Decision support for information systems management : applying analytic hierarchy process

Decision-making in the field of information systems has become more complex due to a larger number of alternatives, multiple and sometimes conflicting goals, and an increasingly turbulent environment. In this paper we explore the appropriateness of Analytic Hierarchy Process to support I/S decision making. AHP can be applied if the decision problem includes multiple objectives, conflicting criteria, incommensurable units, and aims at selecting an alternative from a known set of alternatives. An AHP analysis is described by using the project selection decision as an example. The strengths and weaknesses of AHP are investigated based on a set of eight criteria for evaluating I/S decision support methods. This evaluation shows that AHP scores well on most criteria. Given this promising performance, other possible applications of AHP within the I/S function are listed.

COOPERATION SUPPORT IN A DYADIC SUPPLY CHAIN

To improve the supply chains performance, taking into account the customer demand in the tactical planning process is essential. It is more and more difficult for the customers to insure a certain level of demand over a medium term period. Then it is necessary to develop methods and decision support systems to reconcile the order and book processes. In this context, this paper aims at introducing a collaboration support tool and methodology dedicated to a dyadic supply chain. This approach aims at evaluating in term of risks different demand management strategies within the supply chain using a simulation dedicated tool. The evaluation process is based on an exploitation of decision theory and game theory concepts and methods.supply chain ; simulation ; collaboration ; decision theory ; risk

A case study in multiple criteria decision support systems

Evaluation of strategic alternatives is an important task for strategic managers. This is a difficult task due to inherent complexities of the evaluation process and lack of structured information. The evaluation process must consider external opportunities and threats, and internal strengths and weaknesses. This paper presents a case study in multiple criteria decision support systems. The decision support system presented in this paper utilizes the model presented in the appendix along with several computer systems including EXPERT CHOICE and Spreadsheets to enhance and aid the decision maker\u27s- intuition in evaluating potential alternatives

Evaluating Detection and Diagnostic Decision Support Systems for Bioterrorism Response

We evaluated the usefulness of detection systems and diagnostic decision support systems for bioterrorism response. We performed a systematic review by searching relevant databases (e.g., MEDLINE) and Web sites for reports of detection systems and diagnostic decision support systems that could be used during bioterrorism responses. We reviewed over 24,000 citations and identified 55 detection systems and 23 diagnostic decision support systems. Only 35 systems have been evaluated: 4 reported both sensitivity and specificity, 13 were compared to a reference standard, and 31 were evaluated for their timeliness. Most evaluations of detection systems and some evaluations of diagnostic systems for bioterrorism responses are critically deficient. Because false-positive and false-negative rates are unknown for most systems, decision making on the basis of these systems is seriously compromised. We describe a framework for the design of future evaluations of such systems

Using a knowledge-based approach: the way healthy communities make decisions

The planning for Knowledge Cities faces significant challenges due to the lack of effective information tools. These challenges are magnified while planning healthy communities. The Australian Health Information Council (AHIC) concluded in its last report that health information needs to be shared more effectively (AHIC, 2008). Some research justifies the use of Decision Support Systems (DSS) as an E-planning tool, particularly in the context of healthy communities. However, very limited research has been conducted in this area to date, especially in terms of evaluating the impact of these tools on decision-makers within the health planning practice. The paper presents the methodological instruments which were developed to measure the impact of the E-planning tool (i.e., Health Decision Support System [HDSS])) on a group of health planners, namely, the Logan Beaudesert Health Coalition (LBHC). The paper is focused on the culture in which decisions were made before and after the intervention of the HDSS. Subsequently, the paper presents the observed impact of the HDSS tool, to facilitate a knowledge-based decision-making approach. This study is an attempt to make some contribution to the Knowledge Cities literature in the context of planning healthy communities by adopting E-planning tools

Decision Support for Distributed Database Fragmentation and Allocation Schema Design

If peer to peer distributed database systems meet current expectations, they are likely to replace virtually all centralized database systems over the next decade. One impediment to the proliferation of peer to peer distributed database systems is the lack of proven and established normative methodologies for designing distributed database fragmentation and allocation schemas. The literature discussed here serves as the basis of research-in-progress for designing, implementing, and empirically evaluating a support system to aid distributed database fragmentation and allocation schema decision making. Future manuscripts are planned that describe the prototype decision support system and our empirically-based experiences

Assessing Demand for Transparency in Intelligent Systems Using Machine Learning

Intelligent systems offering decision support can lessen cognitive load and improve the efficiency of decision making in a variety of contexts. These systems assist users by evaluating multiple courses of action and recommending the right action at the right time. Modern intelligent systems using machine learning introduce new capabilities in decision support, but they can come at a cost. Machine learning models provide little explanation of their outputs or reasoning process, making it difficult to determine when it is appropriate to trust, or if not, what went wrong. In order to improve trust and ensure appropriate reliance on these systems, users must be afforded increased transparency, enabling an understanding of the systems reasoning, and an explanation of its predictions or classifications. Here we discuss the salient factors in designing transparent intelligent systems using machine learning, and present the results of a user-centered design study. We propose design guidelines derived from our study, and discuss next steps for designing for intelligent system transparency

An Empirical Application of Delone and Mclean Model In Evaluating Decision Support System In The Banking Sector of Oman

Banks are investing a huge amount on information systems to provide fast services to their customers and to stay competitive. Therefore it becomes necessary to measure the success of these systems. The main objective of this study is to assess the applicability of DeLone and McLean model of information systems in evaluating decision support system in the banking sector of Oman and to check the relationship among variables of the model. In order to achieve the objectives, data was collected from the managers and assistant managers using decision support system in the banks of Oman. Hypotheses were tested using correlation analysis. The results found that most of the relationships between the constructs were supported. System quality, information quality and service quality had a direct positive association with user satisfaction. User satisfaction influenced individual impact positively. The findings supported DeLone and McLean model and suggested its applicability in the evaluation of decision support system in the banking sector

Development of a Decision Support System for Post Mining Land Use on Abandoned Surface Coal Mines in Appalachia

Decision support systems are diverse and have been used to solve multiple problems ranging from the complex to the simple. With the complexity of environmental decisions today, these systems provide a logic based approach to evaluating and choosing environmental solutions. Abandoned mining lands (AML) are an issue for the environment in the Appalachian region. Given this a decision support system was designed using previously created frameworks and indices from other systems created. The system is comprised of two main sections, selecting the ideal post-mining land-use (PMLU), and maximizing the potential of land to be reclaimed under budgetary constraints. This system incorporates stakeholders, and takes into account the regulations governing reclamation of AML in Appalachia. The system could potentially be adjusted and used in other land use decision situations