A framework to evaluate methods' capacity to design flexible business processes

International audienceThe assumption done in this paper is that changing processes require specific methods for their design. The decision of adopting a method for modeling flexible processes depends on many criterions and situations. Accordingly, we propose a framework with a list of criterions. The user can use it as a decision support framework for the choice of a modeling method. We used two enterprise modeling approaches to illustrate the proposed framework

Developing a semantic web-based distributed model management system: Experiences and lessons learned

Distributed model management systems (DMMSs) are decision support systems with a focus on managing decision models throughout the modeling lifecycle and across the extended enterprise. The advent and proliferation of web services and semantic web technologies offers the possibilities of sharing and reusing models in a distributed setting. This paper presents the design and implementation of a semantic web-based DMMS. Key lessons learned, technical and organizational issues encountered are summarized and directions for future research have been outlined. From a technical perspective, future research will need to explore the viability of tools specifically designed to facilitate the semantic annotation of models, specify and validate SA-SMML, and extend the white-box approach presented in this paper to other model types not amenable to structured modeling. From an organizational perspective, further research is needed in the areas of adoption issues and business models that would ensure the sustainable support for of such systems in the service enterprise

Decision Support Tools for Cloud Migration in the Enterprise

This paper describes two tools that aim to support decision making during the migration of IT systems to the cloud. The first is a modeling tool that produces cost estimates of using public IaaS clouds. The tool enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. The tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios. The second tool is a spreadsheet that outlines the benefits and risks of using IaaS clouds from an enterprise perspective; this tool provides a starting point for risk assessment. Two case studies were used to evaluate the tools. The tools were useful as they informed decision makers about the costs, benefits and risks of using the cloud.Comment: To appear in IEEE CLOUD 201

Информационное моделирование системы поддержки принятия решений в управлении развитием ресурсного потенциала предприятия

Розглянуто основи інформаційного моделювання системи підтримки прийняття рішень в управлінні розвитком ресурсного потенціалу підприємства на основі використання системно-динамічної моделі прийняття рішень. Ключові слова: моделювання, ресурсний потенціал, управління розвитком, системи під-тримки прийняття рішень.Рассмотрены основы информационного моделирования системы поддержки принятия решений в управлении развитием ресурсного потенциала предприятия на основе использования системно-динамической модели принятия решений и предложены ее основные элементы. Ключевые слова: моделирование, ресурсный потенциал, управление развитием, системы поддержки принятия решений.The basic aspects of information modeling of the decision support system in management of development of enterprise resource potential based on the use of system-dynamic model of decision making are discussed and the main elements of such a model are proposed. Keywords: modeling, resource potential, management of development, decision support systems

A framework for hierarchical, object-oriented simulation modeling of a steel manufacturing enterprise

This is the final report of a two-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The objective of the project is to combine detailed physical models of industrial processes with unit operations and business-level models. This would allow global and individual process control schemes to be implemented that would facilitate improved overall system performance. Intelligent decision support that employs expert system concepts (knowledge base and rules) could then also be incorporated. This project is innovative because it attempts to incorporate all levels of production-related activities from atoms to enterprise, and to integrate those activities into one comprehensive decision support tool. This project is an interdisciplinary effort requiring enterprise modeling and simulation model integration; process modeling and control; process control and optimization; chemical process modeling; and detailed molecular-level models. It represents the state of the art in enterprise modeling and simulation and incorporates cutting edge process modeling, process control, and system optimization techniques

MODELING OF AGRICULTURAL SYSTEMS

The authors present an overview of agricultural systems models. Beginning with why systems are modeled and for what purposes, the paper examines types of agricultural systems and associated model types. The broad categories range from pictorial (iconic) models to descriptive analogue models to symbolic (usually mathematical) models. The uses of optimization versus non-optimizing mechanistic models are reviewed, as are the scale and aggregation challenges associated with scaling up from the plant cell to the landscape or from a farm enterprise to a world market supply-demand equilibrium Recent modeling developments include the integration of formerly stand-alone biophysical simulation models, increasingly with a unifying spatial database and often for the purpose of supporting management decisions. Current modeling innovations are estimating and incorporating environmental values and other system interactions. At the community and regional scale, sociological and economic models of rural community structure are being developed to evaluate long-term community viability. The information revolution is bringing new challenges in delivering agricultural systems models over the internet, as well as integrating decision support systems with the new precision agriculture technologies.Farm Management,

Decision Support for Mission-Centric Cyber Defence

In this paper, we propose a novel approach to enterprise mission modeling and mission-centric decision support for cybersecurity operations. The goal of the decision support analytical process is to suggest an effective response for an ongoing attack endangering established mission security requirements. First, we propose an enterprise mission decomposition model to represent the requirements of the missions' processes and components on their confidentiality, integrity, availability. The model is illustrated in a real-world scenario of a medical information system. Second, we propose an analytical process that calculates mission resilience metrics using the attack graphs and Bayesian network reasoning. The process is designed to help cybersecurity operations teams in understanding the complexity of a situation and decision making concerning requirements on enterprise missions

Human error control in the collaborative workflow modeling tool based on GEMS model

Business process should support the execution of collaboration process with agility and flexibility through the integration of enterprise inner or outer application and human resources from the collaborative workflow view.Although the dependency of enterprise activities to the automated system has been increasing, human role is as important as ever.In the workflow modelling this human role is emphasized and the structure to control human error by analysing decision-making itself is needed.Also, through the collaboration of activities agile and effective communication should be constructed, eventually by the combination and coordination of activities to the aimed process the product quality should be improved.This paper classifies human errors can be occurred in collaborative workflow by applying GEMS(Generic Error Modelling System) to control them, and suggests human error control method through hybrid based modelling as well.On this base collaborative workflow modeling tool is designed and implemented. Using this modelling methodology it is possible to workflow modeling could be supported considering human characteristics has a tendency of human error to be controlled