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Esta investigación aporta con el planteamiento de una Guía de Implementación Referencial en la cual se ha definido una secuencia estructurada de siete actividades que permiten en conjunto unir el Cuadro de Mando Integral y el Data Warehouse. Se inicia la Guía con la primera actividad la cual es el análisis de la situación en el que se definen los factores internos y externos que afectan a la organización. En la segunda actividad se propone la definición del Cuadro de Mando Integral, en el que se definen los objetivos, estrategias e indicadores. En la tercera actividad se propone la definición del Data Warehouse, en el cual se define la estructura del modelado dimensional que se utilizará en esta Guía. En la cuarta actividad se realiza el análisis y selección de las fuentes de datos, las cuales sirven para alimentar de datos al Data Warehouse. En la quinta actividad se realiza el procesamiento de información, el cual procesa el modelo multidimensional en Cubos OLAP. En la sexta actividad se propone la visualización de la información, esta actividad permite visualizar los datos almacenados en el Cubo OLAP a través de tres interfaces: Interfase del objetivo, Interfase del mapa estratégico e Interfase del Cubo OLAP. Finalmente, en la séptima actividad se propone el uso de la información. En esta actividad el usuario podrá realizar un análisis detallado y totalizado de los indicadores propuestos a través de las tres interfaces definidas en la actividad anterior. Esta Guía permite a las organizaciones, que deseen aplicarla, medir y hacer el seguimiento de la estrategia a través de indicadores estratégicos soportados por información de mayor detalle, para que los encargados de tomar decisiones en las organizaciones puedan realizar, con un soporte metodológico, la toma de decisiones oportunas y acertadas.

Zugl.: Leipzig, Univ., Masterarbeit, 2006

Zugl.: Duisburg, Essen, Universiẗat, Diss., 2007.

Failure to identify major target market and lack of cost management have shown to be negative for enterprise operations. To remedy these two problems information technologies have promised to support effective and efficent decision-making processing by providing accurate and real-time information, so as to assist front-line sales, to lower down costs, to create more profits and to further diversified the risks. In steel industry, the repeated purchasing frequency (associated with customer loyalty) has been identified as one of the major determinants due to the narrow target market and capital-intensive features existed in steel business. Steel businesses often involve international transactions, which have much higher uncertainties than domestic transactions. Therefore, the major risk control task is on the collection and analysis customers¡¦ creditability. More customer information leads to fewer risks. As such, we believe that by applying the methodology of collecting and analyzing information through information technologies, the decision makers will be more confident in making correct decisions, which in turn help to generate profits. In this thesis, we develop a data warehouse system in support of order procurement. We begin by identifying the types of information that is crucial in making order procurement decision by consulting with the experienced international and domestic front-line sales managers. The different types of information are prioritized by applying Analytic Hierarchy Process (AHP) approach. We then construct a datawarehouse model that encompasses these information. A system prototype is finally developed to demonstrate the feasiblity of our approach.

Thesis (doctoral)--Technische Universität, Braunschweig, 1999.

This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems.

Zugl.: Marburg, University, Diss., 2006.

Zugl.: Hamburg, Univ., Diss., 2007

Universiẗat, Diss.--St. Gallen, 2006.