略决策框架

借助于全面质量管理理论中的QFD矩阵,本文提出了一种顾客导向的竞争战略决策框架,它有效地将内部竞争能力与外部竞争要素放在一起进行分析与决策。文中还进一步探讨了其中的竞争要素的特征以及竞争冲击曲线理论,为有效地作出竞争战略决策提供了基础。     

Risk Decision Making in Operational Safety Management– Experience from the Nordic Benchmark Study

Technical Research Centre of Finland (VTT) and Studsvik AB, Sweden, have simulated decision making of the Swedish Nuclear Power Inspectorate and a power company by applying decision models in a benchmark study. Based on the experience from the benchmark study, a decision analysis framework to be used in safety related problems is outlined. By this framework both the power companies and the safety authorities could be provided with a more rigorous, systematic approach in their decision making. A decision analytic approach provides a structure for identifying the information requirements of the problem solving. Thus it could serve as a discussion forum between the authorities and the utilities. In this context, probabilistic safety assessment (PSA) has a crucial role of expressing the plant safety status in terms of reactor core damage accident probability and of risk contributions from various accident precursors. However, a decision under uncertainty should not be based solely on probabilities, particularly when the event in question is a rare one and its probability of occurrence is estimated by means of different kinds of approximations.     

PROGRESS IN OPERATIONS MANAGEMENT

In this issue, Robert Hayes has written provocatively about the implications of the digital economy for Operations Management (om). Here I examine and illustrate a simple four‐stage framework for thinking about these implications: advances in digital technology (stage 1) lead to business developments (stage 2), which impact the views of OM‐relevant thought leaders (stage 3), which influence the conduct of OM activities (stage 4). This framework provides a perspective for viewing the evolution of OM and indicates how educators, researchers, and practitioners can steal the march on mainstream thinking.     

Performance Evaluation of Enterprise Knowledge Management based on Multiple Attribute Group Decision Making

Given that the classical performance evaluation models can not deal with the group decision making problems since they simply average the index, we propose an enterprise knowledge management evaluation model based on multiple attribute group decision making (MAGDM). Find the differences between Ordered Weighted Averaging (OWA) and meth- ods for uncertain decision making. Also, analyze the multiple attribute group decision making process and implement the al. gorithm. Finally, apply the method on performance evaluation of four enterprises and make sensitivity analysis towards the evaluation results.     

A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

A Diagnostic Tree for Improving Production Line Performance

Improving performance of production systems is a critical but often unstructured activity. To help managers convert ad hoc or trial & error improvement efforts into efficient and systematic reviews, we develop a diagnostic tree which decomposes a performance improvement objective into successively more concrete sub‐objectives and finally into potential improvement strategies. Based on principles from the Operations Management literature, this tree is structured to enable a non‐specialist to better understand the links between corrective actions and performance. It also provides an important foundation for a principles‐based knowledge management system that couples the decision tree with a search engine for locating relevant documents within an intranet.     

LOGISTICS MANAGEMENT SYSTEM: AN ADVANCED DECISION SUPPORT SYSTEM FOR THE FOURTH DECISION TIER DISPATCH OR SHORT-INTERVAL SCHEDULING

The Logistics Management System (LMS) is a real-time transaction-based system combining decision technologies from AI, MS/OR, and decision support system that serves very successfully as a dispatcher or short-interval scheduler by monitoring and controlling the manufacturing flow of IBM's semiconductor facility near Burlington, Vermont. LMS coordinates the actions and decisions of several logically isolated participants in a serially dependent system of activities. Therefore, it balances the requirements of several goals (cycle time, output, serviceability, and inventory management) that compete for the same resource, exploits emerging opportunities on the manufacturing floor, and reduces the distortion from unplanned events. This paper provides an overview of the LMS application, the concept of interrelated decision tiers in manufacturing decision making, and the need for the dispatch decision tier to successfully reduce apparent randomness. Historically, production and operations management has ignored this decision tier. This has significantly limited our ability to make an impact on the performance of the manufacturing operation.     

Operations Risk Management: Overview of Paul Kleindorfer's Contributions

This paper reviews Paul Kleindorfer's contributions to Operations Management (OM), with a special focus on his research on risk management. An annotated bibliography of selected other contributions reviews the breadth of topics that have occupied Kleindorfer's research attention over his now 45 + years of research. These include optimal control theory, scheduling theory, decision sciences, investment planning and peak load pricing, plus a number of important applications in network industries and insurance. In the area of operations risk management, we review recent work that Kleindorfer and his colleagues in the Wharton Risk Center have undertaken on environmental management and operations, focusing on process safety and environmental risks in the chemical industry. This work is directly related to Kleindorfer's work in the broader area of “sustainable operations”, which he, Kal Singhal and Luk Van Wassenhove recently surveyed as part of the new initiative at POMS to encompass sustainable management practices within the POMS community. Continuing in the area of supply chain risks, the paper reviews Kleindorfer's contributions to the development of an integrated framework for contracting and risk hedging for supply management. The emphasis on alignment of pricing, performance and risk management in this framework is presaged in the work undertaken by Kleindorfer and his co‐authors in the 1980s on after‐sales support services for high‐technology products. This work on supply chain risk, and its successors, is reviewed here in light of its growing importance in managing the unbundled and global supply chains characteristic of the new economy.     

A Decisional Framework of Offshoring: Integrating Insights from 25 Years of Research to Provide Direction for Future*

Mirroring the growing trend for firms to support their operations by locating activities abroad, research on the practice of offshoring has increased considerably in recent years. However, despite the mounting research, understanding of the key factors influencing decision‐making for offshoring remains surprisingly limited due to fragmentation. In this study, we synthesize and integrate insights from different research domains in order to develop a comprehensive decisional framework for key offshoring decisions. The integrative decisional framework is based on a systematic review of offshoring research published in the most influential management and business journals in the past 25 years. In addition to providing a snapshot of the state of research on decision‐making for offshoring, this study aims to stimulate future research by identifying promising research opportunities. In particular, we propose that future research should use alternative theories to incorporate overlooked aspects of decision‐making, integrate different theories to account for the interdependencies between decisions, and adopt a portfolio perspective that considers each decision as part of an overall offshoring strategy.     

The evolution of Omega-The International Journal of Management Science over the past 40 years: A bibliometric overview

The aim of this study is to reflect on where Omega-The International Journal of Management Science (Omega) comes from and evaluate where Omega currently is in a field that arguably paves the way for the challenging task of identifying future directions and trends. To achieve this goal, this study presents a bibliometric overview of Omega over the past 40 years, from 1979 to 2018. First, this study outlines the basic bibliometric profile of Omega. Second, VOSviewer is employed to visualize the collaborative relationships that Omega has with other journals to show how Omega has linked to them over the past 40 years. Finally, co-occurrences of keywords are analysed and strategic diagrams are built to reveal the thematic trends in Omega. The results show that Omega has substantial influences in the field of operational research and management science (OR&MS), with contributors from all over the world. Omega has strong connections with other leading journals in the fields of OR&MS and engineering. The mainstream research of Omega mainly focuses on production and manufacturing management, mathematical modelling for optimization problems, supply chain management, data envelopment analysis, multicriteria and decision making. Moreover, motor themes, highly developed-and-isolated themes, emerging-or-declining themes, and basic-and-transversal themes are identified. The study provides strong evidence that Omega has been a story of success, and the findings of this study will support decision making for the Omega-related scientific community.     

Collective Intuition: Implications for Improved Decision Making and Organizational Learning

This article establishes the foundation for research on collective intuition through a study of decision making and organizational learning processes in police senior management teams. We conceptualize collective intuition as independently formed judgement based on domain‐specific knowledge, experience and cognitive ability, shared and interpreted collectively. We contribute to intuition research, which has tended to focus its attention at the individual level, by studying intuition collectively in team settings. From a dual‐process perspective, we investigate how expert intuition and deliberation affect decision making and learning at various levels of the organization. Furthermore, we contribute to organizational learning research by offering an empirically derived elaboration of the foundational 4I framework, identifying additional ‘feed‐forward’ and ‘feedback’ loop processes, and thereby providing a more complete account of this organizational learning model. Bridging a variety of relevant but previously unconnected literatures via our focal concept of collective intuition, our research provides a foundation for future studies of this vitally important but under‐researched organizational phenomenon. We offer theoretical and practical implications whereby expert intuitions can be developed and leveraged collectively as valuable sources of organizational knowledge and learning, and contribute to improved decision making in organizations.     

A Flexible Evaluative Framework for Order Picking Systems

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.     

An Analysis of the Pedagogical Effects of Expert System Use in the Classroom

An experiment that examines the use of expert systems to enhance student learning in an introductory Production and Operations Management (POM) class is discussed. An experimental expert system, SCHEDULER, was built within the domain of scheduling. It provided students with the opportunity to experience working with and integrating scheduling heuristics, constraints, and management requirements. The experiment also exposed students to using, rather than having to build, computerized decision aids, and expert systems in particular. Measurements of student learning and assessments of student perception regarding use of expert systems indicate expert systems can provide a valuable enhancement to the learning environment.     

Systems Resilience for Multihazard Environments: Definition,Metrics, and Valuation for Decision Making

The United Nations Office for Disaster Risk Reduction reported that the 2011 natural disasters, including the earthquake and tsunami that struck Japan, resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide in 2011, with earthquakes producing the greatest number of fatalities. Average annual losses in the United States amount to about $55 billion. Enhancing community and system resilience could lead to massive savings through risk reduction and expeditious recovery. The rational management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics. In this article, a resilience definition is provided that meets a set of requirements with clear relationships to the metrics of the relevant abstract notions of reliability and risk. Those metrics also meet logically consistent requirements drawn from measure theory, and provide a sound basis for the development of effective decision‐making tools for multihazard environments. Improving the resiliency of a system to meet target levels requires the examination of system enhancement alternatives in economic terms, within a decision‐making framework. Relevant decision analysis methods would typically require the examination of resilience based on its valuation by society at large. The article provides methods for valuation and benefit‐cost analysis based on concepts from risk analysis and management.     

泛综合评价信息集成框架求解算法及应用

针对多信息来源、多数据结构的复杂评价问题,对传统评价模式进行拓展并提出了泛综合评价的方法。泛综合评价理论主要为复杂评价信息的整合及求解提供支撑,具体而言主要采用构建信息融合框架的方式对不同类别与结构的多源信息进行整合,并通过随机模拟仿真的方法对信息融合框架的求解算法进行了探讨。由于信息融合框架中包含信息的复杂性增加了框架的求解成本,因而进一步分析了信息集成框架的简化求解算法,并通过算例的方式对信息集成框架简化求解算法的有效性进行了验证。简化求解算法的研究提升了泛综合评价在实际应用中的可操作性。"区域发展绩效的参与式评价"算例的构建,说明泛综合评价的理论为不同利益主体之间的民主决策提供了可能。本文的研究可为大数据背景下群体智慧的挖掘、民主决策的结果分析等实际应用问题提供理论和技术支撑。