Technology selection and group decision support systems: Case studies on supporting strategic technology selection processes

The objective of the dissertation is to increase understanding and knowledge in the field where group decision support system (GDSS) and technology selection research overlap in the strategic sense. The purpose is to develop pragmatic, unique and competent management practices and processes for strategic technology assessment and selection from the whole company's point of view. The combination of the GDSS and technology selection is approached from the points of view of the core competence concept, the lead user -method, and different technology types. In this research the aim is to find out how the GDSS contributes to the technology selection process, what aspects should be considered when selecting technologies to be developed or acquired, and what advantages and restrictions the GDSS has in the selection processes. These research objectives are discussed on the basis of experiences and findings in real life selection meetings. The research has been mainly carried outwith constructive, case study research methods. The study contributes novel ideas to the present knowledge and prior literature on the GDSS and technology selection arena. Academic and pragmatic research has been conducted in four areas: 1) the potential benefits of the group support system with the lead user -method,where the need assessment process is positioned as information gathering for the selection of wireless technology development projects; 2) integrated technology selection and core competencies management processes both in theory and in practice; 3) potential benefits of the group decision support system in the technology selection processes of different technology types; and 4) linkages between technology selection and R&D project selection in innovative product development networks. New type of knowledge and understanding has been created on the practical utilization of the GDSS in technology selection decisions. The study demonstrates that technology selection requires close cooperation between differentdepartments, functions, and strategic business units in order to gather the best knowledge for the decision making. The GDSS is proved to be an effective way to promote communication and co-operation between the selectors. The constructs developed in this study have been tested in many industry fields, for example in information and communication, forest, telecommunication, metal, software, and miscellaneous industries, as well as in non-profit organizations. The pragmatic results in these organizations are some of the most relevant proofs that confirm the scientific contribution of the study, according to the principles of the constructive research approach.

Evaluating land-use related environmental impacts of biomass value chains for decision-support : Comparison and testing of methodologies proposed for environmental life cycle assessment

Life cycle assessment (LCA) is one of the most established quantitative tools for environmental impact assessment of products. To be able to provide support to environmentally-aware decision makers on environmental impacts of biomass value-chains, the scope of LCA methodology needs to be augmented to cover landuse related environmental impacts. This dissertation focuses on analysing and discussing potential impact assessment methods, conceptual models and environmental indicators that have been proposed to be implemented into the LCA framework for impacts of land use. The applicability of proposed indicators and impact assessment frameworks is tested from practitioners' perspective, especially focusing on forest biomass value chains. The impacts of land use on biodiversity, resource depletion, climate change and other ecosystem services is analysed and discussed and the interplay in between value choices in LCA modelling and the decision-making situations to be supported is critically discussed. It was found out that land use impact indicators are necessary in LCA in highlighting differences in impacts from distinct land use classes. However, many open questions remain on certainty of highlighting actual impacts of land use, especially regarding impacts of managed forest land use on biodiversity and ecosystem services such as water regulation and purification. The climate impact of energy use of boreal stemwood was found to be higher in the short term and lower in the long-term in comparison with fossil fuels that emit identical amount of CO2 in combustion, due to changes implied to forest C stocks. The climate impacts of energy use of boreal stemwood were found to be higher than the previous estimates suggest on forest residues and stumps. The product lifetime was found to have much higher influence on the climate impacts of woodbased value chains than the origin of stemwood either from thinnings or final fellings. Climate neutrality seems to be likely only in the case when almost all the carbon of harvested wood is stored in long-lived wooden products. In the current form, the land use impacts cannot be modelled with a high degree of certainty nor communicated with adequate level of clarity to decision makers. The academia needs to keep on improving the modelling framework, and more importantly, clearly communicate to decision-makers the limited certainty on whether land-use intensive activities can help in meeting the strict mitigation targets we are globally facing.

Collaborative business networks evaluation of technological tools for decision support making

Nowadays, companies are living great difficulties on managing their business due to constant and unpredictable economic market fluctuations. Recent changes in market trends (such as the constant demand for new products and services, mass customization and the drastic reduction of delivery time) lead companies to adopt strategies of creating partnerships with other companies as a way to respond effectively to such difficult economical times. Collaborative Networks’ concept born by the consequence of companies could no longer consider their internal business processes’ management as sufficient and tend to seek for a collaborative approach with other partners for their critical processes. Information technologies (ICT) assumed a major role acting as “enablers” of these kinds of networks, enhancing information sharing and business process integration. Several new trends concerning ICT architectures have been created to support collaborative networks requirements, but still doesn’t exist a common platform to reduce the needed integration effort on virtual organizations. This study aims to investigate the current technological solutions available in the market which enhances the management of companies’ business processes (specially, Collaborative Planning). Finally, the research work ends with the presentation of a conceptual model to answer to the constraints evaluated.

Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

Adequacy of venous thromboprophylaxis in acutely ill medical patients (IMPART): multisite comparison of different clinical decision support systems

BACKGROUND: The adequacy of thromboprophylaxis prescriptions in acutely ill hospitalized medical patients needs improvement. OBJECTIVE: To prospectively assess the efficacy of thromboprophylaxis adequacy of various clinical decision support systems (CDSS) with the aim of increasing the use of explicit criteria for thromboprophylaxis prescription in nine Swiss medical services. METHODS: We randomly assigned medical services to a pocket digital assistant program (PDA), pocket cards (PC) and no CDSS (controls). In centers using an electronic chart, an e-alert system (eAlerts) was developed. After 4 months, we compared post-CDSS with baseline thromboprophylaxis adequacy for the various CDSS and control groups. RESULTS: Overall, 1085 patients were included (395 controls, 196 PC, 168 PDA, 326 eAlerts), 651 pre- and 434 post-CDSS implementation: 472 (43.5%) presented a risk of VTE justifying thromboprophylaxis (31.8% pre, 61.1% post) and 556 (51.2%) received thromboprophylaxis (54.2% pre, 46.8% post). The overall adequacy (% patients with adequate prescription) of pre- and post-CDSS implementation was 56.2 and 50.7 for controls (P = 0.29), 67.3 and 45.3 for PC (P = 0.002), 66.0 and 64.9 for PDA (P = 0.99), 50.5 and 56.2 for eAlerts (P = 0.37), respectively, eAlerts limited overprescription (56% pre, 31% post, P = 0.01). CONCLUSION: While pocket cards and handhelds did not improve thromboprophylaxis adequacy, eAlerts had a modest effect, particularly on the reduction of overprescription. This effect only partially contributes to the improvement of patient safety and more work is needed towards institution-tailored tools.

Knowledge Management and Decision Support for Sustainable Land Management

Much research has focused on desertification and land degradation assessments without putting sufficient emphasis on prevention and mitigation, although the concept of sustainable land management (SLM) is increasingly being acknowledged. A variety of SLM measures have already been applied at the local level, but they are rarely adequately recognised, evaluated, shared or used for decision support. WOCAT (World Overview of Technologies and Approaches) has developed an internationally recognised, standardised methodology to document and evaluate SLM technologies and approaches, including spatial distribution, allowing the sharing of SLM knowledge worldwide. The recent methodological integration into a participatory process allows now analysing and using this knowledge for decision support at the local and national level. The use of the WOCAT tools stimulates evaluation (self-evaluation as well as learning from comparing experiences) within SLM initiatives where all too often there is not only insufficient monitoring but also a lack of critical analysis. The comprehensive questionnaires and database system facilitate to document, evaluate and disseminate local experiences of SLM technologies and their implementation approaches. This evaluation process - in a team of experts and together with land users - greatly enhances understanding of the reasons behind successful (or failed) local practices. It has now been integrated into a new methodology for appraising and selecting SLM options. The methodology combines a local collective learning and decision approach with the use of the evaluated global best practices from WOCAT in a concise three step process: i) identifying land degradation and locally applied solutions in a stakeholder learning workshop; ii) assessing local solutions with the standardised WOCAT tool; iii) jointly selecting promising strategies for implementation with the help of a decision support tool. The methodology has been implemented in various countries and study sites around the world mainly within the FAO LADA (Land Degradation Assessment Project) and the EU-funded DESIRE project. Investments in SLM must be carefully assessed and planned on the basis of properly documented experiences and evaluated impacts and benefits: concerted efforts are needed and sufficient resources must be mobilised to tap the wealth of knowledge and learn from SLM successes.

Monitoring Programmes, Multiple Stress Analysis and Decision Support for River Basin Management

The identification of plausible causes for water body status deterioration will be much easier if it can build on available, reliable, extensive and comprehensive biogeochemical monitoring data (preferably aggregated in a database). A plausible identification of such causes is a prerequisite for well-informed decisions on which mitigation or remediation measures to take. In this chapter, first a rationale for an extended monitoring programme is provided; it is then compared to the one required by the Water Framework Directive (WFD). This proposal includes a list of relevant parameters that are needed for an integrated, a priori status assessment. Secondly, a few sophisticated statistical tools are described that subsequently allow for the estiation of the magnitude of impairment as well as the likely relative importance of different stressors in a multiple stressed environment. The advantages and restrictions of these rather complicated analytical methods are discussed. Finally, the use of Decision Support Systems (DSS) is advocated with regard to the specific WFD implementation requirements.

Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

Building a decision support system with a knowledge modeling tool

Knowledge modeling tools are software tools that follow a modeling approach to help developers in building a knowledge-based system. The purpose of this article is to show the advantages of using this type of tools in the development of complex knowledge-based decision support systems. In order to do so, the article describes the development of a system called SAIDA in the domain of hydrology with the help of the KSM modeling tool. SAIDA operates on real-time receiving data recorded by sensors (rainfall, water levels, flows, etc.). It follows a multi-agent architecture to interpret the data, predict the future behavior and recommend control actions. The system includes an advanced knowledge based architecture with multiple symbolic representation. KSM was especially useful to design and implement the complex knowledge based architecture in an efficient way.

Decision support system for project selection

The evaluation and selection of industrial projects before investment decision is customarily done using marketing, technical, and financial information. Subsequently, environmental impact assessment and social impact assessment are carried out mainly to satisfy the statutory agencies. Because of stricter environment regulations in developed and developing countries, quite often impact assessment suggests alternate sites, technologies, designs, and implementation methods as mitigating measures. This causes considerable delay to complete project feasibility analysis and selection as complete analysis requires to be taken up again and again until the statutory regulatory authority approves the project. Moreover, project analysis through the above process often results in suboptimal projects as financial analysis may eliminate better options as more environment friendly alternative will always be cost intensive. In this circumstance, this study proposes a decision support system which analyses projects with respect to market, technicalities, and social and environmental impact in an integrated framework using analytic hierarchy process, a multiple attribute decision-making technique. This not only reduces duration of project evaluation and selection, but also helps select an optimal project for the organization for sustainable development. The entire methodology has been applied to a cross-country oil pipeline project in India and its effectiveness has been demonstrated. © 2008, IGI Global.

A methodology for implementing a decision support system:a small company case study

The importance of non-technical factors in the design and implementation of information systems has been increasingly recognised by both researchers and practitioners, and recent literature highlights the need for new tools and techniques with an organisational, rather than technical, focus. The gap between what is technically possible and what is generally practised, is particularly wide in the sales and marketing field. This research describes the design and implementation of a decision support system (DSS) for marketing planning and control in a small, but complex company and examines the nature of the difficulties encountered. An intermediary with functional, rather than technical, expertise is used as a strategy for overcoming these by taking control of the whole of the systems design and implementation cycle. Given the practical nature of the research, an action research approach is adopted with the researcher undertaking this role. This approach provides a detailed case study of what actually happens during the DSS development cycle, allowing the influence of organisational factors to be captured. The findings of the research show how the main focus of the intermediary's role needs to be adapted over the systems development cycle; from coordination and liaison in the pre-design and design stages, to systems champion during the first part of the implementation stage, and finally to catalyst to ensure that the DSS is integrated into the decision-making process. Two practical marketing exercises are undertaken which illustrate the nature of the gap between the provision of information and its use. The lack of a formal approach to planning and control is shown to have a significant effect on the way the DSS is used and the role of the intermediary is extended successfully to accommodate this factor. This leads to the conclusion that for the DSS to play a fully effective role, small firms may need to introduce more structure into their marketing planning, and that the role of the intermediary, or Information Coordinator, should include the responsibility for introducing new techniques and ideas to aid with this.

Psychological cue use and implications for a clinical decision support system

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training. © 2002 Informa UK Ltd All rights reserved.

A Real-time Decision Support System Prototype for Management of a Power Block

This paper describes the basic tools for a real-time decision support system of a semiotic type on the example of the prototype for management and monitoring of a nuclear power block implemented on the basis of the tool complex G2+GDA using cognitive graphics and parallel processing. This work was supported by RFBR (project 02-07-90042).

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.