A probabilistic multidimensional data model and its applications in business management

This dissertation develops a conceptual data model that can efficiently handle huge volumes of data containing uncertainty and are subject to frequent changes. This model can be used to build Decision Support Systems to improve decision-making process. Business intelligence and decision-making in today\u27s business world require extensive use of huge volumes of data. Real world data contain uncertainty and change over time. Business leaders should have access to Decision Support Systems that can efficiently handle voluminous data, uncertainty, and modifications to uncertain data. Database product vendors provide several extensions and features to support these requirements; however, these extensions lack support of standard conceptual models. Standardization generally creates more competition and leads to lower prices and improved standards of living. Results from this study could become a data model standard in the area of applied decisions sciences. The conceptual data model developed in this dissertation uses a mathematical concept based on set theory, probability axioms, and the Bayesian framework. Conceptual data model, algebra to manipulate data, a framework and an algorithm to modify the data are presented. The data modification algorithm is analyzed for time and space efficiency. Formal mathematical proof is provided to support identified properties of model, algebra, and the modification framework. Decision-making ability of this model was investigated using sample data. Advantages of this model and improvements in inventory management through its application are described. Comparison and contrast between this model and Bayesian belief networks are presented. Finally, scope and topics for further research are described

Customer integration and operational performance: The mediating role of information quality

Much supply chain integration literature tends to be biased towards its positive impact on operational performance. However, inconclusive results demand investigation of the mechanisms through which supply chain integration can lead to superior operational performance. The purpose of this study is to identify empirically the mediating role of information quality on the relationship between customer integration and operational performance, and the direct relationship between customer integration and operational performance. The study is based on a questionnaire sent to 228 manufacturing companies in the Republic of Ireland, and the relationships between the constructs are analyzed through regression analysis. The results indicate that information quality partially mediates the relationship between customer integration and quality, delivery and flexibility. Further, information quality was found to fully mediate the relationship between customer integration and cost

Evaluating the Semantic and Representational Consistency of Interconnected Structured and Unstructured Data

In this paper we present research in progress that has the aim of developing a set of data quality metrics for two aspects of the dimension of consistency, the semantic and representational aspects. In the literature metrics for these two aspects are relatively unexplored, especially in comparison with the data integrity aspect. Our goal is to apply these data quality metrics to interconnected structured and unstructured data. Because of the prevalence of unstructured data in organizations today, many strive for “content convergence” by interconnecting structured and unstructured data. The literature offers few data quality metrics for this type of data, despite the growing recognition of its potential value. We are developing our metrics in the context of data mining, and evaluating their utility using data mining outcomes in an economic context. If our metric development is successful, a well-defined economic utility function for data quality metrics can be of direct use to managers making decisions

Representation of Aggregation Knowledge in OLAP Systems

Decision support systems are mainly based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels, using OLAP operators such as roll-up and drill-down. Roll-up operators decrease the details of the measure, aggregating it along the dimension hierarchy. Conversely, drill-down operators increase the details of the measure. As a consequence, dimensions hierarchies play a central role in knowledge representation. More precisely, since aggregation hierarchies are widely used to support data aggregation, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects and rules. Static aggregation knowledge is represented using UML class diagrams, while rules, which represent the dynamics (i.e. how aggregation may be performed depending on context), are represented using the Production Rule Representation (PRR) language. The latter allows us to incorporate dynamic aggregation knowledge. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a decision support system project. In order to illustrate the applicability and benefits of our approach, we exemplify the production rules and present an application scenario

Information Systems and strategic decisions: A literature Review

This paper looks at information systems and the information they provide specifically for strategic decision-making. The study employs a brief review of the recent research on information systems for strategic decision making and presents a framework for better understanding of such systems Future research plans are also given

A Model of Error Propagation in Satisficing Decisions and its Application to Database Quality Management

This study centers on the accuracy dimension of information quality and models the relationship between input accuracy and output accuracy in a popular class of applications. Such applications consist of dichotomous decisions or judgments that are implemented through conjunction of selected criteria. Initially, this paper introduces a model that designates a single decision rule which employs a single binary conjunction operation. This model is extended to handle multiple, related decision rules that consist of any number of binary conjunction operations. Finally, application of the extended model is illustrated through the example of an online hotel reservation database. This example demonstrates how the new model can be utilized for ranking and quantifying the damage that errors in different database attributes inflict. Numerical estimates of the model can be integrated into cost-benefit analyses that assess alternative data accuracy enhancements or process or system designs

Information quantity assessment : bases for managing the information resource

Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1991.Title as it appears in the M.I.T. Graduate List, Sept. 1991: Valuing information.Includes bibliographical references (leaves 85-89).by Marhsall W. Van Alstyne.M.S

An Empirical Study of the GIGO Axiom in Satisficing Decisions

An effort to improve data accuracy that yields poorer information accuracy when the data are processed would normally be labeled a major failure. While popular belief discounts the likelihood of such an event, research of conjunctive and disjunctive decision rules suggests that a negative association between input accuracy and decision accuracy is a deeply rooted phenomenon. In this paper we extend the understanding of this phenomenon through an empirical investigation of conjunctive decision rules using Monte Carlo simulations. The implications of this research are not limited to data accuracy; other data deficiencies can generate a comparable effect

Managing successful buyer-supplier relationships : Aligning the enabling roles of governance structure

Tämän väitöskirjan tarkoituksena on tutkia transaktiokustannusteoriaan ja sosiaalisen vaihdannan teoriaan perustuvia hallintamallien konsepteja menestyksekkäiden toimittajasuhteiden johtamisessa. Väitöskirjassa käytetään useita tutkimusmetodeja, kuten kirjallisuus-katsaus, laadulliset ja määrälliset tutkimusmetodit, joiden avulla päästään tutkimuksen tavoitteisiin viidellä tiedeartikkelilla. Laadullinen tieto kerättiin osittain strukturoiduissa haastatteluissa kansainvälisiltä case-yrityksiltä. Määrällinen tieto kerättiin kyselyiden avulla 170 pieneltä ja keskisuurelta yritykseltä. Tutkimuksen tulosten mukaan yhteistyökumppaneiden yhdenmukaisessa hallintamallissa, joka sisältää tavat organisoida kumppaneiden yhteistyötä, on tärkeää kehittää ostaja-toimittajasuhteiden suorituskykyä, koska se pienentää kustannusriskiä ja konflikteja. Toimittajien kehitysprosessi lisää luottamusta yritysten välillä, mikä kannustaa toimittajaa reagoimaan ostajan tarpeisiin. Yritykset haluavat osallistua investoimalla transaktiospesifisiin resursseihin, jolloin vaihtokustannus on suurempi. Yritysjohdon on analy¬soitava ja käytettävä sopivimpia suhteiden hallintamalleja, saman¬aikaisesti tai vaihtoehtoisesti, jotta saavutetaan pitkäaikaisia kustannus- ja suhde-etuja. Kattava tutkimusviitekehys selittää, kuinka yrityksen johto voi suunnitella tehokkaita ostaja-toimittajasuhteiden hallintamalleja, jotka perustuvat tutkimuksen tuloksiin ja löydöksiin.Based on transaction cost economics (TCE) and social exchange theory (SET), this dissertation aims to explore and investigate the determinants of governance structure and align their enabling roles in managing successful buyer-supplier relationships. By employing a mixed method approach as its research methodology, this dissertation attempts to achieve the research objectives by formulating five articles, approached through a systematic literature review, as well as qualitative and quantitative methods. Qualitative data were collected through semi-structured interviews from MNEs, whereas quantitative data were collected through survey questionnaires involving 170 SMEs. The results of the study suggest that an aligned governance structure, i.e., an approach to organizing and regulating the conduct of relationship partners, is crucial in order to improve firm performance because it can minimize the operational hazards of ex-post transaction costs as well as inter-firm conflicts. While supplier development processes enhance the level of confidence between firms that encourage suppliers’ response to buyers’ needs. Firms are eager to become directly involved in investing transaction-specific resources that have a lower alternative value. Further, based on relationship objectives, managers need to analyse and employ the most appropriate relationship governance mechanisms, whether simultaneously and/or alternatively, in order to achieve long-term cost as well as relationship performance. The comprehensive research framework explains how managers can craft effective buyer-supplier governance arrangements based on synthesized results and findings.fi=vertaisarvioitu|en=peerReviewed