Understanding Data-Driven Decision Support Systems

It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.     

Understanding Data-Driven Decision Support Systems

Abstract

It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.     

An effective database formation for decision support systems

Managers' tasks have two aspects: to monitor (control) business activities and to plan the future based upon the monitored results. Thus a decision Support System (DSS) must have two kinds of databases appropriate for its needs.A management database, i.e., one for monitoring activities, is constructed mainly from the existing operational databases. A data cube should be employed for the logical data structure of the management database so that managers can share it and access it in multiple ways.Planning databases, i.e., those for planning activities, are constructed mostly from the management database. A table form should be employed for its logical data structure so that managers will find it easy to use.The management and planning databases should be connected through DSS's system architecture [1]. This makes the operational data (indicating business activities) directly and immediately available for management decision making.We have previously presented the DSS architecture. This paper discusses the management and planning databases from the viewpoint of it.     

DSS应用中一种跨数据库平台的数据抽取技术

DSS开发过程中,最关键的是数据抽取技术,如何有效地从已有的数据库中提取所需的信息非常重要,本针对DSS应用开发的特点,采用面向对象的方法,设计了一种动态链接库,解决了跨数据库平台的数据抽取问题。     

An approach for an integrated DSS for strategic planning

Although Decision Support Systems (DSS) have become widespread in recent years for operational control their use in strategic decision-making has only rarely been seen. This study investigates how DSS technology can be applied in the process of strategic planning. The requirements of Strategic Decision Support Systems (SDSS) are discussed and a conceptual frame for the construction of SDSS is developed. The authors emphasize the integration of both the planning instruments and the corresponding data flows. They present the StratConsult system - a PC-based prototype for supporting strategic sessions. Benefits and drawbacks of SDSS are explored and relevant trends for integrated computer-aided strategic DSS are outlined.     

DSS research and practice in perspective

The aim of this paper is to assess the state-of-the-art in the Decision Support Systems (DSS) field from both a research and a practice perspective. Three main dimensions of DSS research and practice are addressed: 1) supporting human decision-making processes, 2) integrating DSS into the organizational context, and 3) identifying new application domains. The related analysis and discussion provides a better understanding of past developments in the DSS field and insights into future evolution patterns.     

A DSS For Modelling Logistic Chains in Freight Transport Policy Analysis

As the logistics activities of firms are becoming increasingly dynamic and global in nature, private and public decision makers depend more and more on information regarding the expected development of future freight flows. A newly developed Decision Support System (DSS) called SMILE (Strategic Model for Integrated Logistic Evaluations) produces forecasts of freight flows related to the Netherlands for a large number of products and modes of transport. The underlying model describes logistics chains at three levels: production, inventory and (multimodal) transportation. The second level accounts for warehousing by using integrated cost functions comprising handling, stockholding and transport costs, using a new segmentation of goods according to their logistical requirements. By means of a graphical user interface, the DSS assists the user with designing scenarios for simulations up to 25 years ahead and visualises the impacts of policy measures on freight flows and the environment. This paper describes the theory behind the model, characteristics of the DSS and potential policy applications.     

Local Area Management and Planning (LAMP) in India

To assist decentralised planning, the Department of Science and Technology, Government of India has taken the initiative to develop a computer-based spatial information system for planning and management of the natural resources at the district and sub-district level. At present ten district level database centres called Natural Resources Data Management System (NRDMS) have been functioning in different geo-environments in India.
The present paper discusses the experience of the Centre for Applied Systems Analysis in Development (CASAD). CASAD is involved in managing the NRDMS database centre at Bankura district and was associated with the development of DSS models in Kheda district. Finally, a few case studies on the use of Decision Support Systems (DSS) for decentralised decision making at the district level are discussed.     

Adaptive decision support systems

The effectiveness of decision support systems (DSS) is enhanced through dynamic adaptation of support to the needs of the decision maker, to the problem, and to the decision context. We define this enhanced DSS as adaptive decision support systems (ADSS) and propose its architecture. In an ADSS, the decision maker controls the decision process. However, the system monitors the process to match support to the needs. The proposed architecture evolves from the traditional DSS models and includes an additional intelligent‘Adaptation’ component. The ‘Adaptation’ component workd with the data, model, and interface components to provide adaptive support. The architecture also integrates enhancements proposed in the past research. In this paper, we have illustrated the proposed architecture with two examples, a prototype system, and results from a preliminary empirical investigations     

A decision support system for electric utilities: compliance with Clean Air Act

The Clean Air Act (CAA) Amendments established an absolute national limit for sulfur dioxide (SO₂) emissions by the year 2000. The Act is based on a market-oriented system under which power plants will be granted ‘emission allowances', each of which grants the right to emit a ton of SO₂ into the atmosphere. Utilities that reduce their emissions below their allocated allowances may sell the additional allowances in what will presumably be a developing market. Utilities are free to choose how to comply with the Act, but a few generally recognized options are likely to form the basis for compliance strategies. While the options are known, the costs of implementing the options are not known with certainty. Each utility will need to determine the lowest cost, most effective strategy given its current configuration and generation requirements. The compliance strategy decision must also be presented and defended to the appropriate Public Utility Commission (PUC). This paper outlines a prototype decision support system (DSS) using an optimization engine integrated with a database to help aid utilities in making decisions regarding their compliance strategy. This DSS was created specifically with a mid-western utility company in mind; however, it is generic enough to be useful to any utility. The results obtained using the DSS are very encouraging. The decisions supported by it are consistent with those offered by experts in the industry. The DSS promises to be a very useful tool for strategic planning related to CAA compliance.     

基于互联网的区域农业产业结构调整决策支持系统

基于网络的决策支持系统技术应用于农业领域是农业信息化的一个新的有着光明前景的发展方向。介绍了一个基于网络的区域农业产业结构调整决策支持系统-AHASO(AnHuiAgriculture Structure Optimization)。详细描述了它的组成结构、主要功能、开发工具、运行环境、各模块设计思想及技术特点、优化实例,最后探讨了基于网络的区域农业产业结构调整决策支持系统的未来发展方向。     

决策支持系统在航空公司的应用

随着中国加入WTO,国内航空市场竞争愈加激烈。竞争的压力提高了航空公司对决策质量与速度的要求。而决策支持系统作为一种新兴的信息技术,能够为航空公司提供各种决策信息以及许多商业问题的解决方案,从而减轻了管理者从事低层次信息处理和分析的负担,使得他们专注于最需要决策智慧和经验的工作,因此提高了决策的质量和效率。文章分析了现代航空企业决策所遇到的挑战,指出了当前管理信息系统的不足,然后阐述了决策支持系统的概念、功能、航空公司的需求及应用,最后提出了航空公司建设决策支持系统的要点和实施建议。     

Progress in Web-based decision support technologies

World Wide Web technologies have transformed the design, development, implementation and deployment of decision support systems. This article reviews and summarizes recent technology developments, current usage of Web-based DSS, and trends in the deployment of such systems. Many firms use the Web as a medium to convey information about DSS products or to distribute DSS software. The use of Web-based computation to provide product demonstrations or to deploy DSS applications for remote access remains less common. The academic literature on Web-based DSS is largely focused on applications and implementations, and only a few articles examine architectural issues or provide design guidelines based on empirical evidence.     

Judgmental forecasting with interactive forecasting support systems

With an increasing use of DSS/EIS, managers are often required to process information coming from a variety of sources in making a final decision. However, we have little understanding of the efficiency with which people select and use the multiple pieces of information. This issue was examined under various conditions using a DSS in a forecasting task where multiple items of information were displayed on request in an interactive manner. Results indicate that overall people underacquired information. Moreover, people often selected less-reliable information. This sub-optimal behaviour did not diminish over time (it became worse). But an aggregation DSS was helpful at the task. This suggests that people seemed to have a problem in aggregating multiple pieces of information. It was also found that the independent preparation of an initial forecast improved forecast accuracy significantly. Perhaps, forecasters may prepare the initial forecast independently and use decision aids for the subsequent tasks of the forecasting process.     

The wildspace™ decision support system

A system architecture was developed for the wildspace decision support system (DSS) to provide a better understanding of complex wildlife and habitat problems. The system makes use of two key concepts, SPECIES and SPACES, to define the study domain. wildspace DSS’s flexible user interface allows users to select SPECIES through a number of different approaches, including direct selection and selection using information such as avian life history and project metadata. On the SPACES side, the system uses the raison™ object system (ROS) for mapping functions and spatial analysis. The key element in wildspace DSS is its knowledge-based database manager that provides intelligent support to various components of the system. It keeps track of all the legitimate databases, provides intelligence within the SPECIES and SPACES selection process and, more importantly, interfaces with the knowledge templates which are sets of operations implementing pre-defined analysis routines used for integrated analysis. This integrated decision support approach allows users to combine a diverse set of tools within a common framework. wildspace DSS is used to study complex wildlife problems involving multiple projects and data that are temporally and spatially heterogeneous. A case study about a relevant wildlife conservation question is presented using a series of queries and analyses performed within wildspace DSS. The system also serves as the repository for all past, current and future wildlife data collected by the Canadian Wildlife Service—Ontario Region.     

Impact of design methods on decision support systems success: An empirical assessment

Many of the Decision Support Systems (DSS) developed and implemented in the last decade or so, have not been used or are only used for mundane report generation rather than for direct support of strategic planning. These failures can be attributed to the fact that existing DSS design methods (representation-based, evolutive, and adaptive) have never been evaluated with respect to their success. Thus, the designer had no way of knowing what design method to employ in developing an effective DSS.This paper investigated the relationship between the selection of a DSS design method and its ultimate success. DSS success was measured through DSS usage, user satisfaction, and user attitudes and perceptions criteria. The data utilized was collected from individuals responsible for designing and evaluating DSS, specifically the managers, intermediaries, and designers. In terms of the DSS usage and user satisfaction criteria, all three groups rated the evolutive design method superior, followed by the representation-based and adaptive methods. Using the DSS attitudes and perceptions criteria, the designer group rated the evolutive method best, followed by the representation-based, and adaptive methods. The ratings by the managers and intermediaries were rather mixed. Each design method did better in some success criteria than in others.     

An architecture of an intelligent system for routing user requests in a network of heterogeneous databases

We present a general purpose model for routing user requests, e.g. queries, in a network of autonomous heterogeneous databases. The database schemas and other information on the database nodes are used to construct a multi-level knowledge-base (MKB) that resides in various nodes. Access to the databases is not done by creating direct connections between the user and the nodes where the data are presumably located. Rather, the user approaches the network by contents via an intelligent system that utilizes the MKB in order to identify the nodes and databases where the most relevant information resides, and establishes access routes to those nodes.     

The design and use of laboratory experiments for DSS evaluation

DSS evaluation may be either ex-post or ex-ante. In the former, evaluation focuses on determining the actual result of DSS implementation. In the latter, emphasis is on predicting the likely impact of DSS alternatives on a given task set or on estimating relationships between DSS characteristics and task set(s) performance. If a firm can, ex-ante, effectively estimate or predict the performance of varying DSS on arrays of tasks or task sets, then it can avoid costly DSS selection errors and gain competitive and strategic advantages. We outline a methodoloy for developing such information using the induced-value methodology of experimental economics. An example experiment is detailed and initial results are presented relating to one general DSS hypothesis and one implication derived from a specific theory of DSS portfolio selection.     

基于GIS的SDSS模式研究和分析

本文首先分析了ＧＩＳ作为决策支持系统的分析方法,说明管理信息系统向决策支持系统的发展状况,从而推出地理信息系统作为一种空间信息管理信息系统,向空间决策支持系统的发展的必然性;在此基础上,给出基于ＧＩＳ的ＳＤＳＳ的若干模式,研究其分析方法,最后,描述解决这一问题的合理模式