80,927 research outputs found

    Approaches to selecting information systems projects under uncertainty

    Get PDF
    The rapid advance in information and communication technologies has effectively facilitated the development and implementation of information systems (IS) projects in modern organizations for reorganizing their business processes and streamlining the provision of their products and services in today's dynamic environment. Such a development brings organizations with numerous benefits including increased automation of business processes, improved customer service, and timely provision of effective decision support. As a result, evaluating and selecting the most appropriate IS project for development and implementation from a pool of available IS projects becomes a critical decision to make in modern organizations. Evaluating and selecting appropriate IS projects for development in an organization, however, is complex and challenging. The complexity of the evaluation and selection process is due to the multi-dimensional nature of the decision making process, the conflicting nature of the multiple selection criteria, and the presence of subjectiveness and imprecision of the human decision making process. The challenging of the evaluation and selection comes from the need for making transparent and balanced decisions based on a comprehensive evaluation of all available IS projects in a timely manner. Much research has been done on the development of various approaches for evaluating and selecting IS projects, and numerous applications of those approaches for addressing real world IS project evaluation and selection problems have been reported in the literature. In general, existing approaches can be classified into (a) cost-benefit analysis based approaches, (b) utility based approaches, and (c) optimization oriented approaches. These approaches, however, are not totally satisfactory due to various shortcomings including (a) the inability to tackle the subjectiveness and imprecision of the selection process, (b) the failure to adequately handle the multi-dimensional nature of the problem, and (c) cognitively very demanding on the decision maker. To address these issues above, this research has developed three novel approaches for effectively solving the IS project evaluation and selection problem under uncertainty in an organization. The first approach is developed for helping the decision maker better model the subjectiveness and imprecision inherent in the decision-making process with the use of linguistic variables approximated by fuzzy numbers. The second approach is designed to reduce the cognitive demanding on the decision maker in the IS project evaluation and selection process with the introduction of fuzzy pairwise comparison. The third approach is formulated with respect to the use of intelligent decision support systems for facilitating the use of specific multi-criteria analysis approaches in relation to individual IS project evaluation and selection situations. The developed approaches have been applied for solving three IS project evaluation and selection problems in the real world settings. The results show that the three developed ap proaches are of practical significance for effectively and efficiently solving the IS project evaluation and selection problem due to (a) the simplicity and comprehensibility of the underlying concept, (b) the adequate handling of inherent uncertainty and imprecision, and (c) the ability to help the decision maker better understand the IS project selection problem and the implications of their decision behaviours

    Multi crteria decision making and its applications : a literature review

    Get PDF
    This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

    Strategic and Operational Management of Supplier Involvement in New Product Development: a Contingency Perspective

    Get PDF
    This paper examines how firms succeed to leverage supplier involvement in product development. The paper extends earlier work on managing supplier involvement by providing an integrated analysis of results, processes and conditions both at the level of individual development projects and the overall firm. Following a multiple-case study approach with theoretical sampling, the study is carried out by examining eight projects in which four manufacturers from different industries involve multiple suppliers. The findings suggest that successful supplier involvement is dependent on the coordinated design, execution and evaluation of strategic, long-term processes and operational, short-term management processes and the presence of enabling factors such as a cross-functional oriented organization. The required intensity of these processes and enablers depends on contingencies such as firm size and environmental uncertainty. In contrast with previous research, we find no indications that managing supplier involvement requires a different approach in highly innovative projects compared to less innovative projects.innovation;new product development;purchasing;supplier relations;R&D management

    Crossing the death valley to transfer environmental decision support systems to the water market

    Get PDF
    Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development.The authors would like to thank the Catalan Water Agency (Agència Catalana de l’Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291.Peer ReviewedPostprint (published version

    Fuzzy Logic and Intelligent Agents: Towards the Next Step of Capital Budgeting Decision Support

    Get PDF
    The economic life of large investments is long and thus necessitates constant dynamic managerial actions. To be able to act in an optimal way in the dynamic management of large investments managers need the support of advanced analytical tools. They need to have constant access to information about the real time situation of the investment, as well as, access to up-to-date information about changes in the business environment. What is more challenging, they need to integrate qualitative information into quantitative analysis process, and to integrate foresight information into the capital budgeting process. In this paper we will look at how emerging soft computing technologies, specifically fuzzy logic and intelligent agents, will help to provide a better support in such a context and then to frame a support system that will make an integrated application of the aforementioned technologies. We will first develop a holistic framework for an agent-facilitated capital budgeting system using a fuzzy real option approach. We will then discuss how intelligent agents can be applied to collect decision information, both qualitative and quantitative, and to facilitate the integration of foresight information into capital budgeting process. Integration of qualitative information into quantitative analysis process will be discussed. Methods for integrating qualitative and quantitative information into fuzzy numbers, as well as, methods for using the fuzzy numbers in capital budgeting will be presented. A specification of how the agents can be constructed is elaborated.Intelligent Agents, Fuzzy Sets, Capital Budgeting, Real Options, DSS

    Integration of decision support systems to improve decision support performance

    Get PDF
    Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
    corecore