For manufacturing firms, having knowledge about customers is very important, in particular for the developers and designers of new products. A way in which software can help to build an information channel between the customers and the firm is through Customer Relationship Management (CRM) and Business Intelligence (BI) solutions. Customers’ data are captured into the Customer Relationship Management solution while Business Intelligence analyses them and provide clear processed information to the developers and designers of new products. In this study we have researched if this process occurs in the industry, if and how it can be improved and what advantages it could bring to manufacturing firms. We have carried out the data collection by interviewing experts in four companies, three software companies that provide Business Intelligence solutions and one manufacturing firm. We found out that those software solutions are not used to directly connect developers and designers to customers’ data, and that there are no specific technical obstacles that prevents this, if not managerial reasons rooted in everyday practice. We also uncovered facts that would help to make this process more efficient and make customers’ data even more relevant to development.

Purpose:  The purpose of this study is to analyze the perceived impact of business intelligence tools on marketing success. Methodology:  This research has conducted based on a literature study in the field business intelligence and pharmaceutical industry marketing. Primary  data  has  been  used  in  this  thesis  to  evaluate  the information technology impact on the  market performance for pharmaceutical industries. The SPSS software package is used to analyze the employee responses and statistical technique. Regression analysis is used to check the impact of information technology on market performance. Findings: One hundred fifty questionnaires were given to the employees of 15 different pharmaceutical companies located in Pakistan. Among them, 104 were collected back and 100 questionnaires were considered valid and used for further analysis. After analyzing the data, it was evaluated that information technology has a significant impact on market performance in pharmaceutical industries. According to the stakeholders, strategic planning, organizational support, knowledge management and technological advancement have significant impact on the success of information technology for decision support systems. Research limitations/Implications: More research will be required on the theory presented in this thesis “The Perceived Impact of Business Intelligence Tools on Marketing Success”.

Business Intelligence (BI) is not a new concept. However, it is still complicated to work with it due to the different views and ways of implementation. This study has adopted an organisational oriented approach of BI: getting the right information, to the right people, at the right time. It was found in this study that poorly implemented BI features and inadequate information infrastructure may lead to workarounds in order to compensate for missing or lacking features and systems. This case study has been conducted at the faculty of Social Sciences of Uppsala University (UU) where a set of interviews were conducted to individuals on one of the key positions at the university; the Director of Studies (DoS). The aim was to map out the key processes and head activities of the DoS’s and evaluate them through the BI view of this thesis. Being the conclusion a concrete mapping of the DoS head activities as well as BI assessment of the current situation of the studied departments. Some improvements have been proposed in order to remedy the lacking of valuable and critical features and systems. However, it was found that the current system infrastructure and problem area is too immature in order to implement any form of integration. Several studies on key positions and influential systems need to be conducted in order to exhaustively map out and discover the problem area.

The thesis is concerned with logistic processes optimization in automotive industry companies that assemble car components. In the theoretical part of the thesis, areas and a structure of industrial logistics are defined and frequently used terms are explained. Then, theoretical approaches and practical applications used in the practical part are described. In the practical part, two particular companies are presented. The aim of the thesis is to optimize a tool that is used in the first organization during the process of logistic preparation of manufacturing. Secondly, the aim of the thesis is to analyse the process of material picking and handling before the supply of assembly line that is performed in the second organization and to suggest a solution that would eliminate non-value added activities. In the end, benefits of the thesis for both the organizations and the author are summarized.

The thesis focuses on implementation of Business Intelligence in a small retail company. The aim of the thesis is to design a pilot Business Intelligence solution to support management activities in Lintea, s. r. o. The company is part of Slovak lingerie and underwear retail market since 2004. Business performance measurement is not effectively supported in the company. Rise in the quality level of management activities is the main benefit of the proposed implementation which is achieved by delivering previously inaccessible or laboriously accessible information in an appropriate form. Proposed solution substantially simplifies, accelerates and improves management and decision making activities in the company. Thesis has two parts: theoretical and practical. Theoretical part has two chapters devoted to performance management and BI. Chapter about performance management presents wider aspects of using BI in companies and introduces the Balanced Scorecard method. Chapter about Business Intelligence defines BI, explains its principles, components and basic analytic method of BI - the dimensional modelling which is then applied in the practical part. The theoretical part ends with the description of current situation in the BI market. The practical part starts with the introduction of the company Lintea, s. r. o. followed by the description of its current state and proposal of the business performance measures based on the Balanced Scorecard method. Second chapter of the practical part contains the proposed Business Intelligence solution itself. Individual steps of the Business Intelligence design process are: analysis of prerequisites and requirements, analysis of data sources, dimensional modelling, ETL design, multidimensional data structures design and finally presentation layer design.

In this thesis the candidate explores the apparent gaps between strategy development and strategy implementation (the strategy alignment question), and between business end-user needs and the suppliers of information technology (IT) related products and services. With business intelligence (BI) emerging as one of the fastest growing fields in IT, the candidate develops a conceptual model in which BI is placed into context with other relevant subjects such as strategy development, enterprise architecture and modelling and performance measurement. The emphasis is on the development of processes and templates that support a closed loop control system with the following process steps: - A business strategy is defined. - The implication of the strategy on business processes, supporting IT resources and organizational structure is formally documented according to enterprise architecture principles. - This documented blueprint of the organization helps to implement the selected business strategy. - A performance measurement system is developed and supported by a well-designed data warehouse. - On a regular basis the measurements that were defined to support the implementation of the strategy, together with information from the external environment are interpreted and this analysis leads to either a new strategy, or refinement of the implementation of the existing strategy. Both options may lead to changes in the enterprise architecture, the execution of business processes and/or the performance measurement system. Some of the individual components of the model are supported by existing theories, for example the Zachman Framework for enterprise architecture and the Balanced Scorecard from Kaplan and Norton. The contribution of the author was to position them in the bigger picture to indicate how they can add value with regard to the establishment of business intelligence in organizations. Instead of packaging existing ideas slightly differently under a new name, the author intentionally searched for existing theories to fulfil certain requirements in the Bigger Picture BI Context Model. Apart from a set of templates that were adapted from various other sources and packaged into practical formats that can be used during facilitation sessions, the author has also developed and described the Fourier Model and the Pots of Money Model. The Fourier Model is a powerful conceptual model that helps a business to package solutions for market related requirements through selections of previously defined building blocks (technical components) that can be delivered through various business entities, depending on the requirements of the opportunity. The Pots of Money Model is a quantitative model embedded in a spreadsheet format to illustrate and communicate the effect of spending decisions in one area of the business on other areas. The candidate demonstrates the Bigger Picture BI Context Model in several case studies. The thesis is accompanied by a CD ROM, which contains over 700 references to relevant literature (most of them available in full text) and links to internet web sites, as well as examples of the software templates that support some of the steps in the context model. The following figure depicts the conceptual model in schematic format: (See figure in the abstract of 00front) / Thesis (PhD (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted

Ontologies are a key factor of Information management as they provide a common representation to any domain. Historically, finance domain has suffered from a lack of efficiency in managing vast amounts of financial data, a lack of communication and knowledge sharing between analysts. Particularly, with the growth of fraud in financial markets, cases are challenging, complex, and involve a huge volume of information. Gathering facts and evidence is often complex. Thus, the impetus for building a financial fraud ontology arises from the continuous improvement and development of financial market surveillance systems with high analytical capabilities to capture frauds which is essential to guarantee and preserve an efficient market.This thesis proposes an ontology-based approach for financial market surveillance systems. The proposed ontology acts as a semantic representation of mining concepts from unstructured resources and other internet sources (corpus). The ontology contains a comprehensive concept system that can act as a semantically rich knowledge base for a market monitoring system. This could help fraud analysts to understand financial fraud practices, assist open investigation by managing relevant facts gathered for case investigations, providing early detection techniques of fraudulent activities, developing prevention practices, and sharing manipulation patterns from prosecuted cases with investigators and relevant users. The usefulness of the ontology will be evaluated through three case studies, which not only help to explain how manipulation in markets works, but will also demonstrate how the ontology can be used as a framework for the extraction process and capturing information related to financial fraud, to improve the performance of surveillance systems in fraud monitoring. Given that most manipulation cases occur in the unregulated markets, this thesis uses a sample of fraud cases from the unregulated markets. On the empirical side, the thesis presents examples of novel applications of text-mining tools and data-processing components, developing off-line surveillance systems that are fully working prototypes which could train the ontology in the most recent manipulation techniques.

The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional data processing. Feature selection or attribute selection is one of the techniques used for dimensionality reduction. Previous research has shown that data mining results can be improved in terms of accuracy and efficacy by selecting the attributes with most significance. This study analyzes vehicle service and sales data from multiple car dealerships. The purpose of this study is to find a model that better classifies existing customers as new car buyers based on their vehicle service histories. Six different feature selection methods such as; Information Gain, Correlation Based Feature Selection, Relief-F, Wrapper, and Hybrid methods, were used to reduce the number of attributes in the data sets are compared. The data sets with the attributes selected were run through three popular classification algorithms, Decision Trees, k-Nearest Neighbor, and Support Vector Machines, and the results compared and analyzed. This study concludes with a comparative analysis of feature selection methods and their effects on different classification algorithms within the domain. As a base of comparison, the same procedures were run on a standard data set from the financial institution domain.

Since the1970s many approaches of representing domains have been suggested. Each approach maintains the assumption that the information about the objects represented in the Information System (IS) is specified and verified by domain experts and potential users. Yet, as more IS are developed to support a larger diversity of users such as customers, suppliers, and members of the general public (such as many multi-user online systems), analysts can no longer rely on a stable single group of people for complete specification of domains –to the extent that prior research has questioned the efficacy of conceptual modeling in these heterogeneous settings. We formulated principles for identifying basic classes in a domain. These classes can guide conceptual modeling, database design, and user interface development in a wide variety of traditional and emergent domains. Moreover, we used a case study of a large foster organization to study how unstructured data entry practices result in differences in how information is collected across organizational units. We used institutional theory to show how institutional elements enacted by individuals can generate new practices that can be adopted over time as best practices. We analyzed free-text notes to prioritize potential cases of psychotropic drug use—our tactical need. We showed that too much flexibility in how data can be entered into the system, results in different styles, which tend to be homogenous across organizational units but not across organizational units. Theories in Psychology help explain the implications of the level of specificity and the inferential utility of the text encoded in the unstructured note.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.