Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning

Post-seismic inventory and logistics planning under incomplete and fuzzy information is an important yet understudied area in supply chain risk management. The goal of this paper is to propose a system dynamics model to analyze the behaviors of disrupted disaster relief supply chain by simulating the uncertainties associated with predicting post-seismic road network and delayed information. The simulation results indicate: (1) information delay has different influences over the relief head-quarter (the upstream) and the disaster-affected areas (the downstream); and (2) the change of road conditions and shipment schedules have impact on the on-time transportation rate in supply chain management. Furthermore, this paper defined and tested supplies' replenishment solutions combined with three inventory planning strategies and four forecasting methods under different lead time uncertainties. The results show that: (1) a strategy that considers information from both the post-seismic management center and the affected areas can provide a better logistic plan than an one takes information from one side; (2) the smooth-the-trend forecasting method is suitable for inventory and logistic planning when the post-seismic situations are volatile, while the quick-response forecasting method has good performance in stable environments. In addition, this paper proposes decision tree to help decision makers choose the appropriate stocking strategies.     

A hybrid statistical genetic-based demand forecasting expert system

Demand forecasting is considered a key factor for balancing risk of over-stocking and out-of-stock. It is the main input to supply chain processes affecting their performance. Even with much effort and funds spent to improve supply chain processes, they still lack reliability and efficiency if the demand forecast accuracy is poor. This paper presents a proposal of an integrated model of statistical methods and improved genetic algorithm to generate better demand forecast accuracy. An improved genetic algorithm is used to choose the best weights among the statistical methods and to optimize the forecasted activities combinations that maximize profit. A case study is presented using different product types. And, a comparison is conducted between results obtained from the proposed model and from traditional statistical methods, which demonstrates improved forecast accuracy using the proposed model for all time series types.     

A simulation-based risk network model for decision support in project risk management

This paper presents a decision support system (DSS) for the modeling and management of project risks and risk interactions. This is a crucial activity in project management, as projects are facing a growing complexity with higher uncertainties and tighter constraints. Existing classical methods have limitations for modeling the complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This will influence the effectiveness of decisions for risk response planning and will lead to unexpected and undesired behavior in the project. Based on the concepts of DSS and the classical steps of project risk management, we develop an integrated DSS framework including the identification, assessment and analysis of the risk network. In the network, the nodes are the risks and the edges represent the cause and effect potential interactions between risks. The proposed simulation-based model makes it possible to re-evaluate risks and their priorities, to suggest and test mitigation actions, and then to support project manager in making decisions regarding risk response actions. An example of application is provided to illustrate the utility of the model.     

A computer-based system for risk management support

The scale and complexity of present day industrial operations involving hazardous substances are such that managers are faced with increasingly demanding decision problems. They must simultaneously consider technological, economic, environmental and sociopolitical factors. As a response to this problem a computer based decision support system is being developed to support risk management activities, with special emphasis given to hazardous chemicals. The IRIMS (Ispra Risk Management Support) system is an attempt to integrate a number of data bases, containing information relevant to risk management, with several existing simulation models which can be used to address problems of environmental assessment, risk analysis and system optimisation. The system is designed to be user friendly and results are displayed through high resolution colour graphics allowing the non specialised user to obtain a “feel” for the problem under investigation. The paper describes the current prototype system, which is geared to handle problems on a European scale, and plans for further developments which will allow more detailed studies on particular countries or regions.     

在供应链决策中的多阶生产规划支持模式研究

以液晶显示器(TFT-LCD)制造业为例，对供应链中多阶生产规划问题进行了研究。运用混合整数线性规划，以企业整体获利最大为目标，考虑材料成本随时间的变化以及库存对资金的占用和市场需求量、需求价格的变动，对工厂生产做出安排，给出不同时段的库存状态，由此开发TFT-LCD产业多阶生产规划决策支持系统，为生产安排提供决策依据。通过在TFT-LCD厂的应用，证明了该系统的实 用性。     

生产计划问题的决策模型研究

生产决策问题一直以来都是一个重要的决策问题。它涉及到营销策略、市场预测、风险分析,以及管理经验等方面的知识。文章结合数据仓库和联机分析技术,提出了一种生产计划问题的决策模型,以求提供一个生产计划问题的决策支持解决方案。文中首先分析了生产计划问题的决策模式,在此基础上提出了生产计划问题的决策模型,并讨论了模型的建立方法,以及求解模型过程中所涉及的算法等问题。     

The functions of a decision support system

The growing literature on decision support systems outlines their principal characteristics and presents case studies of successful systems. This paper reviews the literature, describes six functions that a decision support system may perform for a manager or staff analyst, and introduces a new technique, functional mapping, for representing these systems.     

Production management as a constraint satisfaction problem

Production management problems can be quite straightforwardly presented as constraint satisfaction problems, where values for some variables are searched for under a set of constraints. A combination of an operation and a resource is usually interpreted as the variable, and a time window is usually interpreted as the value to be searched for. This convention is challenged. A case is considered where the most appropriate interpretation treats the combination of a resource and a time window as the variable, and an operation as the value. A third possible interpretation is also briefly covered, where the combination of an operation and a time window is the variable, and the resource is the value.     

电力系统信息化建设中的风险管理探究

随着社会的进步,电网企业信息化建设亦是日新月异,信息系统在电网企业的应用日趋广泛,既有实时控制又有流程管理,既有数据管理又有决策支持。基于此,本文探讨了电力系统信息化建设中的风险管理,提高电力系统信息化建设水平。     

Quantification of cost and risk during product development

In response to customers’ rising demands for more customization and quality, companies are making more frequent changes to their products. A framework for a decision support system (DSS) which helps product development managers to understand the cost and risk of change is described and illustrated with a simple example of a thermoflask. The DSS assesses project performance metrics, such as development effort, development time, product cost and revenue, customer satisfaction, profit margin, and risk. The system allows the recalculation of these performance metrics when engineering change occurs during the creation of new design solutions. The assessment of different design solutions can then be performed by comparing the change in performance metrics. The DSS integrates methods from quality function deployment, functional analysis, and risk assessment that increases product knowledge during design stages in order to calculate the effects of engineering change, and thus, to support design managers in decision making.     

GBOM-oriented management of production disruption risk and optimization of supply chain construction

A generic bill-of-materials (GBOM) describes demand for materials and their proportional relations to a family of products. Supply chain constructed from the perspective of the GBOM is able to respond swiftly to market demand and lean production can be achieved by managing the total cost of supply chain effectively. Based on the GBOM, this paper examines the control of production disruption risk related to supply chain and investigates the uncertainty of production in supply chain enterprises for the purpose of achieving optimal profits in supply chain. As the production disruption risk is controlled at a certain level, the selection model of supply chain partners, which is specific and more feasible, can be constructed. A combination of random simulation and neural network is deployed to approximate uncertain function, and genetic algorithm and simulated annealing arithmetic are also used to approximately achieve the optimal scheme of supply chain construction in the context of uncertainty.     

A decision support methodology with risk assessment on prediction of terrorism insurgency distribution range radius and elapsing time: An empirical case study in Thailand

The objectives of this research are to design and develop a practical decision support methodology with efficient prediction tool and risk assessment analysis of a terrorism insurgency situation. The proposed methodology consists of two main parts as: (1) the prediction modelling and (2) risk assessment analysis. The Improvised Explosive Device (IED) incidents from 2007 to 2013 in the capital district of Yala province, the southern part of Thailand, are collected and generated to the methodology as a case study implementation. The proposed methodology is capable of indicating and illustrating the risk assessment prediction results of terrorism insurgency incidents. Furthermore, the demonstration of the Explosive Ordnance Disposal Mobile Unit (EODMU) based upon a Risk Assessment Radar Chart is investigated. In practical terms of applying the proposed methodology, the Thai Government can concentrate on a critical operation zone under a Risk Assessment Radar Chart, resulting in a more accurate operation and leading to a much lower number of casualties.     

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.     

Modeling and analysis of the effects of QoS and reliability on pricing, profitability, and risk management in multiperiod grid-computing networks

In this paper we develop a network equilibrium model for optimal pricing and resource allocation in Computational Grid Network. We consider a general network economy model with Grid Resource Providers, Grid Resource Brokers and Grid Users. The proposed framework allows for the modeling and theoretical analysis of Computational Grid Markets that considers a non-cooperative behavior of decision-makers in the same tier of the grid computing network (such as, for example, Grid Resource Providers) as well as cooperative behavior between tiers (between Resource Providers and Grid Brokers). We introduce risk management into the decision making process by analyzing the decision-marker's reliability and quality of service (QoS) requirement. We analyze resource allocation patterns as well as equilibrium price based on demand, supply, and cost structure of the grid computing market network. We specifically answer the following questions with several numerical examples: How do system reliability levels affect the QoS levels of the service providers and brokers under competition? How do system reliability levels affect the profits of resource providers and brokers in a competitive market? How do system reliability levels influence the pricing of the services in a competitive environment? How do users' service request types, QoS requirements, and timing concerns affect users' behaviors, costs and risks in equilibrium? How does the market mechanism allocate resources to satisfy the demands of users? We find that for users who request same services certain timing flexibility can not only reduce the costs but also lower the risks. The results indicated that the value of QoS can be efficiently priced based on the heterogeneous service demands.     

Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach

As sea ports and terminals are valuable assets, in today’s uncertain and complex environment further refinements are needed to assess risks and prioritise protective measures for these critical pieces of logistics infrastructure. The major problem that port professionals (e.g. port risk managers and port auditors) are facing is the lack of an appropriate methodology and evaluation techniques to support their risk management (RM) cycle. Therefore in response to the uncertainties and to provide continuous risk control assurance in port industry, this paper uses fuzzy set theory (FST) to describe and evaluate the associated risk factors within the ports and terminals operations and management (PTOM). An evidential reasoning (ER) approach is employed to synthesise the information produced. These processes constitute a decision support framework that will be used to conduct port-to-port risk evaluations or to assess a whole port’s and terminal’s overall risk level in order to facilitate continuous improvement strategies. The proposed framework along with a generic methodology and a risk evaluation model is tested by a case study. The case study analyses pieces of three Southern Iranian ports by using an illustrative operational risk hierarchy. The sensitivity analysis carried out in this paper prove pieces of the applicability of the proposed methodology and model for risk evaluation of the sea ports and terminals in real situations.     

A note on “fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain”

Liang (2008) [Liang, T. -F. (2008). A note on “fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain”. Computers & Industrial Engineering, 55, 676–694] proposed a production/distribution planning model and its solution approach in fuzzy environment. However, his mathematical model does not use backordering option. The main purpose of this paper is to demonstrate this handicap and propose a valid constraint.     

A web-based screening model for climate risk to water supply systems in the northeastern United States

The aim of this study is to describe the development and application of a web-based decision support tool (ViRTUE) for performing climate risk evaluations of water supply systems. The tool is designed for small-scale water utilities in the northeastern United States that may lack the resources for detailed climate change risk investigations. Development of this tool demonstrates a relatively new approach to web application development using the Shiny framework for the R programming language to create an interactive environment for stakeholders and water managers to explore climate vulnerabilities. Using a decision-scaling framework, the tool allows the user to perform a climate stress test to evaluate the performance and vulnerability to water supply shortfalls of local reservoir systems over a wide range of potential climate change scenarios using a generic systems model. Probabilities of future climate conditions derived from climate projections then help inform utility operators of impending risk.     

Team performance and information system implementation

Although the Earned Value Method (EVM), a multi-dimensional project control system, has been widely used since 1960, there has been little research focused specifically on its application in IS implementation. The management of IS projects—including the accurate forecasting of project duration—is complicated by the strong impact of the learning curve on the effectiveness of EVM. This study makes new contributions by advancing formulas for the accurate prediction of project duration, and by developing a decision support model in which the learning curve is fully integrated with EVM, and performance changes due to learning are isolated from other factors affecting project duration. The study makes three contributions to the understanding and use of EVM in IS implementation: (1) It provides an extended version of EVM, (2) it provides an illustration of the application of EVM to a real project situation based on empirical data from an ERP implementation project, and (3) it provides a theoretical basis for empirical studies of IS project control techniques. The decision support model can be used to determine both the learning curve coefficient and the project completion date during the early stages of a project, and it thus offers a significant practical contribution to the management of IS projects.

An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.     

Assessing water quality management options in the Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system

The widespread and relentless discharge of untreated wastewater into the Upper Litani Basin (ULB) river system in Lebanon has reached staggering levels rendering its water unfit for most uses especially during the drier times of the year. Despite the call by governmental and non-governmental agencies to develop several wastewater treatment plants and sewage networks in an effort to control this problem, these efforts do not seem to be coordinated or based on comprehensive and integrated assessments of current and projected conditions in the basin.This paper provides an overview of the development and implementation of an integrated decision support system (DSS) designed to help policy makers and other stakeholders have a clearer understanding of the key factors and processes involved in the sewage induced degradation of surface water quality in the ULB, and formulate, assess and evaluate alternative management plans. The DSS is developed based on the WEAP model, which provides a GIS based and visual simulation environment and scenario management and analysis capabilities. The DSS was used to assess two main water quality management plans taking into consideration hydrological, spatial and seasonal variabilities. An incremental cost-effectiveness analysis was conducted to identify best buy plans. The results have confirmed the gravity of this problem and demonstrated the importance of taking immediate action on curbing this onslaught on this valuable and scarce fresh water resource.