scholarly journals A Case Study of a Complex System with the Decimas Framework

Eidos ◽  
1970 ◽  
pp. 24
Author(s):  
Marina V Sokolova ◽  
Antonio Fernández-Caballero ◽  
Francisco J Gómez
Keyword(s):  
Complex System ◽  
Data Mining ◽  
Information System ◽  
Decision Support ◽  
Support System ◽  
Decision Support Tools ◽  
Agent Based ◽  
Support Tools ◽  
Mining Methods

This paper presents the DeciMaS framework , which supports the vital stages of information system creation. The case study for the DeciMaS framework is also presented. For this reason an agent-based decision support system (ADSS) is created and described in detail. We discuss the structure and the data mining methods of the designed ADSS. The intelligent ADSS described here provides a platform for integration of related knowledge coming from external heterogeneous sources, and supports its transformation into an understandable set of models and analytical dependencies, with the global aim of assisting a manager with a set of decision support tools.

Decision Support Tools for Managing Unpredictability in the Supply Chain

2000 ◽  
Author(s):  
Carol Vesier
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Production Scheduling ◽  
Capital Investment ◽  
Investment Strategy ◽  
Decision Support Tools ◽  
Business Outcomes ◽  
Support Tools

Abstract Effectively managing unpredictability requires decision support tools that can predict the financial and business outcomes of various supply chain strategies. This paper will discuss the role of these decision support tools and their characteristics as well as review a case study. In the case study, decision support tools facilitated development of strategies that increased after tax profit by $140 Million. These strategies included: • Reliability improvement strategy: Identifying the reliability improvements that offered the biggest profitability impact. • Supply chain strategy: Defining inventory management and production scheduling rules that ensured order shipment within two days. • Capital investment strategy: Defining when new capacity should come on line as well as the minimum capital investment.

Assessing the Performance of Decision Support Systems in Military Environment

2015 ◽  
pp. 45-70 ◽  
Author(s):  
Jean-Fabrice Lebraty ◽  
Cécile Godé
Keyword(s):  
Decision Making ◽  
Information System ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Extreme Environment ◽  
Is Success ◽  
Original Case ◽  
And Performance

This article explores the ability of a decision support system (DSS) to improve the quality of decision making in extreme environment. This DSS is actually based on a networked information system. Academic literature commonly mentions models of fit to explore the relationship between technology and performance, reckoning users' evaluations as a relevant measurement technique for Information System (IS) success. Although effective contributions have been achieved in measurement and exploration of fit, there have been few attempts to investigate the triangulation of fit between “Task-DSS-Decision Maker” under stressful and uncertain circumstances. This article provides new insights regarding the advantages provided by networked IS for making relevant decisions. An original case study has been conducted. It is focused on a networked decision support system called Link 16 that is used during aerial missions. This case study shows that the system improves decision making on an individual basis. Our result suggest the importance of three main fit criteria – Compliance, Complementarity and Conformity – to measure DSS performance under extreme environment and display a preliminary decisional fit model.

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 929-948
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

Research on Decision Support System Based on EDM-PIR Model in it Governance

2012 ◽  
Vol 263-266 ◽  
pp. 3238-3243 ◽  
Author(s):  
Shao Hua Zhang ◽  
Shuang Yang ◽  
Jun Dian Song ◽  
Jun Ling Qian
Keyword(s):  
Information System ◽  
Decision Support ◽  
Support System ◽  
Index System ◽  
It Governance ◽  
Three Stages ◽  
Governance Models ◽  
Scientific Decision ◽  
It Risk

The issue how to make scientific decision to avoid IT risk is one of the hot topics recently. IT governance models have been proposed are so general those they are unable to provide actual guidance for implementing informatization. The paper proposes a new model based on Evaluate-Direct-Monitor (EDM) model of ISO/IEC 38500 standard. An index system and prototype for decision support are proposed with the study of three stages in information system lifecycle. As a case study, an instantiated decision support index system is developed combining with specific application requirement.

scholarly journals Exploring Public Sector Managers’ Motivations in Deploying Decision Support Tools to the Street Level

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Chris McIntyre
Keyword(s):  
Decision Support ◽  
Public Sector ◽  
Digital Tools ◽  
Decision Support Tools ◽  
Structured Interviews ◽  
Senior Managers ◽  
Street Level ◽  
Support Tools ◽  
Public Sector Managers

This article explores the motivations of public sector managers in developing and deploying digital tools to support decision making at the front lines of public service delivery. Two digital decision support tools created by New Zealand’s Ministry of Social Development are presented as a case study, drawing primarily on semi-structured interviews with senior managers. Results provide empirical evidence that public sector managers deploy digital tools not to curtail, but to support street-level bureaucrats’ discretion. Managers appear to be motivated not by increased control over front-line staff, but, rather, by improving clients’ experience of the system and decreasing longterm service costs.

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 359-378 ◽  
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

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